Abstract

Weighted residuals and the reduced χ2R 2) value are investigated with regard to their relevance for assessing optical property estimates using the diffusion equation for time-resolved measurements in turbid media. It is shown and explained, for all photon counting experiments including lifetime estimation, why χR 2 increases linearly with the number of photons when there is a model bias. Only when a sufficient number of photons has been acquired, χR 2 is a pertinent value for assessing the accuracy of μa and μs' estimates. It was concluded that χR 2 is of particular interest for cases of small interfiber separation, low-level scattering, strong absorption and incorrect measurement of instrument response function. It was also found that χR 2 is less pertinent for judging μa in case of air boundary effects.

© 2009 OSA

1. Introduction

Optical biomedical diagnosis is a growing field mainly due to the non-invasiveness of devices and non-ionizing radiation. In the near infrared range, where tissues are less absorbing, there are many potential applications such as small animal imaging [1], oximetry [2, 3], photodynamic therapy [4], diffuse optical tomography [5, 6], or fluorescence optical tomography (fDOT) [7, 8]. In tissues, propagating photons interact mostly by means of elastic scattering and/or absorption. In general, biological media can thus be characterized by their reduced scattering coefficient μs and absorption coefficient μa. Knowledge of these optical properties is necessary for the above applications. As examples, it has been shown that errors on background optical properties can result in misestimation of other optical properties or the position of an absorbing inclusion [9], but knowledge of scattering is also of particular importance for reconstructing the depth and concentration of fluorescent inclusions during fDOT [10].

Time-resolved spectroscopy (TRS) is one of the well established techniques for μa and μs estimation [11]. It has been introduced for biomedical investigations since the late 1980s [1215], and since then, it has been widely used. For instance, TRS has been applied in a variety of in-vivo optical property estimates, such as for muscle [16], human brain [12], human breast [17, 18] or more recently for prostate [19]. Schematically, the method consists in illuminating the medium to be probed with a subnanosecond pulse, then collecting the temporal profile, after dispersion in the medium. An inversion algorithm is used to estimate the optical parameters, μa and μs, by fitting the data with a photon propagation model. The algorithm seeks to minimize a χ2 error function between the temporal profile and the model. An important issue, which has been discussed for many years, is then the choice of an appropriate model, and fast enough to compute for estimation algorithm [20]. For example, the diffusion equation can be computed in a very short time, a fact that is of great advantage for inversion algorithm, but has the drawback of a limited range of applications [21], in particular μs has to be large compared to μa, the source-to-detector distance has to be large enough and early arriving photons fail to fit the model [22]. Moreover, when using an infinite medium, there has to be sufficient distance between air and medium or to other boundaries [23]. In all these situations, where the diffusion model is incorrect, μa and μs estimates are inaccurate. Many works have thus been performed to assess accuracy of μa and μs estimates when using diffusion approximation and TRS measurements [21]. For example, it has been shown that for strong absorption tissues and weak scattering (μa>0.5cm−1 and μs<5 cm−1), errors in both parameters can exceed 30% [2426].

To overcome these limitations, Monte Carlo (MC) simulation of radiative transport theory has appeared as a gold standard, but faced time consuming computation [2729]. More recently, thanks to the scalability of MC simulation, Alerstam et al. [24, 25] computed a fast version of MC called White Monte Carlo to overcome both incorrect model and computation time issues. This new method may appear to be a reference for TRS community, especially for strong absorption tissue characterization; the method has already been applied to accurately evaluate optical properties of human prostate [30].

However, although the great efforts made in recent years, estimating the accuracy of optical properties in TRS remains an issue for experimenters. In order to judge the goodness of fit, and afterwards the accuracy of estimates, it is proposed to consider the χ2 criterion together with the weighted residuals between model and data. This fitting method has proven efficiency for different time-resolved photon counting applications, notably for TCSPC lifetime measurements for choosing the right number of parameters in multi-exponential decay fluorescence. Fitting values are correct inasmuch that data follow a certain set of assumptions. According to [31], these assumptions are:

  • (i) data uncertainty is the dependent variable (y-axis), meaning in this case that time uncertainty (x-axis) is negligible,
  • (ii) this uncertainty has a Gaussian distribution, centered around the model value,
  • (iii) there is no systematic error whether in time or in intensity,
  • (iv) data points correspond to independent observations,
  • (v) the number of data points T is greater than the number of estimated parameters p,
  • (vi) the model is correct (incorrect models yield incorrect fitted parameters).

This article examines the case of using an incorrect model (depending on experimental conditions and investigated optical ranges), in this instance the infinite diffusion model, for estimating absorption and scattering in turbid media. The reduced χ2 value (χR2) is known to judge efficiently goodness of fit, but is unable to judge accuracy of estimates in all situations; an incorrect model can fit the data leading to a good χR2 value, but provide incorrect estimates (see [31] and subsections 3.1 and 3.7).

The aim of this work is to demonstrate the strengths and the limits of using the χR2 value and weighted residuals (WRes) to judge the accuracy of μa and μs estimation in TRS. As proof of the principle of using χR2 to judge this accuracy, the study is restricted to a homogeneous medium characterized using the infinite diffusion equation. The paper is organized as follows. The experimental materials and numerical methods are described in Sect. 2, while the results are presented in Sect. 3. How χR2 value is affected by the well known sources of misestimation are investigated: influence of high absorption, distance between fibers, Instrument Response Function (IRF), and boundary effects, all of which are presented in Sect. 3.

2. Materials and methods

2.1 Experimental system

The experimental setup (Fig. 1 ) schematically consists of a subnanosecond excitation laser, a medium to be characterized, and a time-resolved detection system. A femtosecond laser is used, producing 775 nm pulses, with 80 MHz repetition rate (Tsunami, Spectra Physics). Photons are brought to the turbid medium to be characterized via an optical fiber (62.5 μm core diameter), providing an average typical power output of 2 mW; the pulses are broadened to a few picoseconds due to dispersion in the fiber. After interaction in the medium, photons are collected via another fiber (plastic-glass, 600 μm core diameter) and conveyed to a fast photomultiplier tube (PMT, R3809U-50, Hamamatsu Photonics). The electronic acquisition chain is a time-correlated single photon counting (TCSPC) card (SPC-630, Becker&Hickl). The time reference signal for TCSPC is generated by a fast photodiode from energy leakage of the laser. Recorded files provide a number of collected photons as a function of time, divided into 4096 channels of 3 ps. The absence of any correlation between channels was checked by repeating the same acquisition 50 times and measuring the correlation coefficients.

 

Fig. 1 Experimental setup. Sync stands for synchronization. Fibers are separated by a distance r and are both at depth z.

Download Full Size | PPT Slide | PDF

The Instrument Response Function (IRF) is measured by aligning emission and collection fibers separated by a known distance, in order to compensate for the induced time lag. The measured IRF has a temporal width of 50 ps (FWMH).

The turbid medium is made of an aqueous solution of Acronal (BASF, aqueous dispersion of styrene-acrylic copolymer) and black India ink (Dalbe, France). The dilution of each controls respectively scattering and absorption properties of this tissue-mimicking phantom. Due to high viscosity of both Acronal and ink, an intermediate solution is made in order to pipette these stock solutions precisely. The scattering properties of the Acronal stock solution can be evaluated with the time-resolved measurements at μs=180+/−20cm−1. The absorption property of the ink stock solution is μa(ink)=5.35+/−0.15cm−1, determined with an absorption UV-Visible spectrophotometer (Cary 300 Scan, Varian). Note that this is only the ink contribution to absorption as a water tank is used in the second line of the spectrometer. This measurement is used to cross-check the μa estimate by the time-resolved measurements using the following formula: μa=μa(ink)V(ink)/V(total)+μa(water,775nm), whereas V(ink) and V(total) are respectively the ink volume added to the solution and the volume of the final solution. Absorption of water at 775nm is μa(water,775nm)=0.027cm−1 and depends only slightly on temperature and purity [32].

Acronal solutions are used instead of conventional Intralipid because low absorption and diluted solutions are more stable: the same optical properties have been measured for a solution more than 6 months after its preparation. The fibers are immersed in the liquid at a controlled depth z and distance between fibers r, both typically approximately ten millimeters. Other distances, from fibers to boundaries, typically 5 cm, are kept sufficiently large compared to z, so that any deviation compared to the infinite model is controlled only by the fiber depth z.

This study explores the typical absorption and scattering of thick biological tissues, between 0.1 and 0.6 cm−1 for absorption, and 1 to 15 cm−1 for isotropic scattering. Values of 0.1 cm−1 for absorption, and 10 cm−1 for isotropic scattering are considered as reference values. Therefore, strong absorption is defined by values greater than 0.1 cm−1 and weak scattering by values smaller than 10 cm−1.

2.2 Monte Carlo simulations

When experimental data appear to deviate from expected behavior, in particular for the case of strong absorption, they are compared with MC data. The C written program used for simulation is derived from the Internet available version of Prahl [33]. The original program is described in [28,34], and has been adapted to record temporal profile at a given source-detector distance. Briefly, an isotropic point source launches photons, which propagate in isotropic and infinite geometry. In order to decrease computation time, photons are not terminated due to the absorption in the medium or due to the acquisition of the detector fiber, but only after a certain time tmax=7.6ns. It means that a photon can generate multiple detection events implying entangled photon events [24]. In the case of μs=14.2cm−1, source-detector distance of r=1.7cm, and time channel resolution of 3ps, it leads to correlation between typically five adjacent time channels (data not shown), the two first neighbors having a correlation coefficient of 0.5. This seems not to be a problem in this configuration as the number of fitting channels T is generally a few hundreds, large enough compared to 5. However, this point would require a more exhaustive study. In the case of strong absorption (less fitting channels) discussed in subsection 3.6, the effects of these correlations start to be visible.

To take absorption μa into account, data are further multiplied by exp(μact), where c is the speed of light in the medium. We call such simulation scalable MC, or MCs (even if the space is not scaled as in [24]). The drawback of this method for producing “artificial experimental data” is that the Poisson statistic of each channel is not kept – each channel has a standard deviation multiplied byexp(μact). This issue is important when fitting these data with any model, in particular the diffusion model (see subsection 2.4).

To overcome this issue, MC simulations taking absorption into account (we call these other simulations MCa, subscript a stands for absorption) have also been performed. After each scattering event, MCa has classically the probability (1-albedo) to kill a photon, where albedo=(μs/(μsa)). However, for a question of computation time, MCa simulations were used only when the statistic issue is at stake.

2.3 Model

Data modeling is based on the diffusion equation transport theory in an infinite homogeneous medium. Validity of the diffusion equation implies, in particular, that multiple scattering occurs, that is to say the mean free path of scattering is small compared both to r and the mean free path of absorption. Moreover, as the first arriving photons have not undergone sufficient scattering events, the first-time channels after the excitation pulse, which do not follow the diffusion equation [22], are excluded. Homogeneity of the medium is ensured by stirring the liquid phantom before acquisition. Measured quantities are assumed to be proportional to the fluence rate φ which is the integrated radiance over all solid angles expressed in watts per square meter. The flux termϕz in the measured quantities is neglected, which is appropriate for an infinite medium [23].

Following the diffusion assumptions, and assuming a point source at the origin in time and space, the measured quantities are proportional to the following model m [15,35]:

mc(4πDc)3/2t3/2exp(r24Dctμact)
where D=13μs' is the diffusion coefficient, assumed to be absorption-independent [36], and r is the distance between a virtual isotropic source and the detector fiber. The virtual isotropic source is classically assumed to be l*=1/μs beneath the fiber source [15]. Due to the results described in subsection 3.2, the distance r is chosen to be 1.7cm, when not specified.

2.4 Fitting procedure

The fitting procedure goal is to estimate two optical parameters, μa and μs, from the temporal profile recorded, containing numerous data points. The Nonlinear Least Squares method is used for the estimation; more precisely, a Nelder-Mead simplex algorithm drives minimization of a reduced χR2 error function which measures the deviation with respect to the above model. The χR2 error value is expressed as:

χR2=1Tpi(WRi)2=1Tpi(misisi)2
where T (typically a few hundred) is the number of temporal channels i used in the fitting procedure, p is the number of estimated parameters (p=2 here, μa and μs), mi and si represent the model and measured signal per temporal channel ti. WRi stands for weighted residual: 1σi=1si is the weight of each residual, σi corresponding to the standard deviation of each point. As the probability of collecting one photon per laser pulse is kept small compared to unity, the data essentially follow Poisson statistics, and noise can then be approximated by a zero-centered Gaussian statistic with standard deviation given by the square of the signal, for high numbers (here, more than 100 photons). Thus, the weighted residuals (WRi) should oscillate around zero and each term of the sum contributes to one on average, and so the reduced χ2 can be expected to be 1.

Before running the fitting procedure, background (corresponding to the average random obscurity noise from the PMT) is subtracted, and the model is then convolved with the measured IRF and multiplied by a constant to obtain the same energy (integral over time) as the signal. This latter operation, called normalization, following Cubeddu et al. [21], permits to fit only the shape of the curve regardless of amplitude variation. Thus, only two parameters instead of classically three are used for the fitting algorithm, which becomes faster and more robust. By normalizing the model without changing the signal, the fit covers only the temporal profile of the curve, and the Poisson noise distribution of the data is kept. The fit is carried out between t1 corresponding to 95% of the maximum on the rising edge (because the first arriving photons do not follow the diffusion equation) and t2 corresponding to 5% of the maximum on the trailing edge (noise limitation), except where otherwise stated.

When using MCs data, the fitting procedure has to be modified. Indeed, the statistic of the raw MCs signal si is modified by the absorption weighting si'=siexp(μact). The distribution can still be approximated by a Gaussian statistic but with a standard deviation of σi'=siexp(μact)=si'exp(0.5*μact). Technically, residuals of the fitting procedure have to be weighted differently as all other data (including MCa data), due to this modified statistic. Figure 2 clearly emphasizes the need of the correction. When the correction is omitted, weighted residuals are no longer randomly distributed around 0, and the associated χR2 value has no signification. Besides, late arriving photons contribute less to the fit, leading to incorrect estimation, especially for μa – but surprisingly, the error due to the omitted correction is very small (just a few percents).

 

Fig. 2 Weighted residuals, (a) uncorrected and (b) corrected, as a function of time (scalable MC simulation, μs’=7cm−1, μa=0.03cm−1, fitted by the diffusion equation). The fitting range 95/1 has been extended to 1% of the trailing edge in order to better emphasize the effect of the correction.

Download Full Size | PPT Slide | PDF

Due to the correction, a good fit still corresponds to a χR2 value of 1, whereas a bad fit corresponds to a significantly higher value.

2.5 Goodness of fit

A high value of χR2, together with a non-random pattern of weighted residual, is indicative of a bad fit, thus providing poor confidence in estimated parameters. Assuming both the model is correct and there are 200 independent measurements (in this case time points), there are less than five chances in 100 of finding a χR2 value above 1.17 [31]. Such probabilities can be found using χ2 distribution which tends towards normal distribution for high degrees of liberty (in practice, a normal distribution is used for T>200). Table 1 gives a few probabilities for usual situations in TCSPC when all the above assumptions are verified, in particular model validity. Moreover, properties of normal distribution imply that less than 5% of the absolute weighted residual points should be above 2, and less than 1% above 3.

Tables Icon

Table 1. Probability of finding a certain value of χR2 or higher, as a function of T, number of degrees of liberty, due to random distribution of photons. Deviation from the values in this table is indicative that the model is false.

A deviation in these characteristics can be indicative of a systematic error, or a model deviation of the data. On the other hand, a good value of χR2 and behavior of weighted residuals is not proof that the fit is good and that the estimated parameters are correct. This study aims at giving an insight into what can be expected from these criteria to estimate the optical parameters, μa and μs.

3. Results

3.1 χR2 and number of photons

To take model deviation into account, a bias term is added. This term takes into account discrepancies between data and the diffusion model. Bias bi is greater for early times, because the first arriving photons do not follow the diffusion model as they do not undergo sufficient scattering events. Both mi and bi depend on the experimental conditions, such as optical properties of the medium, and distance between fibers r. For a particular time channel ti, the signal si is si=mi+bi+εi, where si follows a Poisson distribution; thus εi is approximated by a random zero-centered Gaussian noise for more than a few tens of photons, with amplitudeσi=mi+bisi. In the following, random variables (si,εi,χR2) will be denoted with italic letters, whereas realization-independent quantities (bi,mi) will be denoted with roman letters. The χR2 components then become:

(misiσi)2=εi2σi2+2biσi2εi+bi2σi2εi2si+2bisiεi+bi2si

If the experiment is repeated many times with identical experimental conditions, a distribution is obtained for each of the three terms and for each time channel. Averaging the first term of Eq. (3) tends to one when the number of experiments increases, the second term tends to zero due to the zero centered εi distribution and the fact that εi is not correlated with bi. The last term tends to zero when the model fits the data (bi=0), but will stay strictly positive when model deviation occurs.

When increasing the acquired number of photons N=isi (by increasing experimental acquisition time τ for example), signal-to-noise increases and a biased model is more easily detected withχR2. Note that summation for N is performed between the two time channel boundaries used for fitting, t1 and t2, and after background noise is subtracted. More precisely, the signal, the model and the bias also increase linearly with N, with the standard deviation of the signal increasing with N. Indeed, if quantities are introduced with a reduced number of photons dependence, si=Nsi and bi=Nbi, the last term of Eq. (3) becomes Nbi2si. It can then be expected that χR2will increase linearly with the total number of photons N, χR2=1+NΔ, where Δ=ibi2si is strictly positive when there is a bias and this gives an indication of the model error. It was also found that χR2 tends to one in the limit of a small number of acquired photons, regardless the model and its bias with respect to experimental values. This means that the χR2 value (and weighted residual distribution) has no meaning without an indication of the total number of acquired photons. A χR2 value of one is easily found, regardless of whether the model is correct or not, if just a few photons are acquired. This also means that a large number of photons is necessary in order to choose between two distinct models: this property is known to lifetime measurers when choosing between one, two, or three decay components [31].

Experimentally, due to the large IRF (>50ps) and slowly varying diffusion intensities compared to time channels (3ps with our TSCPC card), averaging is already performed in χR2 summation between adjacent channels. Indeed, the experimental data shown in Fig. 3(b) illustrate perfectly the linear increase in χR2 with the total number of acquired photons, and also the limit of one when the number of photons tends to zero. This confirms that in order for χR2 to be a good criterion to judge the fit, the number of experimentally acquired photons must be increased. In the following, care is taken to keep this number to approximately 1.5*107, and a corrected χR2 is introduced to remove the effect of the remaining number of photons: χRC2=1+1.5*107(χR21)/N.

 

Fig. 3 (a) χR2 as a function of acquired photons for two solutions, varying ink concentrations and with same scatterer concentration (μs=16cm−1). Linear fits are also plotted, emphasizing the behavior of χR2; for each fit a typical R2 value of 0.98 and an intercept of 1.01 ± 0.04 are obtained. Each data point corresponds to a different experiment, except in the case of a high number of photons for which independent acquisitions were added. (b) Weighted residuals for increasing numbers of acquired photons when there is only little model bias (expected μa=0.1cm−1) and (c) worse model bias (expected μa=0.3cm−1).

Download Full Size | PPT Slide | PDF

Figure 3(b) and 3(c) show that weighted residuals behave similarly as the χR2 value when increasing the number of photons: weighted residuals randomly distributed around 0 implies a χR2 value around 1, even when there is a model bias, if the number of acquired photons N is too small. On the contrary, a high χR2 value corresponds to un-random distribution of weighted residuals. Thus, in the case of characterization of turbid media, this number is as high as a few millions (Fig. 3). Such high number may require enough time acquisition which could be in contradiction with the medical needs.

3.2 Influence of the distance between fibers

At a fiber depth of 28 mm, where boundary effects can be ignored (see paragraph 3.7), temporal intensity profiles were measured as a function of r, distance between source and detector fibers. For each of these temporal profiles, a fitting procedure is performed providing μa and μs estimates as well as the χRC2 parameter (corrected by taking N into account) and weighted residuals for each point used in the fitting procedure. Figure 4(a) shows these estimates and χRC2 as a function of r. Both μa and μs are overestimated when r is too short (respectively 30% and 45% error for r=0.5cm), before reaching a plateau for μs=7.9 cm−1 and μa=0.106 cm−1, considered as “the true values”, the latter being checked by the absorption spectroscopy method. Measurements at the two first distances lead to a high value of χRC2 (χRC2=7.3 for r=0.5cm), and non-random weighted residuals, which warn experimenters about the problem. For r=1.1cm and above, μa has already reached the plateau and μs is overestimated by 10% or less, and χRC2 also reaches a “plateau” at 1.5±0.5. In the following, distance between fibers is kept to r=1.7cm when not specified. We attribute the high value of 1.5 at the plateau to unknown systematic errors (light leakage, slightly incorrect IRF) together with a high number of acquired photons which exhibits any little bias. Note that when the distance between fibers is increased, the collected intensity strongly decreases: as a result, the optical density before the PMT and the gain voltage applied to the PMT had to be changed. For each applied voltage, the IRF with the same gain was performed and used for deconvolution. The deviation due to the short detection distance can be explained by the fact that early arriving photons do not follow the diffusion behavior – these photons predominate because source and detector are close to each other. χRC2 is thus a useful value for indicating parameter overestimation due to the short distance r between fibers, especially in the case of μa estimation.

 

Fig. 4 (a) Estimated parameters, μa and μs, as a function of interfiber distance r. The corresponding calculated parameter χR-C 2 is plotted. (b) Measured temporal profile, together with the estimated diffusion profile in an infinite medium for r = 0.8cm. The associated profile of weighted residuals over the fitting zone is clearly not randomly distributed around zero, emphasizing how wrong this fit is.

Download Full Size | PPT Slide | PDF

3.3 Influence of the IRF used for deconvolution

When performing photon counting experiments, the voltage of the PMT can be varied, typically between 2800 and 3400V. This changes the gain, but also the IRF because electrons are accelerated differently inside the PMT: with higher voltages, the IRF peak arrives earlier and is narrower. Deconvolution by an incorrect IRF can lead to misestimating of parameters by more than 20% (data not shown), and this can happen when IRF is measured in inappropriate conditions, such as after reflection on a white support or using a different gain than for acquisition. IRF has been measured by separating the excitation and collection fibers by a known distance l (with further correction by a time shift Δt=l/c), in order to decrease light intensity and to keep all other measurement parameters unchanged (same gain, same filters). The manner in which χRC2 is affected by an incorrect IRF was explored by changing the PMT voltage. Table 2 (and other data not presented) illustrates how a voltage change increases χRC2 (and degrades the associated weighted residual profile): χRC2 is also an ideal means of improving confidence in the IRF measurement.

Tables Icon

Table 2. Effect of deconvolution by an incorrect IRF. PMT voltage during the experiment was 2.83 kV.

Different IRF have also been measured using or not a piece of paper to excite all the modes of the collection fiber, as proposed by [37]. The white paper placed before the collection fiber slightly widen the IRF, but only little changes have been noticed in both optical properties estimates and χRC2 (data not shown).

3.4 Weak scattering tissues or phantom

The validity of the diffusion equation is restricted to the condition μs>>μa. To test this condition, the temporal profile was measured for solutions with an increasing concentration of Acronal scatterers. As shown on Fig. 5 , the high χRC2 values at weak scattering highlight the limits of the model, μa and μs estimates can thus be rejected. Figure 5 was drawn up using a fit interval t2 corresponding to 0.5% of the maximum instead of 5% in order to show the slight decreasing trend of μa due to dilution, which is expected. For t2 taken at 5% of the maximum (data not shown), estimates are modified by less than 2% for μa but are then not able to show the decrease, and less than 3% for μs.

 

Fig. 5 Estimated parameters, μa and μs, as a function of Acronal concentration, for t2 taken at 0.5% of the maximum. The corresponding calculated parameter χR-C 2 is plotted. For comparison, the expected μa values provided by absorption spectrometer measurements (the error bar arises from μa estimation error of the stock solution, but is identical for each point), and the linear increase in μs were plotted assuming the value of the most concentrated solution to be correct. Number of time channels used for fitting are between T=425 for the smallest and T=700 for the strongest concentration of Acronal.

Download Full Size | PPT Slide | PDF

3.5 Strong absorption tissues or phantom: experimental artifacts

Strong absorption is another situation widely encountered in biological tissues, such as for prostate [24]. Multiplying the zero-absorption equation by exp(μact) obviously increases the relative amplitude of the early part of the signal. As the diffusion equation fails to fit correctly the first arriving photons, it leads to overestimation of both μa and μs [30].

In this subsection, the case of strong absorption is investigated starting with μa=0.1cm−1 and μs=15cm−1, and further increasing ink concentration. Figure 6(a) shows how μa and μs are both underestimated for strongly absorbing media, which is in contradiction with previous results comparing experimental data, diffusion equation and MC simulations [24, 25, 30]. The first three measurements, corresponding to a “small” absorption value and leading to parameter underestimation of less than 10%, are associated with a χRC2 value below 2. With increasing ink concentration, the error increases and χRC2 also increases considerably, reaching χRC2 =85 for expected μa=0.53cm−1 (26% underestimation).

 

Fig. 6 (a) Estimated parameters, μa and μs, as a function of ink concentration. The corresponding calculated parameter χRC2 is plotted. After the first three measurements, the χRC2 values deviate from their plateau, thus highlighting incorrect fit. Number of channels used for fitting are T=470 for the lowest and T=190 for the strongest ink concentration. (b) Comparison between measured temporal profiles and MC simulation convolved with the experimental IRF, for excepted μa=0.4cm−1 and μs'=14.2cm−1 ([ink]=70 arbitrary units). The circle emphasizes the unexpected behavior of the data.

Download Full Size | PPT Slide | PDF

In order to understand the controversy between the underestimation found here, and the reported overestimation known for strong absorption, Fig. 6(b) compares the experimental data and the simulated MC data convolved with the measured IRF. Underestimation of μa is explained by the tail which is higher than expected. We checked that this behavior cannot be explained by different IRF measurements, in particular with or without using a white paper to excite the different modes of the collection fiber [37]. Often, later photons are excluded of the fitting range [19] in order to eliminate low signal level were excitation leakage or other experimental artifacts could explain such behavior.

Note that underestimation behavior was also already found with time resolved experiments, probably for the same small signal to artifacts reasons (see Fig. 4(a) and 4(c) in [38]). Consequently, when facing experimental artifacts in the case of small signal-to-noise ratio appearing in strong absorption media, χRC2 is a pertinent value to analyze to warn experimenters about potential problems.

3.6 Strong absorption: MC simulation

In order to investigate strong absorption, and because we were not able to eliminate experimental artifacts described in previous subsection, MC data were simulated to replace experimental data. In all cases investigated (μs’ between 5 and 15 cm−1), strong absorption results in overestimation of both μa and μs’, together with elevation of the χR2 value providing a sufficient number of photons is launched in the MC simulation (Fig. 7(a) ). Scalable MC (MCs) permits to demonstrate that χR2 significantly elevates when absorption increases due to the diffusion model bias at early times (Fig. 7(a) and data not shown). Indeed, with high enough number of acquired photons, estimation error is limited to 3% before χR2 elevates.

 

Fig. 7 (a) Estimation errors and the χR2 value are plotted as a function of absorption for μs’=7cm−1 (scalable MC). (b) The χR2 value as a function of N for MCa in the case of a prostate simulated medium. (c)(d) Weighted residuals corresponding to small and large number of photons of the Fig. 7(b) (note the WRes scale). Number of channels used for the fit is T=80.

Download Full Size | PPT Slide | PDF

However, the linear increase in χR2 as a function of N in the presence of a model bias, as shown in subsection 3.1, is lost with scalable MC, even after correction of the weighted residuals (data not shown). Consequently, MCa simulations (MC including the probability of absorbing a photon) have also been computed. Optical properties of the prostate have been chosen for the simulation (μa=0.3cm−1 and μs’=5cm−1 at 786nm [30]) but for a reduced fibers distance of r=1.2cm due to computational time issue. In this situation where diffusion approximation is known to fail due to the predominance of first arriving photons, the model bias is evidenced by high χR2 value providing a sufficient number of photons is acquired, typically a few millions. Indeed, Fig. 7(b) shows the perfect linear increase of the χR2 as a function of the number of photons. When including more early photons, in the 80/20 fitting range, the χR2 is higher, evidencing a worse estimation (13% and 11% overestimation for μa and μs’ compared to respectively 2% and 1% for the 95/5 fitting range). Figure 7(c) shows that weighted residuals are almost randomly distributed for a small number of photons but Fig. 7(d) clearly demonstrates the short time bias for a large number. Again, for a given model bias and a low number of photons, both the χR2 value and the weighted residuals behavior are misleading: both quantities require high enough photons to assess accuracy of the estimates.

In Fig. 7(b) and 7(c), correlation between channels is expected to be responsible for the χR2 value clearly below one (0.87+/−0.05) for a small number of photons, (channels are typically correlated with their 5 first neighbors, which is no more negligible compared to T=80).

3.7 Boundary effects

Surface boundaries with air are responsible for erroneous estimation of μa and μs [23], when using the infinite diffusion model. For example, photons which pass the surface escape definitively from the diffusing medium; the fitting algorithm compensates for these losses by overestimating the μa parameter. This implies that the curve tail is steeper, so that the time measured to obtain the peak intensity, tmax, is underestimated. And given that μs increases with tmax [15,39], μs is also underestimated. These deviations from an infinite medium as a function of depth are shown in Fig. 8 ; the plateau of an infinite medium is almost reached at z = 2cm. Except for the first two depth values, where the χRC2 value is higher than the others, there is no clue given by either the weighted residuals or χRC2 of the deviation due to the surface boundary. This means that a plane surface boundary with air modifies the temporal profile but in such a way that other optical parameters give a similar profile; the algorithm can compensate for the air-boundary effect without any significant degradation of the fit criteria. Consequently, the main drawback of the χRC2 and weighted residual criteria for judging the accuracy of the estimators is the presence of a plane boundary with air: an error of 17% in μa is reached without any clear indication on χRC2 (whereas an error of less than 5% is obtained for μs). The distance to all boundaries has to be chosen precisely by experimenters in order to avoid any such misestimation which cannot be highlighted by χRC2.

 

Fig. 8 Estimated parameters, μa and μs, as a function of fiber depth z for r = 1.7cm. The corresponding χRC2 parameter is plotted.

Download Full Size | PPT Slide | PDF

The presence of a semi-reflective element also imposes different boundary conditions for propagating photons and, obviously, the infinite model does not take these into account. A possible cause of artifact could, for example, be a metal support for an excitation fiber where the end of the optical fiber is located too close to the metal surface. Figure 9 shows the comparison between different holders for the excitation fiber: the optical fiber is either both stripped and inserted in a thin metallic needle, or the fiber end with its commercial connector is just inserted in a large metallic holder. Note that the collection fiber is rigid enough so that no specific holder close to the fiber end is necessary. The large holder modifies data so that neither the infinite model nor the μa and μs’ estimates are correct anymore. It is noticed that early photons are more affected by the large holder. Consequently, Fig. 9(a) shows weighted residuals and χRC2 for a 80/5 fitting range. Both statistical quantities warn the experimenters about the artifact. As the validity of the model and the χRC2 value depend together on the fitting range, Table 3 summarizes the results.

 

Fig. 9 (a) Weighted residuals and χRC2 are plotted for both holders (same solution). The fitting range has been extended to 80/5 in order to include more early photons to evidence the failure of the model in the presence of the large metallic piece. (b) Pictures of the holders. The needle holder is used in all the other data presented in the article.

Download Full Size | PPT Slide | PDF

Tables Icon

Table 3. Estimates of the Acronal solution as a function of fiber holders and fitting range. The error introduced by the presence of the large metallic holder is also indicated.

3.8 Experimental uncertainties

Looking at the diffusion equation, an erroneous measurement of the distance r is completely compensated by the diffusion coefficient D, leading to an incorrect estimate of μs, but the same estimate of μa and the same residuals between model and data: there is no clue given to experimenters of this potential error. Due to the squared dependence of r in the exponential, a 5% error in r will be responsible for a 10% error in μs.

Another possible error could come from the IRF distance measurement which has to be compensated; a time shift could thus be introduced. Figure 10 shows the error in estimated parameters if a small time shift is introduced, without any substantial change in χRC2, between plus 30ps and minus 15ps. Note that a time shift of 17ps, corresponding to a 5mm error in distance l between fibers when measuring IRF, leads to an error of around 6% in μs and 2% in μa. As the μs estimation is directly linked with tmax, it is normal that μs is more affected than μa by a time shift. On the other hand, for abnormally big time shifts (such as 100ps), a high χRC2 (such as 3) warns experimenters about the problem.

 

Fig. 10 Estimated parameters, μa and μs, as a function of an artificial introduced time shift. The corresponding calculated parameter χRC2 is plotted. Each grid line corresponds to a 5% error. Note that for a reasonable r measurement error (less than 5 mm) during the IRF, the time shift is very small (less than 17 ps).

Download Full Size | PPT Slide | PDF

4. Conclusion and summary

The infinite diffusion model for light propagation in a diffusing medium is attractive given its computational rapidity and ease of use. However, model inversion for estimating optical parameters presents limitations due to the model itself. This article shows that when there is a model bias, χR2 increases linearly with the number of acquired photons: a high number of acquired photons is thus required to discriminate between models and/or to test model validity. Moreover, regardless of the model bias, χR2 always tends to one when the number of acquired photons is too low. The authors therefore recommend that this number should always be provided together with χR2 and weighted residuals to judge goodness of fit. These two conclusions are not specific to time-resolved diffuse optical parameter estimation, but also apply to different χR2 estimations, in particular for estimating dye lifetime.

Weighted residuals and the χR2 value can both judge a model bias providing enough photons are acquired: for a χR2 value around one is obtained when weighted residuals are randomly distributed around zero, and weighted residuals are correlated (which could be checked by autocorrelation) for χR2 above one. However, as the χR2 value can sometimes be ambiguous, weighted residuals should always be shown and analyzed. If the ambiguity remains, the experiment could be made again with more photons, ideally with a longer acquisition time.

In the field of turbid media characterization, experimenters should at least acquire a few million photons to assess accuracy of estimation with the χR2 value and weighted residuals. If significantly fewer photons are acquired, only big artifacts can be detected with these statistical quantities; accuracy of μa and μs’ estimation cannot be assessed.

In this article, a correct situation (with satisfactory estimation) corresponds to a χRC2 value of less than 2, which is already a high value. This is due to the large number of photons, N=1.5*107, and the fact that both the diffusion equation is not perfectly applicable and there are remaining little experimental artifacts such as small light leakage or slightly incorrect IRF measurements. For the situations reviewed in this article and summarized in Table 4 , χRC2 and the associated weighted residuals are powerful quantities for judging the accuracy of estimation except for μa in the presence of a flat air boundary and μs when there is an error in fiber distance r. More precisely, with regard to the influence of fiber distance, IRF, weak scattering, strong absorption, and the presence of semi-reflective elements, these criteria are pertinent for judging the accuracy of estimation. With regard to surface effects, the model compensates for photon loss by overestimating μa without significant degradation of χRC2: in this case, the criteria are less pertinent for judging the accuracy of μa estimation but are still appropriate for μs. The table should be read as follows: when one obtains a χRC2 value of around 1 (or even 2 as Nref=1.5*107), the estimates are reliable providing surface interface is far enough and distance r between fibers is precise. On the contrary, if χRC2 is really high (more than 2), one can expect incorrect estimates. The explanation can be found in the table as being incorrect IRF, experimental artifacts, weak scattering, strong absorption, or too short fiber distance. The last three explanations are related to model error and are specific to the use of diffusion equation. However, the presence of a plane air boundary interface or a wrong fiber distance measurement cannot explain the bad fit.

Tables Icon

Table 4. Summary of the role of χRC2 for judging the accuracy of estimation together with typical situations encountered by experimenters. When not specified, this table refers to values of μa~0.1cm−1 and μs~10cm−1. Significant increase of χRC2 means χRC2>2 (due to the large number of photons N used as a reference).

Monte Carlo simulations have been made in order to analyze our results and to track any experimental artifacts, in particular in the case of strong absorption. Both MC scalable with absorption and MC taking absorption into account have been performed. The modified statistic of the scalable MC has been evidenced. The linear increase in χR2 with the number of photons in the presence of a model bias has been confirmed with the MC simulations taking absorption into account.

To provide more exhaustive results, a future study will consider the case of inhomogeneous media, more particularly the presence of inclusions with different optical properties. The use of a semi-infinite model, or other models closer to radiative transport theory, will also be investigated.

Acknowledgments

This work was supported by the French National Research Agency (ANR) through Carnot funding. The authors also wish to thank Graham Sumner for correcting the English text, and both referees for valuable comments and suggestions.

References and links

1. V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005). [CrossRef]   [PubMed]  

2. V. V. Tuchin, Handbook of optical biomedical diagnostics. (2002).

3. L. Spinelli, A. Torricelli, A. Pifferi, P. Taroni, G. M. Danesini, and R. Cubeddu, “Bulk optical properties and tissue components in the female breast from multiwavelength time-resolved optical mammography,” J. Biomed. Opt. 9(6), 1137–1142 (2004). [CrossRef]   [PubMed]  

4. B. C. Wilson and M. S. Patterson, “The physics, biophysics and technology of photodynamic therapy,” Phys. Med. Biol. 53(9), R61–R109 (2008). [CrossRef]   [PubMed]  

5. A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50(4), R1–R43 (2005). [CrossRef]   [PubMed]  

6. R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005). [CrossRef]   [PubMed]  

7. A. Corlu, R. Choe, T. Durduran, M. A. Rosen, M. Schweiger, S. R. Arridge, M. D. Schnall, and A. G. Yodh, “Three-dimensional in vivo fluorescence diffuse optical tomography of breast cancer in humans,” Opt. Express 15(11), 6696–6716 (2007). [CrossRef]   [PubMed]  

8. A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008). [CrossRef]   [PubMed]  

9. X. F. Cheng and D. A. Boas, “Systematic diffuse optical image errors resulting from uncertainty in the background optical properties,” Opt. Express 4(8), 299–307 (1999). [CrossRef]   [PubMed]  

10. V. Chernomordik, D. Hattery, I. Gannot, and A. H. Gandjbakhche, “Inverse method 3-D reconstruction of localized in vivo fluorescence - Application to Sjogren syndrome,” IEEE J. Sel. Top. Quant. 5(4), 930–935 (1999). [CrossRef]  

11. J. Swartling, J. S. Dam, and S. Andersson-Engels, “Comparison of spatially and temporally resolved diffuse-reflectance measurement systems for determination of biomedical optical properties,” Appl. Opt. 42(22), 4612–4620 (2003). [CrossRef]   [PubMed]  

12. B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988). [CrossRef]   [PubMed]  

13. D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of Optical Pathlength through Tissue from Direct Time of Flight Measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988). [CrossRef]   [PubMed]  

14. S. L. Jacques, “Time-resolved reflectance spectroscopy in turbid tissues,” IEEE Trans. Biomed. Eng. 36(12), 1155–1161 (1989). [CrossRef]   [PubMed]  

15. M. S. Patterson, B. Chance, and B. C. Wilson, “Time Resolved Reflectance and Transmittance for the Noninvasive Measurement of Tissue Optical-Properties,” Appl. Opt. 28(12), 2331–2336 (1989). [CrossRef]   [PubMed]  

16. B. Chance, S. Nioka, J. Kent, K. McCully, M. Fountain, R. Greenfeld, and G. Holtom, “Time-resolved spectroscopy of hemoglobin and myoglobin in resting and ischemic muscle,” Anal. Biochem. 174(2), 698–707 (1988). [CrossRef]   [PubMed]  

17. T. Svensson, J. Swartling, P. Taroni, A. Torricelli, P. Lindblom, C. Ingvar, and S. Andersson-Engels, “Characterization of normal breast tissue heterogeneity using time-resolved near-infrared spectroscopy,” Phys. Med. Biol. 50(11), 2559–2571 (2005). [CrossRef]   [PubMed]  

18. R. Cubeddu, C. D’Andrea, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Effects of the menstrual cycle on the red and near-infrared optical properties of the human breast,” Photochem. Photobiol. 72(3), 383–391 (2000). [PubMed]  

19. T. Svensson, S. Andersson-Engels, M. Einarsdóttír, and K. Svanberg, “In vivo optical characterization of human prostate tissue using near-infrared time-resolved spectroscopy,” J. Biomed. Opt. 12(1), 014022 (2007). [CrossRef]   [PubMed]  

20. K. M. Yoo, F. Liu, and R. R. Alfano, “When Does the Diffusion Approximation Fail to Describe Photon Transport in Random Media?” Phys. Rev. Lett. 64(22), 2647–2650 (1990). [CrossRef]   [PubMed]  

21. R. Cubeddu, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Experimental test of theoretical models for time-resolved reflectance,” Med. Phys. 23(9), 1625–1633 (1996). [CrossRef]   [PubMed]  

22. R. Cubeddu, M. Musolino, A. Pifferi, P. Taroni, and G. Valentini, “Time-Resolved Reflectance - a Systematic Study for Application to the Optical Characterization of Tissues,” IEEE J. Quantum Electron. 30(10), 2421–2430 (1994). [CrossRef]  

23. A. Laidevant, A. da Silva, M. Berger, and J. M. Dinten, “Effects of the surface boundary on the determination of the optical properties of a turbid medium with time-resolved reflectance,” Appl. Opt. 45(19), 4756–4764 (2006). [CrossRef]   [PubMed]  

24. E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon migration,” J. Biomed. Opt. 13(4), 041304 (2008). [CrossRef]   [PubMed]  

25. E. Alerstam, S. Andersson-Engels, and T. Svensson, “Improved accuracy in time-resolved diffuse reflectance spectroscopy,” Opt. Express 16(14), 10440–10454 (2008). [CrossRef]   [PubMed]  

26. A. Pifferi, A. Torricelli, P. Taroni, D. Comelli, A. Bassi, and R. Cubeddu, “Fully automated time domain spectrometer for the absorption and scattering characterization of diffusive media,” Rev. Sci. Instrum. 78(5), 053103 (2007). [CrossRef]   [PubMed]  

27. B. C. Wilson and G. Adam, “A Monte Carlo model for the absorption and flux distributions of light in tissue,” Med. Phys. 10(6), 824–830 (1983). [CrossRef]   [PubMed]  

28. L. H. Wang, S. L. Jacques, and L. Q. Zheng, “MCML-Monte Carlo Modeling of Light Transport in Multi-layered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995). [CrossRef]   [PubMed]  

29. S. T. Flock, M. S. Patterson, B. C. Wilson, and D. R. Wyman, “Monte Carlo Modeling of Light Propagation in Highly Scattering Tissue .1. Model Predictions and Comparison with Diffusion-Theory,” IEEE Trans. Biomed. Eng. 36(12), 1162–1168 (1989). [CrossRef]   [PubMed]  

30. T. Svensson, E. Alerstam, M. Einarsdóttír, K. Svanberg, and S. Andersson-Engels, “Towards accurate in vivo spectroscopy of the human prostate,” J. Biophoton. 1(3), 200–203 (2008). [CrossRef]  

31. J. R. Lakowicz, Principles of Fluorescence Spectroscopy, 3rd ed. (2006).

32. H. Buiteveld, J. H. M. Hakvoort, and M. Donze, “Optical properties of pure water,” Ocean Optics XIIProc. SPIE 2258, 174–183 (1994).

33. S. Prahl, “Monte Carlo Simulations,” http://omlc.ogi.edu/software/mc/.

34. S. A. Prahl, M. Keijzer, S. L. Jacques, and A. J. Welch, “A Monte Carlo model of light propagation in tissue,” Dosimetry of Laser Radiation in Medicine and Biology-Proc. SPIE IS 5, 102–111 (1989).

35. J. C. J. Paasschens, “Solution of the time-dependent Boltzmann equation,” Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 56(1), 1135–1141 (1997). [CrossRef]  

36. M. Bassani, F. Martelli, G. Zaccanti, and D. Contini, “Independence of the diffusion coefficient from absorption: experimental and numerical evidence,” Opt. Lett. 22(12), 853–855 (1997). [CrossRef]   [PubMed]  

37. A. Liebert, H. Wabnitz, D. Grosenick, and R. Macdonald, “Fiber dispersion in time domain measurements compromising the accuracy of determination of optical properties of strongly scattering media,” J. Biomed. Opt. 8(3), 512–516 (2003). [CrossRef]   [PubMed]  

38. A. Pifferi, A. Torricelli, A. Bassi, P. Taroni, R. Cubeddu, H. Wabnitz, D. Grosenick, M. Möller, R. Macdonald, J. Swartling, T. Svensson, S. Andersson-Engels, R. L. van Veen, H. J. Sterenborg, J. M. Tualle, H. L. Nghiem, S. Avrillier, M. Whelan, and H. Stamm, “Performance assessment of photon migration instruments: the MEDPHOT protocol,” Appl. Opt. 44(11), 2104–2114 (2005). [CrossRef]   [PubMed]  

39. L. Leonardi and D. H. Burns, “Quantitative measurements in scattering media: Photon time-of-flight analysis with analytical descriptors,” Appl. Spectrosc. 53(6), 628–636 (1999). [CrossRef]  

References

  • View by:
  • |
  • |
  • |

  1. V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
    [CrossRef] [PubMed]
  2. V. V. Tuchin, Handbook of optical biomedical diagnostics. (2002).
  3. L. Spinelli, A. Torricelli, A. Pifferi, P. Taroni, G. M. Danesini, and R. Cubeddu, “Bulk optical properties and tissue components in the female breast from multiwavelength time-resolved optical mammography,” J. Biomed. Opt. 9(6), 1137–1142 (2004).
    [CrossRef] [PubMed]
  4. B. C. Wilson and M. S. Patterson, “The physics, biophysics and technology of photodynamic therapy,” Phys. Med. Biol. 53(9), R61–R109 (2008).
    [CrossRef] [PubMed]
  5. A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50(4), R1–R43 (2005).
    [CrossRef] [PubMed]
  6. R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
    [CrossRef] [PubMed]
  7. A. Corlu, R. Choe, T. Durduran, M. A. Rosen, M. Schweiger, S. R. Arridge, M. D. Schnall, and A. G. Yodh, “Three-dimensional in vivo fluorescence diffuse optical tomography of breast cancer in humans,” Opt. Express 15(11), 6696–6716 (2007).
    [CrossRef] [PubMed]
  8. A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
    [CrossRef] [PubMed]
  9. X. F. Cheng and D. A. Boas, “Systematic diffuse optical image errors resulting from uncertainty in the background optical properties,” Opt. Express 4(8), 299–307 (1999).
    [CrossRef] [PubMed]
  10. V. Chernomordik, D. Hattery, I. Gannot, and A. H. Gandjbakhche, “Inverse method 3-D reconstruction of localized in vivo fluorescence - Application to Sjogren syndrome,” IEEE J. Sel. Top. Quant. 5(4), 930–935 (1999).
    [CrossRef]
  11. J. Swartling, J. S. Dam, and S. Andersson-Engels, “Comparison of spatially and temporally resolved diffuse-reflectance measurement systems for determination of biomedical optical properties,” Appl. Opt. 42(22), 4612–4620 (2003).
    [CrossRef] [PubMed]
  12. B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
    [CrossRef] [PubMed]
  13. D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of Optical Pathlength through Tissue from Direct Time of Flight Measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
    [CrossRef] [PubMed]
  14. S. L. Jacques, “Time-resolved reflectance spectroscopy in turbid tissues,” IEEE Trans. Biomed. Eng. 36(12), 1155–1161 (1989).
    [CrossRef] [PubMed]
  15. M. S. Patterson, B. Chance, and B. C. Wilson, “Time Resolved Reflectance and Transmittance for the Noninvasive Measurement of Tissue Optical-Properties,” Appl. Opt. 28(12), 2331–2336 (1989).
    [CrossRef] [PubMed]
  16. B. Chance, S. Nioka, J. Kent, K. McCully, M. Fountain, R. Greenfeld, and G. Holtom, “Time-resolved spectroscopy of hemoglobin and myoglobin in resting and ischemic muscle,” Anal. Biochem. 174(2), 698–707 (1988).
    [CrossRef] [PubMed]
  17. T. Svensson, J. Swartling, P. Taroni, A. Torricelli, P. Lindblom, C. Ingvar, and S. Andersson-Engels, “Characterization of normal breast tissue heterogeneity using time-resolved near-infrared spectroscopy,” Phys. Med. Biol. 50(11), 2559–2571 (2005).
    [CrossRef] [PubMed]
  18. R. Cubeddu, C. D’Andrea, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Effects of the menstrual cycle on the red and near-infrared optical properties of the human breast,” Photochem. Photobiol. 72(3), 383–391 (2000).
    [PubMed]
  19. T. Svensson, S. Andersson-Engels, M. Einarsdóttír, and K. Svanberg, “In vivo optical characterization of human prostate tissue using near-infrared time-resolved spectroscopy,” J. Biomed. Opt. 12(1), 014022 (2007).
    [CrossRef] [PubMed]
  20. K. M. Yoo, F. Liu, and R. R. Alfano, “When Does the Diffusion Approximation Fail to Describe Photon Transport in Random Media?” Phys. Rev. Lett. 64(22), 2647–2650 (1990).
    [CrossRef] [PubMed]
  21. R. Cubeddu, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Experimental test of theoretical models for time-resolved reflectance,” Med. Phys. 23(9), 1625–1633 (1996).
    [CrossRef] [PubMed]
  22. R. Cubeddu, M. Musolino, A. Pifferi, P. Taroni, and G. Valentini, “Time-Resolved Reflectance - a Systematic Study for Application to the Optical Characterization of Tissues,” IEEE J. Quantum Electron. 30(10), 2421–2430 (1994).
    [CrossRef]
  23. A. Laidevant, A. da Silva, M. Berger, and J. M. Dinten, “Effects of the surface boundary on the determination of the optical properties of a turbid medium with time-resolved reflectance,” Appl. Opt. 45(19), 4756–4764 (2006).
    [CrossRef] [PubMed]
  24. E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon migration,” J. Biomed. Opt. 13(4), 041304 (2008).
    [CrossRef] [PubMed]
  25. E. Alerstam, S. Andersson-Engels, and T. Svensson, “Improved accuracy in time-resolved diffuse reflectance spectroscopy,” Opt. Express 16(14), 10440–10454 (2008).
    [CrossRef] [PubMed]
  26. A. Pifferi, A. Torricelli, P. Taroni, D. Comelli, A. Bassi, and R. Cubeddu, “Fully automated time domain spectrometer for the absorption and scattering characterization of diffusive media,” Rev. Sci. Instrum. 78(5), 053103 (2007).
    [CrossRef] [PubMed]
  27. B. C. Wilson and G. Adam, “A Monte Carlo model for the absorption and flux distributions of light in tissue,” Med. Phys. 10(6), 824–830 (1983).
    [CrossRef] [PubMed]
  28. L. H. Wang, S. L. Jacques, and L. Q. Zheng, “MCML-Monte Carlo Modeling of Light Transport in Multi-layered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
    [CrossRef] [PubMed]
  29. S. T. Flock, M. S. Patterson, B. C. Wilson, and D. R. Wyman, “Monte Carlo Modeling of Light Propagation in Highly Scattering Tissue .1. Model Predictions and Comparison with Diffusion-Theory,” IEEE Trans. Biomed. Eng. 36(12), 1162–1168 (1989).
    [CrossRef] [PubMed]
  30. T. Svensson, E. Alerstam, M. Einarsdóttír, K. Svanberg, and S. Andersson-Engels, “Towards accurate in vivo spectroscopy of the human prostate,” J. Biophoton. 1(3), 200–203 (2008).
    [CrossRef]
  31. J. R. Lakowicz, Principles of Fluorescence Spectroscopy, 3rd ed. (2006).
  32. H. Buiteveld, J. H. M. Hakvoort, and M. Donze, “Optical properties of pure water,” Ocean Optics XII Proc. SPIE 2258, 174–183 (1994).
  33. S. Prahl, “Monte Carlo Simulations,” http://omlc.ogi.edu/software/mc/ .
  34. S. A. Prahl, M. Keijzer, S. L. Jacques, and A. J. Welch, “A Monte Carlo model of light propagation in tissue,” Dosimetry of Laser Radiation in Medicine and Biology - Proc. SPIE IS 5, 102–111 (1989).
  35. J. C. J. Paasschens, “Solution of the time-dependent Boltzmann equation,” Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 56(1), 1135–1141 (1997).
    [CrossRef]
  36. M. Bassani, F. Martelli, G. Zaccanti, and D. Contini, “Independence of the diffusion coefficient from absorption: experimental and numerical evidence,” Opt. Lett. 22(12), 853–855 (1997).
    [CrossRef] [PubMed]
  37. A. Liebert, H. Wabnitz, D. Grosenick, and R. Macdonald, “Fiber dispersion in time domain measurements compromising the accuracy of determination of optical properties of strongly scattering media,” J. Biomed. Opt. 8(3), 512–516 (2003).
    [CrossRef] [PubMed]
  38. A. Pifferi, A. Torricelli, A. Bassi, P. Taroni, R. Cubeddu, H. Wabnitz, D. Grosenick, M. Möller, R. Macdonald, J. Swartling, T. Svensson, S. Andersson-Engels, R. L. van Veen, H. J. Sterenborg, J. M. Tualle, H. L. Nghiem, S. Avrillier, M. Whelan, and H. Stamm, “Performance assessment of photon migration instruments: the MEDPHOT protocol,” Appl. Opt. 44(11), 2104–2114 (2005).
    [CrossRef] [PubMed]
  39. L. Leonardi and D. H. Burns, “Quantitative measurements in scattering media: Photon time-of-flight analysis with analytical descriptors,” Appl. Spectrosc. 53(6), 628–636 (1999).
    [CrossRef]

2008

B. C. Wilson and M. S. Patterson, “The physics, biophysics and technology of photodynamic therapy,” Phys. Med. Biol. 53(9), R61–R109 (2008).
[CrossRef] [PubMed]

A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
[CrossRef] [PubMed]

T. Svensson, E. Alerstam, M. Einarsdóttír, K. Svanberg, and S. Andersson-Engels, “Towards accurate in vivo spectroscopy of the human prostate,” J. Biophoton. 1(3), 200–203 (2008).
[CrossRef]

E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon migration,” J. Biomed. Opt. 13(4), 041304 (2008).
[CrossRef] [PubMed]

E. Alerstam, S. Andersson-Engels, and T. Svensson, “Improved accuracy in time-resolved diffuse reflectance spectroscopy,” Opt. Express 16(14), 10440–10454 (2008).
[CrossRef] [PubMed]

2007

A. Corlu, R. Choe, T. Durduran, M. A. Rosen, M. Schweiger, S. R. Arridge, M. D. Schnall, and A. G. Yodh, “Three-dimensional in vivo fluorescence diffuse optical tomography of breast cancer in humans,” Opt. Express 15(11), 6696–6716 (2007).
[CrossRef] [PubMed]

T. Svensson, S. Andersson-Engels, M. Einarsdóttír, and K. Svanberg, “In vivo optical characterization of human prostate tissue using near-infrared time-resolved spectroscopy,” J. Biomed. Opt. 12(1), 014022 (2007).
[CrossRef] [PubMed]

A. Pifferi, A. Torricelli, P. Taroni, D. Comelli, A. Bassi, and R. Cubeddu, “Fully automated time domain spectrometer for the absorption and scattering characterization of diffusive media,” Rev. Sci. Instrum. 78(5), 053103 (2007).
[CrossRef] [PubMed]

2006

2005

A. Pifferi, A. Torricelli, A. Bassi, P. Taroni, R. Cubeddu, H. Wabnitz, D. Grosenick, M. Möller, R. Macdonald, J. Swartling, T. Svensson, S. Andersson-Engels, R. L. van Veen, H. J. Sterenborg, J. M. Tualle, H. L. Nghiem, S. Avrillier, M. Whelan, and H. Stamm, “Performance assessment of photon migration instruments: the MEDPHOT protocol,” Appl. Opt. 44(11), 2104–2114 (2005).
[CrossRef] [PubMed]

T. Svensson, J. Swartling, P. Taroni, A. Torricelli, P. Lindblom, C. Ingvar, and S. Andersson-Engels, “Characterization of normal breast tissue heterogeneity using time-resolved near-infrared spectroscopy,” Phys. Med. Biol. 50(11), 2559–2571 (2005).
[CrossRef] [PubMed]

A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50(4), R1–R43 (2005).
[CrossRef] [PubMed]

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
[CrossRef] [PubMed]

2004

L. Spinelli, A. Torricelli, A. Pifferi, P. Taroni, G. M. Danesini, and R. Cubeddu, “Bulk optical properties and tissue components in the female breast from multiwavelength time-resolved optical mammography,” J. Biomed. Opt. 9(6), 1137–1142 (2004).
[CrossRef] [PubMed]

2003

J. Swartling, J. S. Dam, and S. Andersson-Engels, “Comparison of spatially and temporally resolved diffuse-reflectance measurement systems for determination of biomedical optical properties,” Appl. Opt. 42(22), 4612–4620 (2003).
[CrossRef] [PubMed]

A. Liebert, H. Wabnitz, D. Grosenick, and R. Macdonald, “Fiber dispersion in time domain measurements compromising the accuracy of determination of optical properties of strongly scattering media,” J. Biomed. Opt. 8(3), 512–516 (2003).
[CrossRef] [PubMed]

2000

R. Cubeddu, C. D’Andrea, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Effects of the menstrual cycle on the red and near-infrared optical properties of the human breast,” Photochem. Photobiol. 72(3), 383–391 (2000).
[PubMed]

1999

1997

M. Bassani, F. Martelli, G. Zaccanti, and D. Contini, “Independence of the diffusion coefficient from absorption: experimental and numerical evidence,” Opt. Lett. 22(12), 853–855 (1997).
[CrossRef] [PubMed]

J. C. J. Paasschens, “Solution of the time-dependent Boltzmann equation,” Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 56(1), 1135–1141 (1997).
[CrossRef]

1996

R. Cubeddu, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Experimental test of theoretical models for time-resolved reflectance,” Med. Phys. 23(9), 1625–1633 (1996).
[CrossRef] [PubMed]

1995

L. H. Wang, S. L. Jacques, and L. Q. Zheng, “MCML-Monte Carlo Modeling of Light Transport in Multi-layered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
[CrossRef] [PubMed]

1994

R. Cubeddu, M. Musolino, A. Pifferi, P. Taroni, and G. Valentini, “Time-Resolved Reflectance - a Systematic Study for Application to the Optical Characterization of Tissues,” IEEE J. Quantum Electron. 30(10), 2421–2430 (1994).
[CrossRef]

1990

K. M. Yoo, F. Liu, and R. R. Alfano, “When Does the Diffusion Approximation Fail to Describe Photon Transport in Random Media?” Phys. Rev. Lett. 64(22), 2647–2650 (1990).
[CrossRef] [PubMed]

1989

S. T. Flock, M. S. Patterson, B. C. Wilson, and D. R. Wyman, “Monte Carlo Modeling of Light Propagation in Highly Scattering Tissue .1. Model Predictions and Comparison with Diffusion-Theory,” IEEE Trans. Biomed. Eng. 36(12), 1162–1168 (1989).
[CrossRef] [PubMed]

S. L. Jacques, “Time-resolved reflectance spectroscopy in turbid tissues,” IEEE Trans. Biomed. Eng. 36(12), 1155–1161 (1989).
[CrossRef] [PubMed]

M. S. Patterson, B. Chance, and B. C. Wilson, “Time Resolved Reflectance and Transmittance for the Noninvasive Measurement of Tissue Optical-Properties,” Appl. Opt. 28(12), 2331–2336 (1989).
[CrossRef] [PubMed]

1988

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of Optical Pathlength through Tissue from Direct Time of Flight Measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[CrossRef] [PubMed]

B. Chance, S. Nioka, J. Kent, K. McCully, M. Fountain, R. Greenfeld, and G. Holtom, “Time-resolved spectroscopy of hemoglobin and myoglobin in resting and ischemic muscle,” Anal. Biochem. 174(2), 698–707 (1988).
[CrossRef] [PubMed]

1983

B. C. Wilson and G. Adam, “A Monte Carlo model for the absorption and flux distributions of light in tissue,” Med. Phys. 10(6), 824–830 (1983).
[CrossRef] [PubMed]

Adam, G.

B. C. Wilson and G. Adam, “A Monte Carlo model for the absorption and flux distributions of light in tissue,” Med. Phys. 10(6), 824–830 (1983).
[CrossRef] [PubMed]

Alerstam, E.

E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon migration,” J. Biomed. Opt. 13(4), 041304 (2008).
[CrossRef] [PubMed]

T. Svensson, E. Alerstam, M. Einarsdóttír, K. Svanberg, and S. Andersson-Engels, “Towards accurate in vivo spectroscopy of the human prostate,” J. Biophoton. 1(3), 200–203 (2008).
[CrossRef]

E. Alerstam, S. Andersson-Engels, and T. Svensson, “Improved accuracy in time-resolved diffuse reflectance spectroscopy,” Opt. Express 16(14), 10440–10454 (2008).
[CrossRef] [PubMed]

Alfano, R. R.

K. M. Yoo, F. Liu, and R. R. Alfano, “When Does the Diffusion Approximation Fail to Describe Photon Transport in Random Media?” Phys. Rev. Lett. 64(22), 2647–2650 (1990).
[CrossRef] [PubMed]

Andersson-Engels, S.

T. Svensson, E. Alerstam, M. Einarsdóttír, K. Svanberg, and S. Andersson-Engels, “Towards accurate in vivo spectroscopy of the human prostate,” J. Biophoton. 1(3), 200–203 (2008).
[CrossRef]

E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon migration,” J. Biomed. Opt. 13(4), 041304 (2008).
[CrossRef] [PubMed]

E. Alerstam, S. Andersson-Engels, and T. Svensson, “Improved accuracy in time-resolved diffuse reflectance spectroscopy,” Opt. Express 16(14), 10440–10454 (2008).
[CrossRef] [PubMed]

T. Svensson, S. Andersson-Engels, M. Einarsdóttír, and K. Svanberg, “In vivo optical characterization of human prostate tissue using near-infrared time-resolved spectroscopy,” J. Biomed. Opt. 12(1), 014022 (2007).
[CrossRef] [PubMed]

T. Svensson, J. Swartling, P. Taroni, A. Torricelli, P. Lindblom, C. Ingvar, and S. Andersson-Engels, “Characterization of normal breast tissue heterogeneity using time-resolved near-infrared spectroscopy,” Phys. Med. Biol. 50(11), 2559–2571 (2005).
[CrossRef] [PubMed]

A. Pifferi, A. Torricelli, A. Bassi, P. Taroni, R. Cubeddu, H. Wabnitz, D. Grosenick, M. Möller, R. Macdonald, J. Swartling, T. Svensson, S. Andersson-Engels, R. L. van Veen, H. J. Sterenborg, J. M. Tualle, H. L. Nghiem, S. Avrillier, M. Whelan, and H. Stamm, “Performance assessment of photon migration instruments: the MEDPHOT protocol,” Appl. Opt. 44(11), 2104–2114 (2005).
[CrossRef] [PubMed]

J. Swartling, J. S. Dam, and S. Andersson-Engels, “Comparison of spatially and temporally resolved diffuse-reflectance measurement systems for determination of biomedical optical properties,” Appl. Opt. 42(22), 4612–4620 (2003).
[CrossRef] [PubMed]

Arridge, S.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of Optical Pathlength through Tissue from Direct Time of Flight Measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[CrossRef] [PubMed]

Arridge, S. R.

A. Corlu, R. Choe, T. Durduran, M. A. Rosen, M. Schweiger, S. R. Arridge, M. D. Schnall, and A. G. Yodh, “Three-dimensional in vivo fluorescence diffuse optical tomography of breast cancer in humans,” Opt. Express 15(11), 6696–6716 (2007).
[CrossRef] [PubMed]

A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50(4), R1–R43 (2005).
[CrossRef] [PubMed]

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

Avrillier, S.

Bassani, M.

Bassi, A.

Berger, M.

A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
[CrossRef] [PubMed]

A. Laidevant, A. da Silva, M. Berger, and J. M. Dinten, “Effects of the surface boundary on the determination of the optical properties of a turbid medium with time-resolved reflectance,” Appl. Opt. 45(19), 4756–4764 (2006).
[CrossRef] [PubMed]

Boas, D. A.

Boretsky, R.

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Boutet, J.

A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
[CrossRef] [PubMed]

Burns, D. H.

Chance, B.

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

M. S. Patterson, B. Chance, and B. C. Wilson, “Time Resolved Reflectance and Transmittance for the Noninvasive Measurement of Tissue Optical-Properties,” Appl. Opt. 28(12), 2331–2336 (1989).
[CrossRef] [PubMed]

B. Chance, S. Nioka, J. Kent, K. McCully, M. Fountain, R. Greenfeld, and G. Holtom, “Time-resolved spectroscopy of hemoglobin and myoglobin in resting and ischemic muscle,” Anal. Biochem. 174(2), 698–707 (1988).
[CrossRef] [PubMed]

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Cheng, X. F.

Chernomordik, V.

V. Chernomordik, D. Hattery, I. Gannot, and A. H. Gandjbakhche, “Inverse method 3-D reconstruction of localized in vivo fluorescence - Application to Sjogren syndrome,” IEEE J. Sel. Top. Quant. 5(4), 930–935 (1999).
[CrossRef]

Choe, R.

A. Corlu, R. Choe, T. Durduran, M. A. Rosen, M. Schweiger, S. R. Arridge, M. D. Schnall, and A. G. Yodh, “Three-dimensional in vivo fluorescence diffuse optical tomography of breast cancer in humans,” Opt. Express 15(11), 6696–6716 (2007).
[CrossRef] [PubMed]

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

Cohen, P.

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Coll, J. L.

A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
[CrossRef] [PubMed]

Comelli, D.

A. Pifferi, A. Torricelli, P. Taroni, D. Comelli, A. Bassi, and R. Cubeddu, “Fully automated time domain spectrometer for the absorption and scattering characterization of diffusive media,” Rev. Sci. Instrum. 78(5), 053103 (2007).
[CrossRef] [PubMed]

Contini, D.

Cope, M.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of Optical Pathlength through Tissue from Direct Time of Flight Measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[CrossRef] [PubMed]

Corlu, A.

A. Corlu, R. Choe, T. Durduran, M. A. Rosen, M. Schweiger, S. R. Arridge, M. D. Schnall, and A. G. Yodh, “Three-dimensional in vivo fluorescence diffuse optical tomography of breast cancer in humans,” Opt. Express 15(11), 6696–6716 (2007).
[CrossRef] [PubMed]

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

Cubeddu, R.

A. Pifferi, A. Torricelli, P. Taroni, D. Comelli, A. Bassi, and R. Cubeddu, “Fully automated time domain spectrometer for the absorption and scattering characterization of diffusive media,” Rev. Sci. Instrum. 78(5), 053103 (2007).
[CrossRef] [PubMed]

A. Pifferi, A. Torricelli, A. Bassi, P. Taroni, R. Cubeddu, H. Wabnitz, D. Grosenick, M. Möller, R. Macdonald, J. Swartling, T. Svensson, S. Andersson-Engels, R. L. van Veen, H. J. Sterenborg, J. M. Tualle, H. L. Nghiem, S. Avrillier, M. Whelan, and H. Stamm, “Performance assessment of photon migration instruments: the MEDPHOT protocol,” Appl. Opt. 44(11), 2104–2114 (2005).
[CrossRef] [PubMed]

L. Spinelli, A. Torricelli, A. Pifferi, P. Taroni, G. M. Danesini, and R. Cubeddu, “Bulk optical properties and tissue components in the female breast from multiwavelength time-resolved optical mammography,” J. Biomed. Opt. 9(6), 1137–1142 (2004).
[CrossRef] [PubMed]

R. Cubeddu, C. D’Andrea, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Effects of the menstrual cycle on the red and near-infrared optical properties of the human breast,” Photochem. Photobiol. 72(3), 383–391 (2000).
[PubMed]

R. Cubeddu, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Experimental test of theoretical models for time-resolved reflectance,” Med. Phys. 23(9), 1625–1633 (1996).
[CrossRef] [PubMed]

R. Cubeddu, M. Musolino, A. Pifferi, P. Taroni, and G. Valentini, “Time-Resolved Reflectance - a Systematic Study for Application to the Optical Characterization of Tissues,” IEEE J. Quantum Electron. 30(10), 2421–2430 (1994).
[CrossRef]

Czerniecki, B. J.

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

D’Andrea, C.

R. Cubeddu, C. D’Andrea, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Effects of the menstrual cycle on the red and near-infrared optical properties of the human breast,” Photochem. Photobiol. 72(3), 383–391 (2000).
[PubMed]

Da Silva, A.

A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
[CrossRef] [PubMed]

A. Laidevant, A. da Silva, M. Berger, and J. M. Dinten, “Effects of the surface boundary on the determination of the optical properties of a turbid medium with time-resolved reflectance,” Appl. Opt. 45(19), 4756–4764 (2006).
[CrossRef] [PubMed]

Dam, J. S.

Danesini, G. M.

L. Spinelli, A. Torricelli, A. Pifferi, P. Taroni, G. M. Danesini, and R. Cubeddu, “Bulk optical properties and tissue components in the female breast from multiwavelength time-resolved optical mammography,” J. Biomed. Opt. 9(6), 1137–1142 (2004).
[CrossRef] [PubMed]

Delpy, D. T.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of Optical Pathlength through Tissue from Direct Time of Flight Measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[CrossRef] [PubMed]

DeMichele, A.

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

Dinten, J. M.

A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
[CrossRef] [PubMed]

A. Laidevant, A. da Silva, M. Berger, and J. M. Dinten, “Effects of the surface boundary on the determination of the optical properties of a turbid medium with time-resolved reflectance,” Appl. Opt. 45(19), 4756–4764 (2006).
[CrossRef] [PubMed]

Durduran, T.

A. Corlu, R. Choe, T. Durduran, M. A. Rosen, M. Schweiger, S. R. Arridge, M. D. Schnall, and A. G. Yodh, “Three-dimensional in vivo fluorescence diffuse optical tomography of breast cancer in humans,” Opt. Express 15(11), 6696–6716 (2007).
[CrossRef] [PubMed]

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

Einarsdóttír, M.

T. Svensson, E. Alerstam, M. Einarsdóttír, K. Svanberg, and S. Andersson-Engels, “Towards accurate in vivo spectroscopy of the human prostate,” J. Biophoton. 1(3), 200–203 (2008).
[CrossRef]

T. Svensson, S. Andersson-Engels, M. Einarsdóttír, and K. Svanberg, “In vivo optical characterization of human prostate tissue using near-infrared time-resolved spectroscopy,” J. Biomed. Opt. 12(1), 014022 (2007).
[CrossRef] [PubMed]

Finander, M.

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Flock, S. T.

S. T. Flock, M. S. Patterson, B. C. Wilson, and D. R. Wyman, “Monte Carlo Modeling of Light Propagation in Highly Scattering Tissue .1. Model Predictions and Comparison with Diffusion-Theory,” IEEE Trans. Biomed. Eng. 36(12), 1162–1168 (1989).
[CrossRef] [PubMed]

Fountain, M.

B. Chance, S. Nioka, J. Kent, K. McCully, M. Fountain, R. Greenfeld, and G. Holtom, “Time-resolved spectroscopy of hemoglobin and myoglobin in resting and ischemic muscle,” Anal. Biochem. 174(2), 698–707 (1988).
[CrossRef] [PubMed]

Fraker, D. L.

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

Gandjbakhche, A. H.

V. Chernomordik, D. Hattery, I. Gannot, and A. H. Gandjbakhche, “Inverse method 3-D reconstruction of localized in vivo fluorescence - Application to Sjogren syndrome,” IEEE J. Sel. Top. Quant. 5(4), 930–935 (1999).
[CrossRef]

Gannot, I.

V. Chernomordik, D. Hattery, I. Gannot, and A. H. Gandjbakhche, “Inverse method 3-D reconstruction of localized in vivo fluorescence - Application to Sjogren syndrome,” IEEE J. Sel. Top. Quant. 5(4), 930–935 (1999).
[CrossRef]

Gibson, A. P.

A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50(4), R1–R43 (2005).
[CrossRef] [PubMed]

Greenfeld, R.

B. Chance, S. Nioka, J. Kent, K. McCully, M. Fountain, R. Greenfeld, and G. Holtom, “Time-resolved spectroscopy of hemoglobin and myoglobin in resting and ischemic muscle,” Anal. Biochem. 174(2), 698–707 (1988).
[CrossRef] [PubMed]

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Grosenick, D.

Grosicka-Koptyra, M.

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

Hattery, D.

V. Chernomordik, D. Hattery, I. Gannot, and A. H. Gandjbakhche, “Inverse method 3-D reconstruction of localized in vivo fluorescence - Application to Sjogren syndrome,” IEEE J. Sel. Top. Quant. 5(4), 930–935 (1999).
[CrossRef]

Hebden, J. C.

A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50(4), R1–R43 (2005).
[CrossRef] [PubMed]

Hervé, L.

A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
[CrossRef] [PubMed]

Holtom, G.

B. Chance, S. Nioka, J. Kent, K. McCully, M. Fountain, R. Greenfeld, and G. Holtom, “Time-resolved spectroscopy of hemoglobin and myoglobin in resting and ischemic muscle,” Anal. Biochem. 174(2), 698–707 (1988).
[CrossRef] [PubMed]

Ingvar, C.

T. Svensson, J. Swartling, P. Taroni, A. Torricelli, P. Lindblom, C. Ingvar, and S. Andersson-Engels, “Characterization of normal breast tissue heterogeneity using time-resolved near-infrared spectroscopy,” Phys. Med. Biol. 50(11), 2559–2571 (2005).
[CrossRef] [PubMed]

Jacques, S. L.

L. H. Wang, S. L. Jacques, and L. Q. Zheng, “MCML-Monte Carlo Modeling of Light Transport in Multi-layered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
[CrossRef] [PubMed]

S. L. Jacques, “Time-resolved reflectance spectroscopy in turbid tissues,” IEEE Trans. Biomed. Eng. 36(12), 1155–1161 (1989).
[CrossRef] [PubMed]

Josserand, V.

A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
[CrossRef] [PubMed]

Kaufmann, K.

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Kent, J.

B. Chance, S. Nioka, J. Kent, K. McCully, M. Fountain, R. Greenfeld, and G. Holtom, “Time-resolved spectroscopy of hemoglobin and myoglobin in resting and ischemic muscle,” Anal. Biochem. 174(2), 698–707 (1988).
[CrossRef] [PubMed]

Koenig, A.

A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
[CrossRef] [PubMed]

Konecky, S. D.

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

Laidevant, A.

Lee, K.

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

Leigh, J. S.

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Leonardi, L.

Levy, W.

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Liebert, A.

A. Liebert, H. Wabnitz, D. Grosenick, and R. Macdonald, “Fiber dispersion in time domain measurements compromising the accuracy of determination of optical properties of strongly scattering media,” J. Biomed. Opt. 8(3), 512–516 (2003).
[CrossRef] [PubMed]

Lindblom, P.

T. Svensson, J. Swartling, P. Taroni, A. Torricelli, P. Lindblom, C. Ingvar, and S. Andersson-Engels, “Characterization of normal breast tissue heterogeneity using time-resolved near-infrared spectroscopy,” Phys. Med. Biol. 50(11), 2559–2571 (2005).
[CrossRef] [PubMed]

Liu, F.

K. M. Yoo, F. Liu, and R. R. Alfano, “When Does the Diffusion Approximation Fail to Describe Photon Transport in Random Media?” Phys. Rev. Lett. 64(22), 2647–2650 (1990).
[CrossRef] [PubMed]

Macdonald, R.

Martelli, F.

McCully, K.

B. Chance, S. Nioka, J. Kent, K. McCully, M. Fountain, R. Greenfeld, and G. Holtom, “Time-resolved spectroscopy of hemoglobin and myoglobin in resting and ischemic muscle,” Anal. Biochem. 174(2), 698–707 (1988).
[CrossRef] [PubMed]

Miyake, H.

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Möller, M.

Musolino, M.

R. Cubeddu, M. Musolino, A. Pifferi, P. Taroni, and G. Valentini, “Time-Resolved Reflectance - a Systematic Study for Application to the Optical Characterization of Tissues,” IEEE J. Quantum Electron. 30(10), 2421–2430 (1994).
[CrossRef]

Nghiem, H. L.

Nioka, S.

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

B. Chance, S. Nioka, J. Kent, K. McCully, M. Fountain, R. Greenfeld, and G. Holtom, “Time-resolved spectroscopy of hemoglobin and myoglobin in resting and ischemic muscle,” Anal. Biochem. 174(2), 698–707 (1988).
[CrossRef] [PubMed]

Ntziachristos, V.

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
[CrossRef] [PubMed]

Paasschens, J. C. J.

J. C. J. Paasschens, “Solution of the time-dependent Boltzmann equation,” Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 56(1), 1135–1141 (1997).
[CrossRef]

Patterson, M. S.

B. C. Wilson and M. S. Patterson, “The physics, biophysics and technology of photodynamic therapy,” Phys. Med. Biol. 53(9), R61–R109 (2008).
[CrossRef] [PubMed]

M. S. Patterson, B. Chance, and B. C. Wilson, “Time Resolved Reflectance and Transmittance for the Noninvasive Measurement of Tissue Optical-Properties,” Appl. Opt. 28(12), 2331–2336 (1989).
[CrossRef] [PubMed]

S. T. Flock, M. S. Patterson, B. C. Wilson, and D. R. Wyman, “Monte Carlo Modeling of Light Propagation in Highly Scattering Tissue .1. Model Predictions and Comparison with Diffusion-Theory,” IEEE Trans. Biomed. Eng. 36(12), 1162–1168 (1989).
[CrossRef] [PubMed]

Peltié, P.

A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
[CrossRef] [PubMed]

Pifferi, A.

A. Pifferi, A. Torricelli, P. Taroni, D. Comelli, A. Bassi, and R. Cubeddu, “Fully automated time domain spectrometer for the absorption and scattering characterization of diffusive media,” Rev. Sci. Instrum. 78(5), 053103 (2007).
[CrossRef] [PubMed]

A. Pifferi, A. Torricelli, A. Bassi, P. Taroni, R. Cubeddu, H. Wabnitz, D. Grosenick, M. Möller, R. Macdonald, J. Swartling, T. Svensson, S. Andersson-Engels, R. L. van Veen, H. J. Sterenborg, J. M. Tualle, H. L. Nghiem, S. Avrillier, M. Whelan, and H. Stamm, “Performance assessment of photon migration instruments: the MEDPHOT protocol,” Appl. Opt. 44(11), 2104–2114 (2005).
[CrossRef] [PubMed]

L. Spinelli, A. Torricelli, A. Pifferi, P. Taroni, G. M. Danesini, and R. Cubeddu, “Bulk optical properties and tissue components in the female breast from multiwavelength time-resolved optical mammography,” J. Biomed. Opt. 9(6), 1137–1142 (2004).
[CrossRef] [PubMed]

R. Cubeddu, C. D’Andrea, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Effects of the menstrual cycle on the red and near-infrared optical properties of the human breast,” Photochem. Photobiol. 72(3), 383–391 (2000).
[PubMed]

R. Cubeddu, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Experimental test of theoretical models for time-resolved reflectance,” Med. Phys. 23(9), 1625–1633 (1996).
[CrossRef] [PubMed]

R. Cubeddu, M. Musolino, A. Pifferi, P. Taroni, and G. Valentini, “Time-Resolved Reflectance - a Systematic Study for Application to the Optical Characterization of Tissues,” IEEE J. Quantum Electron. 30(10), 2421–2430 (1994).
[CrossRef]

Ripoll, J.

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
[CrossRef] [PubMed]

Rizo, P.

A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
[CrossRef] [PubMed]

Rosen, M. A.

A. Corlu, R. Choe, T. Durduran, M. A. Rosen, M. Schweiger, S. R. Arridge, M. D. Schnall, and A. G. Yodh, “Three-dimensional in vivo fluorescence diffuse optical tomography of breast cancer in humans,” Opt. Express 15(11), 6696–6716 (2007).
[CrossRef] [PubMed]

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

Schnall, M. D.

Schweiger, M.

Smith, D. S.

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Spinelli, L.

L. Spinelli, A. Torricelli, A. Pifferi, P. Taroni, G. M. Danesini, and R. Cubeddu, “Bulk optical properties and tissue components in the female breast from multiwavelength time-resolved optical mammography,” J. Biomed. Opt. 9(6), 1137–1142 (2004).
[CrossRef] [PubMed]

Stamm, H.

Sterenborg, H. J.

Svanberg, K.

T. Svensson, E. Alerstam, M. Einarsdóttír, K. Svanberg, and S. Andersson-Engels, “Towards accurate in vivo spectroscopy of the human prostate,” J. Biophoton. 1(3), 200–203 (2008).
[CrossRef]

T. Svensson, S. Andersson-Engels, M. Einarsdóttír, and K. Svanberg, “In vivo optical characterization of human prostate tissue using near-infrared time-resolved spectroscopy,” J. Biomed. Opt. 12(1), 014022 (2007).
[CrossRef] [PubMed]

Svensson, T.

T. Svensson, E. Alerstam, M. Einarsdóttír, K. Svanberg, and S. Andersson-Engels, “Towards accurate in vivo spectroscopy of the human prostate,” J. Biophoton. 1(3), 200–203 (2008).
[CrossRef]

E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon migration,” J. Biomed. Opt. 13(4), 041304 (2008).
[CrossRef] [PubMed]

E. Alerstam, S. Andersson-Engels, and T. Svensson, “Improved accuracy in time-resolved diffuse reflectance spectroscopy,” Opt. Express 16(14), 10440–10454 (2008).
[CrossRef] [PubMed]

T. Svensson, S. Andersson-Engels, M. Einarsdóttír, and K. Svanberg, “In vivo optical characterization of human prostate tissue using near-infrared time-resolved spectroscopy,” J. Biomed. Opt. 12(1), 014022 (2007).
[CrossRef] [PubMed]

T. Svensson, J. Swartling, P. Taroni, A. Torricelli, P. Lindblom, C. Ingvar, and S. Andersson-Engels, “Characterization of normal breast tissue heterogeneity using time-resolved near-infrared spectroscopy,” Phys. Med. Biol. 50(11), 2559–2571 (2005).
[CrossRef] [PubMed]

A. Pifferi, A. Torricelli, A. Bassi, P. Taroni, R. Cubeddu, H. Wabnitz, D. Grosenick, M. Möller, R. Macdonald, J. Swartling, T. Svensson, S. Andersson-Engels, R. L. van Veen, H. J. Sterenborg, J. M. Tualle, H. L. Nghiem, S. Avrillier, M. Whelan, and H. Stamm, “Performance assessment of photon migration instruments: the MEDPHOT protocol,” Appl. Opt. 44(11), 2104–2114 (2005).
[CrossRef] [PubMed]

Swartling, J.

Taroni, P.

A. Pifferi, A. Torricelli, P. Taroni, D. Comelli, A. Bassi, and R. Cubeddu, “Fully automated time domain spectrometer for the absorption and scattering characterization of diffusive media,” Rev. Sci. Instrum. 78(5), 053103 (2007).
[CrossRef] [PubMed]

A. Pifferi, A. Torricelli, A. Bassi, P. Taroni, R. Cubeddu, H. Wabnitz, D. Grosenick, M. Möller, R. Macdonald, J. Swartling, T. Svensson, S. Andersson-Engels, R. L. van Veen, H. J. Sterenborg, J. M. Tualle, H. L. Nghiem, S. Avrillier, M. Whelan, and H. Stamm, “Performance assessment of photon migration instruments: the MEDPHOT protocol,” Appl. Opt. 44(11), 2104–2114 (2005).
[CrossRef] [PubMed]

T. Svensson, J. Swartling, P. Taroni, A. Torricelli, P. Lindblom, C. Ingvar, and S. Andersson-Engels, “Characterization of normal breast tissue heterogeneity using time-resolved near-infrared spectroscopy,” Phys. Med. Biol. 50(11), 2559–2571 (2005).
[CrossRef] [PubMed]

L. Spinelli, A. Torricelli, A. Pifferi, P. Taroni, G. M. Danesini, and R. Cubeddu, “Bulk optical properties and tissue components in the female breast from multiwavelength time-resolved optical mammography,” J. Biomed. Opt. 9(6), 1137–1142 (2004).
[CrossRef] [PubMed]

R. Cubeddu, C. D’Andrea, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Effects of the menstrual cycle on the red and near-infrared optical properties of the human breast,” Photochem. Photobiol. 72(3), 383–391 (2000).
[PubMed]

R. Cubeddu, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Experimental test of theoretical models for time-resolved reflectance,” Med. Phys. 23(9), 1625–1633 (1996).
[CrossRef] [PubMed]

R. Cubeddu, M. Musolino, A. Pifferi, P. Taroni, and G. Valentini, “Time-Resolved Reflectance - a Systematic Study for Application to the Optical Characterization of Tissues,” IEEE J. Quantum Electron. 30(10), 2421–2430 (1994).
[CrossRef]

Torricelli, A.

A. Pifferi, A. Torricelli, P. Taroni, D. Comelli, A. Bassi, and R. Cubeddu, “Fully automated time domain spectrometer for the absorption and scattering characterization of diffusive media,” Rev. Sci. Instrum. 78(5), 053103 (2007).
[CrossRef] [PubMed]

A. Pifferi, A. Torricelli, A. Bassi, P. Taroni, R. Cubeddu, H. Wabnitz, D. Grosenick, M. Möller, R. Macdonald, J. Swartling, T. Svensson, S. Andersson-Engels, R. L. van Veen, H. J. Sterenborg, J. M. Tualle, H. L. Nghiem, S. Avrillier, M. Whelan, and H. Stamm, “Performance assessment of photon migration instruments: the MEDPHOT protocol,” Appl. Opt. 44(11), 2104–2114 (2005).
[CrossRef] [PubMed]

T. Svensson, J. Swartling, P. Taroni, A. Torricelli, P. Lindblom, C. Ingvar, and S. Andersson-Engels, “Characterization of normal breast tissue heterogeneity using time-resolved near-infrared spectroscopy,” Phys. Med. Biol. 50(11), 2559–2571 (2005).
[CrossRef] [PubMed]

L. Spinelli, A. Torricelli, A. Pifferi, P. Taroni, G. M. Danesini, and R. Cubeddu, “Bulk optical properties and tissue components in the female breast from multiwavelength time-resolved optical mammography,” J. Biomed. Opt. 9(6), 1137–1142 (2004).
[CrossRef] [PubMed]

R. Cubeddu, C. D’Andrea, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Effects of the menstrual cycle on the red and near-infrared optical properties of the human breast,” Photochem. Photobiol. 72(3), 383–391 (2000).
[PubMed]

R. Cubeddu, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Experimental test of theoretical models for time-resolved reflectance,” Med. Phys. 23(9), 1625–1633 (1996).
[CrossRef] [PubMed]

Tualle, J. M.

Valentini, G.

R. Cubeddu, C. D’Andrea, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Effects of the menstrual cycle on the red and near-infrared optical properties of the human breast,” Photochem. Photobiol. 72(3), 383–391 (2000).
[PubMed]

R. Cubeddu, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Experimental test of theoretical models for time-resolved reflectance,” Med. Phys. 23(9), 1625–1633 (1996).
[CrossRef] [PubMed]

R. Cubeddu, M. Musolino, A. Pifferi, P. Taroni, and G. Valentini, “Time-Resolved Reflectance - a Systematic Study for Application to the Optical Characterization of Tissues,” IEEE J. Quantum Electron. 30(10), 2421–2430 (1994).
[CrossRef]

van der Zee, P.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of Optical Pathlength through Tissue from Direct Time of Flight Measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[CrossRef] [PubMed]

van Veen, R. L.

Wabnitz, H.

Wang, L. H.

L. H. Wang, S. L. Jacques, and L. Q. Zheng, “MCML-Monte Carlo Modeling of Light Transport in Multi-layered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
[CrossRef] [PubMed]

Wang, L. V.

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
[CrossRef] [PubMed]

Weissleder, R.

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
[CrossRef] [PubMed]

Whelan, M.

Wilson, B. C.

B. C. Wilson and M. S. Patterson, “The physics, biophysics and technology of photodynamic therapy,” Phys. Med. Biol. 53(9), R61–R109 (2008).
[CrossRef] [PubMed]

M. S. Patterson, B. Chance, and B. C. Wilson, “Time Resolved Reflectance and Transmittance for the Noninvasive Measurement of Tissue Optical-Properties,” Appl. Opt. 28(12), 2331–2336 (1989).
[CrossRef] [PubMed]

S. T. Flock, M. S. Patterson, B. C. Wilson, and D. R. Wyman, “Monte Carlo Modeling of Light Propagation in Highly Scattering Tissue .1. Model Predictions and Comparison with Diffusion-Theory,” IEEE Trans. Biomed. Eng. 36(12), 1162–1168 (1989).
[CrossRef] [PubMed]

B. C. Wilson and G. Adam, “A Monte Carlo model for the absorption and flux distributions of light in tissue,” Med. Phys. 10(6), 824–830 (1983).
[CrossRef] [PubMed]

Wray, S.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of Optical Pathlength through Tissue from Direct Time of Flight Measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[CrossRef] [PubMed]

Wyatt, J.

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of Optical Pathlength through Tissue from Direct Time of Flight Measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[CrossRef] [PubMed]

Wyman, D. R.

S. T. Flock, M. S. Patterson, B. C. Wilson, and D. R. Wyman, “Monte Carlo Modeling of Light Propagation in Highly Scattering Tissue .1. Model Predictions and Comparison with Diffusion-Theory,” IEEE Trans. Biomed. Eng. 36(12), 1162–1168 (1989).
[CrossRef] [PubMed]

Yodh, A. G.

A. Corlu, R. Choe, T. Durduran, M. A. Rosen, M. Schweiger, S. R. Arridge, M. D. Schnall, and A. G. Yodh, “Three-dimensional in vivo fluorescence diffuse optical tomography of breast cancer in humans,” Opt. Express 15(11), 6696–6716 (2007).
[CrossRef] [PubMed]

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

Yoo, K. M.

K. M. Yoo, F. Liu, and R. R. Alfano, “When Does the Diffusion Approximation Fail to Describe Photon Transport in Random Media?” Phys. Rev. Lett. 64(22), 2647–2650 (1990).
[CrossRef] [PubMed]

Yoshioka, H.

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Young, M.

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Zaccanti, G.

Zheng, L. Q.

L. H. Wang, S. L. Jacques, and L. Q. Zheng, “MCML-Monte Carlo Modeling of Light Transport in Multi-layered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
[CrossRef] [PubMed]

Anal. Biochem.

B. Chance, S. Nioka, J. Kent, K. McCully, M. Fountain, R. Greenfeld, and G. Holtom, “Time-resolved spectroscopy of hemoglobin and myoglobin in resting and ischemic muscle,” Anal. Biochem. 174(2), 698–707 (1988).
[CrossRef] [PubMed]

Appl. Opt.

Appl. Spectrosc.

Comput. Methods Programs Biomed.

L. H. Wang, S. L. Jacques, and L. Q. Zheng, “MCML-Monte Carlo Modeling of Light Transport in Multi-layered Tissues,” Comput. Methods Programs Biomed. 47(2), 131–146 (1995).
[CrossRef] [PubMed]

IEEE J. Quantum Electron.

R. Cubeddu, M. Musolino, A. Pifferi, P. Taroni, and G. Valentini, “Time-Resolved Reflectance - a Systematic Study for Application to the Optical Characterization of Tissues,” IEEE J. Quantum Electron. 30(10), 2421–2430 (1994).
[CrossRef]

IEEE J. Sel. Top. Quant.

V. Chernomordik, D. Hattery, I. Gannot, and A. H. Gandjbakhche, “Inverse method 3-D reconstruction of localized in vivo fluorescence - Application to Sjogren syndrome,” IEEE J. Sel. Top. Quant. 5(4), 930–935 (1999).
[CrossRef]

IEEE Trans. Biomed. Eng.

S. T. Flock, M. S. Patterson, B. C. Wilson, and D. R. Wyman, “Monte Carlo Modeling of Light Propagation in Highly Scattering Tissue .1. Model Predictions and Comparison with Diffusion-Theory,” IEEE Trans. Biomed. Eng. 36(12), 1162–1168 (1989).
[CrossRef] [PubMed]

S. L. Jacques, “Time-resolved reflectance spectroscopy in turbid tissues,” IEEE Trans. Biomed. Eng. 36(12), 1155–1161 (1989).
[CrossRef] [PubMed]

J. Biomed. Opt.

E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon migration,” J. Biomed. Opt. 13(4), 041304 (2008).
[CrossRef] [PubMed]

A. Liebert, H. Wabnitz, D. Grosenick, and R. Macdonald, “Fiber dispersion in time domain measurements compromising the accuracy of determination of optical properties of strongly scattering media,” J. Biomed. Opt. 8(3), 512–516 (2003).
[CrossRef] [PubMed]

T. Svensson, S. Andersson-Engels, M. Einarsdóttír, and K. Svanberg, “In vivo optical characterization of human prostate tissue using near-infrared time-resolved spectroscopy,” J. Biomed. Opt. 12(1), 014022 (2007).
[CrossRef] [PubMed]

A. Koenig, L. Hervé, V. Josserand, M. Berger, J. Boutet, A. Da Silva, J. M. Dinten, P. Peltié, J. L. Coll, and P. Rizo, “In vivo mice lung tumor follow-up with fluorescence diffuse optical tomography,” J. Biomed. Opt. 13(1), 011008 (2008).
[CrossRef] [PubMed]

L. Spinelli, A. Torricelli, A. Pifferi, P. Taroni, G. M. Danesini, and R. Cubeddu, “Bulk optical properties and tissue components in the female breast from multiwavelength time-resolved optical mammography,” J. Biomed. Opt. 9(6), 1137–1142 (2004).
[CrossRef] [PubMed]

J. Biophoton.

T. Svensson, E. Alerstam, M. Einarsdóttír, K. Svanberg, and S. Andersson-Engels, “Towards accurate in vivo spectroscopy of the human prostate,” J. Biophoton. 1(3), 200–203 (2008).
[CrossRef]

Med. Phys.

R. Choe, A. Corlu, K. Lee, T. Durduran, S. D. Konecky, M. Grosicka-Koptyra, S. R. Arridge, B. J. Czerniecki, D. L. Fraker, A. DeMichele, B. Chance, M. A. Rosen, and A. G. Yodh, “Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI,” Med. Phys. 32(4), 1128–1139 (2005).
[CrossRef] [PubMed]

R. Cubeddu, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Experimental test of theoretical models for time-resolved reflectance,” Med. Phys. 23(9), 1625–1633 (1996).
[CrossRef] [PubMed]

B. C. Wilson and G. Adam, “A Monte Carlo model for the absorption and flux distributions of light in tissue,” Med. Phys. 10(6), 824–830 (1983).
[CrossRef] [PubMed]

Nat. Biotechnol.

V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, “Looking and listening to light: the evolution of whole-body photonic imaging,” Nat. Biotechnol. 23(3), 313–320 (2005).
[CrossRef] [PubMed]

Opt. Express

Opt. Lett.

Photochem. Photobiol.

R. Cubeddu, C. D’Andrea, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Effects of the menstrual cycle on the red and near-infrared optical properties of the human breast,” Photochem. Photobiol. 72(3), 383–391 (2000).
[PubMed]

Phys. Med. Biol.

B. C. Wilson and M. S. Patterson, “The physics, biophysics and technology of photodynamic therapy,” Phys. Med. Biol. 53(9), R61–R109 (2008).
[CrossRef] [PubMed]

A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys. Med. Biol. 50(4), R1–R43 (2005).
[CrossRef] [PubMed]

T. Svensson, J. Swartling, P. Taroni, A. Torricelli, P. Lindblom, C. Ingvar, and S. Andersson-Engels, “Characterization of normal breast tissue heterogeneity using time-resolved near-infrared spectroscopy,” Phys. Med. Biol. 50(11), 2559–2571 (2005).
[CrossRef] [PubMed]

D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, “Estimation of Optical Pathlength through Tissue from Direct Time of Flight Measurement,” Phys. Med. Biol. 33(12), 1433–1442 (1988).
[CrossRef] [PubMed]

Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics

J. C. J. Paasschens, “Solution of the time-dependent Boltzmann equation,” Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 56(1), 1135–1141 (1997).
[CrossRef]

Phys. Rev. Lett.

K. M. Yoo, F. Liu, and R. R. Alfano, “When Does the Diffusion Approximation Fail to Describe Photon Transport in Random Media?” Phys. Rev. Lett. 64(22), 2647–2650 (1990).
[CrossRef] [PubMed]

Proc. Natl. Acad. Sci. U.S.A.

B. Chance, J. S. Leigh, H. Miyake, D. S. Smith, S. Nioka, R. Greenfeld, M. Finander, K. Kaufmann, W. Levy, M. Young, P. Cohen, H. Yoshioka, and R. Boretsky, “Comparison of Time-Resolved and -Unresolved Measurements of Deoxyhemoglobin in Brain,” Proc. Natl. Acad. Sci. U.S.A. 85(14), 4971–4975 (1988).
[CrossRef] [PubMed]

Rev. Sci. Instrum.

A. Pifferi, A. Torricelli, P. Taroni, D. Comelli, A. Bassi, and R. Cubeddu, “Fully automated time domain spectrometer for the absorption and scattering characterization of diffusive media,” Rev. Sci. Instrum. 78(5), 053103 (2007).
[CrossRef] [PubMed]

Other

V. V. Tuchin, Handbook of optical biomedical diagnostics. (2002).

J. R. Lakowicz, Principles of Fluorescence Spectroscopy, 3rd ed. (2006).

H. Buiteveld, J. H. M. Hakvoort, and M. Donze, “Optical properties of pure water,” Ocean Optics XII Proc. SPIE 2258, 174–183 (1994).

S. Prahl, “Monte Carlo Simulations,” http://omlc.ogi.edu/software/mc/ .

S. A. Prahl, M. Keijzer, S. L. Jacques, and A. J. Welch, “A Monte Carlo model of light propagation in tissue,” Dosimetry of Laser Radiation in Medicine and Biology - Proc. SPIE IS 5, 102–111 (1989).

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (10)

Fig. 1
Fig. 1

Experimental setup. Sync stands for synchronization. Fibers are separated by a distance r and are both at depth z.

Fig. 2
Fig. 2

Weighted residuals, (a) uncorrected and (b) corrected, as a function of time (scalable MC simulation, μs’=7cm−1, μa=0.03cm−1, fitted by the diffusion equation). The fitting range 95/1 has been extended to 1% of the trailing edge in order to better emphasize the effect of the correction.

Fig. 3
Fig. 3

(a) χR 2 as a function of acquired photons for two solutions, varying ink concentrations and with same scatterer concentration (μs=16cm−1). Linear fits are also plotted, emphasizing the behavior of χR 2 ; for each fit a typical R2 value of 0.98 and an intercept of 1.01 ± 0.04 are obtained. Each data point corresponds to a different experiment, except in the case of a high number of photons for which independent acquisitions were added. (b) Weighted residuals for increasing numbers of acquired photons when there is only little model bias (expected μa=0.1cm−1) and (c) worse model bias (expected μa=0.3cm−1).

Fig. 4
Fig. 4

(a) Estimated parameters, μa and μs, as a function of interfiber distance r. The corresponding calculated parameter χR-C 2 is plotted. (b) Measured temporal profile, together with the estimated diffusion profile in an infinite medium for r = 0.8cm. The associated profile of weighted residuals over the fitting zone is clearly not randomly distributed around zero, emphasizing how wrong this fit is.

Fig. 5
Fig. 5

Estimated parameters, μa and μs, as a function of Acronal concentration, for t2 taken at 0.5% of the maximum. The corresponding calculated parameter χR-C 2 is plotted. For comparison, the expected μa values provided by absorption spectrometer measurements (the error bar arises from μa estimation error of the stock solution, but is identical for each point), and the linear increase in μs were plotted assuming the value of the most concentrated solution to be correct. Number of time channels used for fitting are between T=425 for the smallest and T=700 for the strongest concentration of Acronal.

Fig. 6
Fig. 6

(a) Estimated parameters, μa and μs, as a function of ink concentration. The corresponding calculated parameter χRC2 is plotted. After the first three measurements, the χRC2 values deviate from their plateau, thus highlighting incorrect fit. Number of channels used for fitting are T=470 for the lowest and T=190 for the strongest ink concentration. (b) Comparison between measured temporal profiles and MC simulation convolved with the experimental IRF, for excepted μa=0.4cm−1 and μs'=14.2cm−1 ([ink]=70 arbitrary units). The circle emphasizes the unexpected behavior of the data.

Fig. 7
Fig. 7

(a) Estimation errors and the χR2 value are plotted as a function of absorption for μs’=7cm−1 (scalable MC). (b) The χR2 value as a function of N for MCa in the case of a prostate simulated medium. (c)(d) Weighted residuals corresponding to small and large number of photons of the Fig. 7(b) (note the WRes scale). Number of channels used for the fit is T=80.

Fig. 8
Fig. 8

Estimated parameters, μa and μs, as a function of fiber depth z for r = 1.7cm. The corresponding χRC2 parameter is plotted.

Fig. 9
Fig. 9

(a) Weighted residuals and χRC2 are plotted for both holders (same solution). The fitting range has been extended to 80/5 in order to include more early photons to evidence the failure of the model in the presence of the large metallic piece. (b) Pictures of the holders. The needle holder is used in all the other data presented in the article.

Fig. 10
Fig. 10

Estimated parameters, μa and μs, as a function of an artificial introduced time shift. The corresponding calculated parameter χRC2 is plotted. Each grid line corresponds to a 5% error. Note that for a reasonable r measurement error (less than 5 mm) during the IRF, the time shift is very small (less than 17 ps).

Tables (4)

Tables Icon

Table 1 Probability of finding a certain value of χR 2 or higher, as a function of T, number of degrees of liberty, due to random distribution of photons. Deviation from the values in this table is indicative that the model is false.

Tables Icon

Table 2 Effect of deconvolution by an incorrect IRF. PMT voltage during the experiment was 2.83 kV.

Tables Icon

Table 3 Estimates of the Acronal solution as a function of fiber holders and fitting range. The error introduced by the presence of the large metallic holder is also indicated.

Tables Icon

Table 4 Summary of the role of χRC2 for judging the accuracy of estimation together with typical situations encountered by experimenters. When not specified, this table refers to values of μa ~0.1cm−1 and μs~10cm−1. Significant increase of χRC2 means χRC2>2 (due to the large number of photons N used as a reference).

Equations (3)

Equations on this page are rendered with MathJax. Learn more.

mc(4πDc)3/2t3/2exp(r24Dctμact)
χR2=1Tpi(WRi)2=1Tpi(misisi)2
(misiσi)2=εi2σi2+2biσi2εi+bi2σi2εi2si+2bisiεi+bi2si

Metrics