Abstract

In most biomedical optical spectroscopy platforms, a fiber-probe consisting of single or multiple illumination and collection fibers was commonly used for the delivery of illuminating light and the collection of emitted light. Typically, the signals from all collection fibers were combined and then sampled to characterize tissue samples. Such simple averaged optical measurements may induce significant errors for in vivo tumor characterization, especially in longitudinal studies where the tumor size and location vary with tumor stages. In this study, we utilized the Monte Carlo technique to optimize the fiber-probe geometries of a spectroscopy platform to enable tumor-sensitive diffuse reflectance and fluorescence measurements on murine subcutaneous tissues with growing solid tumors that have different sizes and depths. Our data showed that depth-sensitive techniques offer improved sensitivity in tumor detection compared to the simple averaged approach in both reflectance and fluorescence measurements. Through the numerical studies, we optimized the source-detector distances, fiber diameters, and numerical apertures for sensitive measurement of small solid tumors with varying size and depth buried in murine subcutaneous tissues. Our study will advance the design of a fiber-probe in an optical spectroscopy system that can be used for longitudinal tumor metabolism and vasculature monitoring.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Optical spectroscopy can leverage endogenous contrast or be coupled with appropriate indicators to provide functional information while allowing for high-dynamic measures of the tissue metabolism and its associated vasculature in vivo [1,2]. For example, both oxygenated and deoxygenated hemoglobin have broadband optical absorption spectra [3], which have been extensively used to quantify vascular oxygenation and hemoglobin concentration [4] with the use of diffuse reflectance spectroscopy techniques [5]. On the other hand, autofluorescence spectroscopy has been explored heavily to quantify tissue reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) [6,7] as an effective means to provide insights into the reduction-oxidation (redox) state to indicate the tissue metabolism status [8]. To quantify tumor metabolism directly and explicitly, exogenous fluorescence metabolic probes have been explored for in vivo tumor metabolism monitoring. For instance, we have developed novel techniques to quantify glucose uptake using glucose analog 2-NBDG [9] and mitochondrial membrane potential using TMRE [10] in tumor models in vivo [2,11]. The combination of diffuse reflectance and fluorescence spectroscopy has been reported frequently to quantify both tissue metabolism and vascular microenvironment in vivo in biological models [2,8,11].

In a typical optical spectroscopy setup, a fiber-probe consisting of single or multiple illumination and collection fibers was commonly used for the delivery of illuminating light and the collection of emitted light [12]. The fibers with small source-detector distances (SD) probe the superficial tissue layer while the fibers with large SD distances sense the deep tissue region [13,14]. To maximize the collected optical signal, the light intensities from all collection fibers were commonly combined and then sampled by an optical spectrometer [2]. However, such simple averaged-volume optical measurements may induce significant errors for in vivo tumor characterization as the superficial tissue layer over the tumor region could largely affect the optical tumor sensing capability. To increase the optical probing capability of a more deep-seated and larger target, larger SD was usually recommended in a diffuse reflectance setup with a sacrifice of collected optical signal strength [1217]. This well-established guideline has been commonly adapted to optimize the fiber probe design for the detection of advanced tumors in different tissue models using diffuse reflectance spectroscopy. For example, Hennessy et al. [14] conducted a Monte Carlo study to demonstrate that the sampling depth of visible diffuse reflectance spectroscopy in a layered skin tumor model was around half of the SD. More recently, Greening et al. [13] conducted both phantom studies and in vivo animal studies to show that the sampling depth of visible diffuse reflectance spectroscopy in skin tissue was around half of the SD and the largest SD of 4 mm was examined. For the characterization of a semi-infinite or infinite target, the general guideline of increasing SD to maximize the sensing depth worked well as long as sufficient signals could be acquired. However, it may induce significant errors for early-stage tumor characterization, especially in longitudinal studies where the tumor size and depth vary with tumor stages, and a very early-stage tumor might be too small to be considered as a semi-infinite or infinite layer.

In our current study, we aimed to identify the most sensitive and practical probe design for a specific novel important application, i.e. in vivo optical characterization of a growing solid tumor in murine subcutaneous tumor models for longitudinal study in which the tumor size varies from tiny, to small, and to the middle. In vivo optical longitudinal monitoring of tumor biology becomes critical for translational cancer studies because it has the great potential to provide dynamic, quantitative measurements of both metabolism and the associated vascular endpoints of solid tumors under a variety of conditions in vivo. In addition to the examination of optimal SD values, we also investigated other key fiber parameters including fiber diameters and numerical apertures (NA) so that one may achieve high tumor detection sensitivity with decent signal levels for tumors at different stages. Moreover, we combined fluorescence and diffuse reflectance measurement in one platform, thus, one may identify the fiber probe design that works for the two types of measurements, enabling one quantify tumor metabolism and vasculature simultaneously on the same tissue region. To identify the optimal fiber geometries for tumor-sensitive diffuse reflectance and fluorescence measurement on murine subcutaneous tissue with growing solid tumors, our previously reported Monte Carlo code [18,19] was used to simulate both diffuse reflectance and fluorescence in a layered tissue model with a buried tumor-like target. Our study showed that the most tumor-sensitive SD varied with subcutaneous tumor stages. Specifically, the optimal SD distances for tiny subcutaneous tumors (< 3mm) were found to be ∼1.5 to 2.0 mm, while the best SD for small subcutaneous tumors (<6 mm) were found to be ∼2.5 to 3.0 mm, and the SD for middle subcutaneous tumors (>6 mm) were found to be ∼3. 0 mm or larger. Our data also showed that neither fiber diameters nor NA affect the tumor detection sensitivity when an optimal SD was used; however, both fiber diameter and fiber NA could significantly affect the collected signal strength. Our findings in this study will advance the design of tumor-sensitive fiber-probes to be used in an optical spectroscopy platform for longitudinal diffuse reflectance and fluorescence measurements on murine flank solid tumors.

2. Materials and methods

2.1 Monte Carlo method and fiber probe geometry

A Monte Carlo code previously developed by us [18,19] was modified to simulate both diffuse reflectance and fluorescence in a layered tissue model with a buried tumor-like target. A spherical target with a specified radius and position was used to mimic an early stage solid tumor. The details and validation of the code have been reported elsewhere [20]. Twenty million photons were launched in all simulations as described below.

To compare the tumor detection sensitivities of depth-sensitive measurements and averaged spectroscopy measurements, the simple fiber-probe configuration with multiple SD distances as shown in Fig. 1 was examined. As illustrated in Fig. 1(a), the SD values were varied from 0.5 mm to 3.0 mm with an increment of 0.5 mm. A small SD was expected to probe the superficial tissue layer while a large SD is expected to sense the deep tissue region [13,14]. The summed value of the light intensities collected from all six collection fibers represent the averaged optical measurement, which was commonly used in a typical optical spectroscopy platform [2,11,21]. The values of the light intensity simulated from each of the specific collection fibers with different SD refer to the depth-sensitive optical measurements. Both illumination and collection fibers were perpendicular to the tissue surface as shown in Fig. 1(b). When the SD varied from 0.5 mm to 3.0 mm, the diameters of all fibers were fixed at 0.2 mm and the NA was set to be 0.22 in the simulations otherwise specified. The refractive indices of all fibers were set to 1.47.

 

Fig. 1. (a) Probe configurations with one source fiber and six detector fibers. The source and detector distances (SD) were set to 0.5 mm, 1.0 mm, 1.5 mm, 2.0 mm, 2.5 mm, and 3.0 mm respectively. (b) The tilt angles of both illumination and collection fibers were 0 degree relative to the normal axis of the tissue surface. The two cylinders in both sets represent the source and detector fibers and the arrows indicate the direction of light propagation.

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In addition to the examination of SD described above, we further investigated fiber diameter and NA for sensitive detection of a tiny tumor or a small tumor with a fixed tumor diameter. Note that the SD was fixed to be the optimized value for the finite small tumor models when different fiber diameters or NA values were studied. Specifically, the fiber diameters were varied from 0.1 mm, 0.2 mm, 0.4 mm, and 0.6 mm whenever it was practical to be implemented for a given SD, the fiber NA values were set to be 0.15, 0.22, 0.39, and 0.45 respectively.

2.2 Murine subcutaneous tumor models

In most existing tissue optical spectroscopy measurements, the sample was typically assumed to be a semi-infinite, homogenous tissue layer for data processing [2,11,21]. In order to mimic a practical optical spectroscopy measurement on skin tissue with a murine subcutaneous tumor, the simple homogenous layered tissue models as illustrated in Fig. 2(a) were used to simulate skin with advanced murine subcutaneous tumors. To simulate small tumors in a longitudinal tumor growth monitoring study, a finite tumor model shown in Fig. 2(b) consisting of a normal semi-infinite skin layer with a buried tumor-like spherical target was used. To ensure the fluorescence excitation light was always delivered to the central mass of the tumor, the center of the illumination fiber always overlapped with the vertical middle line of a spherical tumor target. In both the semi-infinite tumor model and finite tumor model, the tumor depth was set to be 0.7 mm or 1.0 mm according to previously reported murine skin thickness and subcutaneous tumor depths [13]. In the finite tumor model, the tumor diameters were varied based on their stages including a tiny tumor (<3 mm), a small tumor (<6 mm and >3 mm), and a middle tumor (> 6 mm) [22]. In all tissue models, the total thickness of the tissue was set to 15 mm.

 

Fig. 2. (a) Semi-infinite layered tumor model to simulate advanced murine subcutaneous tumors. The normal skin thickness was set to be 0.7 mm or 1.0 mm. (b) Finite spherical tumor model to simulate small murine subcutaneous tumors. The tumor depth (distance between the tumor surface and the skin surface) was set to be 1.0 mm.

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The tissue optical properties including the absorption coefficient, scattering coefficient, and anisotropy used in our simulations were taken from previously published literatures [11,23,24] and listed in Table 1. A refractive index of 1.4 was used in both normal tissue and tumor tissue [23]. For diffuse reflectance measurement, the wavelength of 550 nm was used because: (1) It is within the typical light band for the extraction of vascular parameters; and (2) it is the excitation peak for the Tetramethylrhodamine ethyl ester (TMRE), a mitochondrial membrane potential fluorescence probe that we are interested in for our fluorescence measurement. For fluorescence simulations, the 550 nm light was used for excitation while the 585 nm light was used for emission given that the TMRE emission peak is around 585 nm. The quantum yield values for tumor region and normal tissue area were set to be 0.5 and 0.3 respectively according to our previous in vivo preclinical study in which we found that breast tumors have increased TMRE uptake compared to normal tissue [2].

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Table 1. Optical properties of normal skin tissue and murine subcutaneous tumor a

2.3 Data analysis

The simulated diffuse reflectance intensities at 550 nm and fluorescence intensities at 585 nm were used to represent the collected optical signals. Because of the general interest in tumor detection, the tumor contrast was introduced to evaluate the optical probing sensitivity to a tumor. The tumor contrast of reflectance (TCR) was defined as the percent deviation for weighted photon visiting frequency (WVF) which was calculated based on Eq. (1).

$$T{C_R} = \frac{{WV{F_{tumor}}}}{{WV{F_{tumor}} + WV{F_{normal}}}}$$

The WVFtumor refered to the weighted photon visiting frequency in the tumor region, while the WVFnormal refered to the weighted photon visiting frequency in the normal tissue region. As reported previously [16], the WVF refered to the number of times that photons visit a region divided by the total attenuation coefficient at a given region. The WVF based TCR reflects the percentage of tumor region contribution to the total detected diffuse reflectance signals, thereby representing the tumor detection sensitivity. The tumor contrast of fluorescence (TCF) was defined as the percent deviation for detected fluorescence generated from the tumor region to the total detected fluorescence generated from the entire tissue model which was calculated based on Eq. (2).

$$T{C_F} = \frac{{{F_{tumor}}}}{{{F_{tumor}} + {F_{normal}}}}$$

The Ftumor refered to the fiber detected fluorescence signal contributed by the tumor region, while the Fnormal refered to the fiber detected fluorescence contributed by normal tissue region. A higher tumor contrast refered to a higher optical probe sensitivity to tumors as it represented that more signals from a tumor region could be captured [18]. Each simulation was repeated four times to generate error bars.

To quantify the optical sensing capability, the interrogation depth decribed previously [15] was used. The interrogation depth was the max depth that the detected photons can reach, which was approximately two times of the optical sensing depth which was defined as the depth that at least 50% photons can reach [13,14]. The interrogation depth of diffuse reflectance was obtained by examining the WVF distribution along the Z-axis direction [16], and interrogation depth of fluorescence was found by examining the function of surface measured fluorescence with the occurring position along the Z-axis introduced previously [25].

3. Results

3.1 Depth-resolved diffuse reflectance and fluorescence measurements provided improved tumor detection sensitivity compared to averaged optical measurements

Figure 3 showed the distribution of WVF and the distribution of fluorescence frequency along the Z-axis at different SD values for the semi-infinite tumor model [Fig. 2(a)] with a tumor depth of 0.7 mm and 1.0 mm, respectively. For both tumor models, the WVF distribution and fluorescence frequency distribution showed that the optical interrogation depth was increased when SD was increased. Generally, the diffuse optical interrogation depth can reach up to ∼2 mm when the SD was equal to or larger than 1.5 mm as evidenced by the considerable visiting frequency in the depth range of ∼2 mm shown in Figs. 3(a) and 3(c). Similarly, the fluorescence interrogation depth may reach up to 3.0 mm when the SD was equal or larger than 1.5 mm as shown in Figs. 3(b) and 3(d).

 

Fig. 3. WVF (refelctance) and fluorescenc frequency (fluorescence) simulated from semi-infinite tumor models with a tumor depth of 0.7 mm and 1.0 mm. WVF (a) and weighted fluorescence frequency (b) simulated at different SD for tumor model with a tumor depth of 0.7 mm. WVF (c) and weighted fluorescence frequency (d) simulated at different SD for tumor model with a tumor depth of 1.0 mm. The SD values were varied from 0.5 mm to 3.0 mm. In all simulations, the fiber diameter was fixed to be 0.2 mm and the NA was fixed to be 0.22.

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Figures 4(a)–4(b) showed TCR and TCF obtained from depth-resolved (dashed line) and averaged (solid line) measurements on semi-infinite tumor models with a tumor depth of 0.7 mm (thick line) and 1.0 mm (thin line), respectively. As introduced previously, the TCR was calculated based on the WVF, and TCF was obtained based on the fluorescence frequency. In both semi-infinite tumor models, both TCR and TCF were increased significantly when the SD was increased from 0.5 mm to 3.0 mm. TCR and TCF values obtained from the tumor models with a tumor depth of 0.7 mm were always higher than those from tumor models with a tumor depth of 1.0 mm. Figures 4(c)–4(d) showed simulated diffuse reflectance and fluorescence intensities from the tumor models with the corresponding SD values. Figures 4(e)–4(f) showed the log scale of simulated diffuse reflectance and fluorescence intensities as that in Figs. 4(c)–4(d). Both diffuse reflectance and fluorescence intensities dropped when SD was increased. Diffuse reflectance intensities from a tumor model with a tumor depth of 0.7 mm were generally lower than those from a tumor model with a tumor depth of 1.0 mm. In contrast, fluorescence intensities from a tumor model with smaller tumor depth were generally higher than that from a tumor model with larger tumor depth. All error bars in the curves were too small to see.

 

Fig. 4. Simulated tumor contrast and light intensities (diffuse reflectance and fluorescence) from semi-infinite tumor models with a tumor depth of 0.7 mm (thick line) and 1.0 mm (thin line). (a) TCR and (b) TCF simluated from depth-resoloved measurements and that from averaged optical measurement. The total sum value of the light intensities simulated from all six collection fibers represent the averaged optical measurement (solid). The values of the light intensity simulated from each of the specific fibers with different SD refer to the depth-resolved optical measurements (dashed). (c) Diffuse reflectance intensities and (d) fluorescence intensities simulated at different SD values. (e) log scale of diffuse refelctance intensities and (f) log scale of fluorescence intensities at different SD values.

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3.2 Tumor-sensitive source-detector distances varied with tumor stages for both diffuse reflectance and fluorescence measurements

Figure 5 showed the WVF distribution and fluorescence frequency simulated from the finite tumor models with a spherical tumor that has different diameters. Both the WVF distribution and fluorescence frequency distribution showed that the optical interrogation depth was increased when SD was increased. Generally, the diffuse optical interrogation depth can reach up to 2 mm when the SD is equal or larger than 1.5 mm. In contrast, the fluorescence optical sensing depth may reach up to 3 mm when the SD is equal or larger than 1.5 mm. Interestingly, the fluorescence frequency distribution showed that the secondary peaks were likely caused by the tumor target that generated abundant fluorescence signals.

 

Fig. 5. Simulated WVF (a-f)) and weighted fluorescence frequency (h-l) for a finite tumor model with a tumor dimater of 1 mm, 2 mm, 3 mm, 5 mm, 6 mm, and 8 mm. The tumor depth was fixed to be 1 mm.

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Figure 6 showed TCR and TCF simulated from the finite tumor models with a spherical tumor that had a diameter of 1 mm, 2 mm, 3 mm, 5 mm, 6 mm, and 8 mm respectively. Figure 6(a) showed that the TCR did not change much when SD was changed if the tumor diameter was 1 mm. However, Figs. 6(b)–6(c) showed that the TCR for the 2-mm tumor model or 3-mm tumor model reached the maximum and then decreased when the SD was increased to certain thresholds. Figures 6(d)–6(f) showed that the TCR for small and middle tumors (>3 mm) was increased when SD was increased from 0.5 to 3 mm. Figure 6(g) showed that the TCF did not change much if the tumor diameter was 1 mm irrespective of any SD. In contrast, Figs. 6(h)–6(l) showed that the TCF was increased when SD was increased, and then reached their maximum when the SD reached a certain threshold.

 

Fig. 6. TCR (a-f) and TCF (g-l) for a finite tumor model with a tumor dimater of 1 mm, 2 mm, 3 mm, 5 mm, 6 mm, and 8 mm. The tumor depth was fixed to be 1 mm.

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Based on Fig. 6, the optimal SD values that yielded the highest optical tumor contrast for detection of tiny tumors, small tumors, and middle tumors respectively were summarized in Table 2. Specifically, a SD of 1.5∼2.0 mm was recommended for diffuse reflectance and fluorescence measurement on tiny tumors, while a SD of 2.5∼3.0 mm was recommended for measurement on small tumors or middle tumors. For the 1-mm diameter tumor, the tumor contrasts were always less than 3%, which suggested that the tumor was too small to be detected by either diffuse reflectance or fluorescence spectroscopy.

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Table 2. Rule of thumb SD values for optical measurements on the infinite tumor models

3.3 Size and numerical aperture of fibers did not affect the detection sensitivity of small tumors, but did affect the collected optical signal strength

Figure 7 showed the effect of fiber size on the simulated tumor contrast and collected optical signals when the optimal SD of 3 mm was used for detection of a tumor with a diameter of 6-mm. Figures 7(a)–7(d) showed that the source fiber size did not affect the tumor contrast nor collected diffuse reflectance intensities and fluorescence intensities. Figures 7(e)–7(f) showed that the detector fiber size did not affect the tumor contrast, but it did affect the collected diffuse reflectance intensities and fluorescence intensities. Specifically, the larger detector diameters yielded increased diffuse reflectance and fluorescence intensities. When the detection fiber diameter was increased from 0.1 mm to 0.6 mm, both the collected diffuse reflectance intensities and the collected fluorescence intensities were increased by 40 folds.

 

Fig. 7. The effect of fiber diameter on tumor contrast and collected diffuse refelctance and fluorescence intensities. TCR (a), TCF (b), diffuse refelctance intensity (c), and fluorescence intensity (d) simulated with different source fiber diameters. The detector fiber diameter was fixed to be 0.2 mm in (a-d). TCR (e), TCF (f), diffuse refelctance intensity (g), and fluorescence intensity (h) simulated with different detector fiber diameters. The source fiber diameter was fixed to be 0.2 mm in (e-h). The NA of all fibers was fixed to be 0.22. The tumor diameter was 6 mm, and the top layer thickness was 1 mm. The SD was fixed to be 3 mm.

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Figure 8 showed the effect of fiber NA on the tumor contrast and collected optical signals. Figures 8(a)–8(d) showed that the source fiber NA did not affect the tumor contrast nor collected intensities of diffuse reflectance and fluorescence. Figures 8(e)–8(f) showed that the detector fiber NA did not affect the tumor contrast, but it affected the collected intensities of diffuse reflectance and fluorescence. Specifically, when the detector fiber NA was increased from 0.19 to 0.45, both the diffuse reflectance and fluorescence intensities were increased by 6 folds.

 

Fig. 8. The effect of fiber numerical apeture (NA) on tumor contrast and collected diffuse refelctance and fluorescence intensities. TCR (a), TCF (b), diffuse refelctance intensity (c), and fluorescence intensity (d) simulated with different source fiber NA. The detector fiber NA was fixed to be 0.22 in (a-d). TCR (e), TCF (f), diffuse refelctance intensity (g), and fluorescence intensity (h) simulated with different detector fiber diameters. The source fiber NA was fixed to be 0.22 in (e-h). The diameter of all fibers was fixed to be 0.2 mm. The tumor diameter was 6 mm, and the top layer thickness was 1 mm. The SD was fixed to be 3 mm.

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4. Discussion

Optical spectroscopy has the great potential to serve as an important tool for translational cancer studies. The commonly used average spectroscopy measurement [2] could result in low tumor detection sensitivity for small growing tumors. Therefore, depth-resolved measurement was necessitated in order to achieve improved tumor detection sensitivity. Large SD distance offers deeper sensing depth as reported previously [26,27]. Greening et al. conducted in vivo diffuse reflectance studyies on mice and found that SD over 2 mm could distinguish a significant difference in oxygen saturation between normal and small solid tumor (diameter is ∼ 6 mm) [13]. In this work, we examined the SD distances for small growing tumors (diameter varied from 1 mm to 8 mm) detection using both diffuse reflectance and fluorescence measurement.

Our simulations on semi-infinite layer tumor models showed that the large SD distances yielded higher tumor contrast but with lower signals for both diffuse reflectance and fluorescence measurement, which can be well explained by diffusion theory. A larger SD will lead to a deeper sensing depth within the semi-infinite tumor layer, which will monotonically increase the tumor contrast. Compared with depth-resolved measurements, the averaged measurement will display a compressed tumor contrast as it is a superposition of the all individual tumor contrasts within the combination. When the top layer becomes thicker, less percentage of the WVF and fluorescence frequency from tumor region will be collected, thus the overall tumor contrast will drop. This indicates that in a longitudinal study where the top layer thickness increases as the mouse grows, the performance of the averaged measurement will drop but the depth-resolved measurement may be able to provide flexibility to adapt to this change.

Our investigations on finite tumor models showed that a tiny tumor with a diameter of 1 mm might be too small to be detected by either diffuse reflectance or fluorescence. However, once the tumor diameters reached over 2 mm, a max tumor contrast can be achieved by adjusting the SD values. The tumor contrast from diffuse reflectance measurements on tiny tumors displayed a trapezoid pattern as SD distance increased. This trapezoid pattern suggested that a larger SD did not necessarily provide best tumor detection contrast for growing tiny tumors. TCR was increased with SD when tumor diameters are larger than 3 mm, while TCF was increased with SD when tumor diameters are larger than 2 mm when SD was increased from 0.5 mm to 3 mm. We observed that the TCR was always increased with SD for the small or middle tumor models (>5 mm). For these small or middle tumors models, the tumor diameter was larger than the SD values and the tumor can be treated as a semi-infinite layer for diffuse reflectance spectroscopy according to our former study [28]. In the semi-infinite tumor models, the ratio of reflectance signals from tumor region to that from non-tumor would be always increased with SD thereby the TCR was always increased with SD as shown in Fig. 9(a). However, if the tumor diameter was comparable or smaller than SD, the ratio of reflectance signals from tumor region to that from non-tumor may not be always increased with SD due to the tumor boundary effect as illustrated in Fig. 9(b). Because of this, it can be expected that the TCR would increase first and then decrease when the SD was increased from 1 mm to 3 mm for the tiny tumors with a diameter of 2 mm or 3 mm. In contrast, TCF was always increased with SD even for the tiny tumors, this was likely because the excited fluorescence was isotropic thus the tumor boundary effect was not obvious. This phenomenon further highlighted the importance of proper fiber geometry design for tiny tumor measurement in a longitudinal study.

 

Fig. 9. Illustration of potential photon travel paths in layered tumor model (a) and tiny spherical tumor model (b) for different fiber SD values. D1 represent a detection fiber that has a small SD, while D3 represent a detection fiber that has a large SD.

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Fluorescence frequency distribution shown in Figs. 5(g)–5(l) showed a second peak in the depth region of 1-2 mm. This feature became more visible for larger tumor size and larger SD distance. This second peak was mainly caused by the sudden change of tissue optical properties, especially fluorescence properties at the spherical interface between normal tissue and tumor target. In contrast, the WVF distribution shown in Figs. 5(a)–5(f) did not display a remarkable second peak as that observed in fluorescence distribution. This was likely because the change of optical properties at the spherical interface only involved nonsignificant change of absorption coefficient and scattering coefficient. The above comparison also suggested that fluorescence measurement might be more sensitive for tumor detection compared to diffuse reflectance measurement.

Small tumors in mice with a diameter of ∼ 6 mm were commonly recommended for in vivo tumor metabolism characterization because these small tumors may retain the actual clinically relevant tumor micro-environment well, which was typically hypoxic but not yet starting to necrotize [22]. From the optical measurement perspective, small tumors with a dimeter of ∼ 6 mm were relatively easy to measure with an optical spectroscopy system. However, increasing evidence showed that the tumor metabolism and vasculature varied with tumor stages [2,22], thus it would be critical to be able to longitudinally monitor tumor metabolism and vasculature to comprehensively understand the tumor biology. Thanks to the advance of sensitive optical detectors and novel fiber probes, it was feasible to use optical spectroscopy to characterize tumors with smaller diameters. Our former study [2] demonstrated that the optical spectroscopy with a custom designed fiber probe could be sensitive enough to measure tiny tumors with a diameter of ∼3 mm. In our current study, the tumors were assumed to be spherical to simplify the simulations. The actual tumors might be not spherical when the tumors are either in early stage or advanced stage. However, we anticipated that the shape of tumor will minimally affect the trends of tumor contrast yielded from the current study irrespective of spherical or non-spherical tumors were used. This is because the tumor contrast value was mainly determined by the ratio of signals contributed from entire tumor region to that from the non-tumor region, which is primarily relying on the optically probed tumor volume rather than its shape.

Our numerical studies suggested a set of optimal SD distances as summarized in Table 2, in order to achieve the best tumor detection sensitivity for a longitudinal cancer study where the tumor size varied from tiny (< 3 mm), to small (3– 6 mm), and to middle (> 6 mm). For a given tumor with a fixed size, we further optimized fiber diameter and NA when the optimal SD was used. Both tumor models with a 6-mm diameter small tumor (Figs. 7 and 8) and a 3-mm diameter tiny tumor (data not shown) were studied. It was interesting to notice that neither TCR nor TCF change much with fiber diameters. This was likely because the diffuse reflectance or fluorescence intensities could be scaled with baseline when the fiber diameters were changed, and the fiber diameter would influence both the baseline intensity and the tumor-sensed intensity of light. Our data showed that fiber diameters and NA value did not affect tumor contrast but did affect the collected diffuse reflectance and fluorescence intensities. Generally, larger diameter and NA of detection fiber but not illumination fiber yielded significantly increased optical signal. Along with optimal SD distances, the appropriate selection of detector fiber diameter and NA could be of significant value in improving signal-to-noise ratio during diffuse reflectance and fluorescence measurement, especially when detecting small tumors which yields lower tumor contrast. The practical application of our findings in this study is to provide valuable guidance to the probe design for diffuse and fluorescence measurement in longitudinal monitoring of murine subcutaneous tumors. For example, linearly arranged fiber probe consisting of one source fiber and three detection fibers (SD equals 1.5 mm, 2.5 mm and 3 mm), which are trifurcated and connected to one spectrophotometer, is suitable to detect solid tumors ranging from ∼2 mm to ∼8 mm. To obtain a stronger signal intensity and larger lateral sampling volume, a more advanced probe design that contains three rings of detection fibers concentrically arranged around several central source fibers can be fabricated to enable tumor-sensitive diffuse reflectance and fluorescence measurement in an in vivo longitudinal tumor monitoring study.

5. Conclusion

We numerically investigated the effect of fiber probe geometries to diffuse reflectance and fluorescence measurements on murine subcutaneous tissues with growing solid tumors that had diameters ranging from 1mm to 8mm. Our study showed that the most tumor-sensitive SD vary with subcutaneous tumor stages. The study also showed that neither fiber diameters nor NA would affect the tumor detection sensitivity when optimal SD distances were used; however, both fiber diameter and fiber NA could significantly affect the collected signal strength. Our results will advance the design of tumor-sensitive fiber-probes that could be adapted by a diffuse reflectance and fluorescence spectroscopy platform for in vivo longitudinal monitoring of tumor metabolism and vascularization.

Funding

University of Kentucky (Startup); National Institute of General Medical Sciences (P20GM121327).

Acknowledgments

We acknowledge the support from the computational facilities provided by the High-Performance Computing Center of the University of Kentucky.

Disclosures

The authors declare that there are no known conflicts of interest related to this article. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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9. C. Zhu, A. F. Martinez, H. L. Martin, M. Li, B. T. Crouch, D. A. Carlson, T. A. J. Haystead, and N. Ramanujam, “Near-simultaneous intravital microscopy of glucose uptake and mitochondrial membrane potential, key endpoints that reflect major metabolic axes in cancer,” Sci. Rep. 7(1), 13772 (2017). [CrossRef]  

10. A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018). [CrossRef]  

11. C. Zhu, Hannah L. Martin, Brian T. Crouch, Amy F. Martinez, Martin Li, Gregory M. Palmer, Mark W. Dewhirst, and N. Ramanujam, “Near-simultaneous quantification of glucose uptake, mitochondrial membrane potential, and vascular parameters in murine flank tumors using quantitative diffuse reflectance and fluorescence spectroscopy,” Biomed. Opt. Express 9(7), 3399–3412 (2018). [CrossRef]  

12. U. Utzinger and R. R. Richards-Kortum, “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt. 8(1), 121–147 (2003). [CrossRef]  

13. G. Greening, A. Mundo, N. Rajaram, and T. J. Muldoon, “Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts,” J. Biomed. Opt. 23(08), 1–14 (2018). [CrossRef]  

14. R. Hennessy, W. Goth, M. Sharma, M. K. Markey, and J. W. Tunnell, “Effect of probe geometry and optical properties on the sampling depth for diffuse reflectance spectroscopy,” J. Biomed. Opt. 19(10), 107002 (2014). [CrossRef]  

15. S. H. Tseng, C. K. Hayakawa, J. Spanier, and A. J. Durkin, “Determination of optical properties of superficial volumes of layered tissue phantoms,” IEEE Trans. Biomed. Eng. 55(1), 335–339 (2008). [CrossRef]  

16. Q. Liu and N. Ramanujam, “Sequential estimation of optical properties of a two-layered epithelial tissue model from depth-resolved ultraviolet-visible diffuse reflectance spectra,” Appl. Opt. 45(19), 4776–4790 (2006). [CrossRef]  

17. T. Papaioannou, N. W. Preyer, Q. Fang, A. Brightwell, M. Carnohan, G. Cottone, R. Ross, L. R. Jones, and L. Marcu, “Effects of fiber-optic probe design and probe-to-target distance on diffuse reflectance measurements of turbid media: an experimental and computational study at 337 nm,” Appl. Opt. 43(14), 2846–2860 (2004). [CrossRef]  

18. C. Zhu and Q. Liu, “Numerical investigation of lens based setup for depth sensitive diffuse reflectance measurements in an epithelial cancer model,” Opt. Express 20(28), 29807–29822 (2012). [CrossRef]  

19. Y. H. Ong, C. Zhu, and Q. Liu, “Phantom validation of Monte Carlo modeling for noncontact depth sensitive fluorescence measurements in an epithelial tissue model,” J. Biomed. Opt. 19(8), 085006 (2014). [CrossRef]  

20. C. Zhu and Q. Liu, “Validity of the semi-infinite tumor model in tissue optics: a Monte Carlo study,” in 2010 Photonics Global Conference (PGC) (IEEE2010), pp. 1–4.

21. N. Rajaram, A. F. Reesor, C. S. Mulvey, A. E. Frees, and N. Ramanujam, “Non-invasive, simultaneous quantification of vascular oxygenation and glucose uptake in tissue,” PLoS One 10(1), e0117132 (2015). [CrossRef]  

22. I. Serganova, A. Rizwan, X. Ni, S. B. Thakur, J. Vider, J. Russell, R. Blasberg, and J. A. Koutcher, “Metabolic imaging: a link between lactate dehydrogenase A, lactate, and tumor phenotype,” Clin. Cancer Res. 17(19), 6250–6261 (2011). [CrossRef]  

23. S. K. Chang, D. Arifler, R. Drezek, M. Follen, and R. Richards-Kortum, “Analytical model to describe fluorescence spectra of normal and preneoplastic epithelial tissue: comparison with Monte Carlo simulations and clinical measurements,” J. Biomed. Opt. 9(3), 511–522 (2004). [CrossRef]  

24. C. P. Sabino, A. M. Deana, T. M. Yoshimura, D. F. da Silva, C. M. Franca, M. R. Hamblin, and M. S. Ribeiro, “The optical properties of mouse skin in the visible and near infrared spectral regions,” J. Photochem. Photobiol., B 160, 72–78 (2016). [CrossRef]  

25. Q. Liu, C. Zhu, and N. Ramanujam, “Experimental validation of Monte Carlo modeling of fluorescence in tissues in the UV-visible spectrum,” J. Biomed. Opt. 8(2), 223–236 (2003). [CrossRef]  

26. L. V. Wang and H.-I. Wu, Biomedical Optics: Principles and Imaging (John Wiley & Sons, 2012).

27. P. Farzam, “Hybrid diffuse optics for monitoring of tissue hemodynamics with applications in oncology,” Dissertation, Harvard Medical School (2014).

28. C. Zhu and Q. Liu, “Validity of the semi-infinite tumor model in diffuse reflectance spectroscopy for epithelial cancer diagnosis: a Monte Carlo study,” Opt. Express 19(18), 17799–17812 (2011). [CrossRef]  

References

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  1. D. Evers, B. Hendriks, G. Lucassen, and T. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).
    [Crossref]
  2. C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
    [Crossref]
  3. E. H. Moriyama, A. Kim, A. Bogaards, L. Lilge, and B. C. Wilson, “A ratiometric fluorescence imaging system for surgical guidance,” Adv. Opt. Technol. 2008, 1–10 (2008).
    [Crossref]
  4. H. C. Hendargo, Y. Zhao, T. Allenby, and G. M. Palmer, “Snap-shot multispectral imaging of vascular dynamics in a mouse window-chamber model,” Opt. Lett. 40(14), 3292–3295 (2015).
    [Crossref]
  5. J. E. Bender, K. Vishwanath, L. K. Moore, J. Q. Brown, V. Chang, G. M. Palmer, and N. Ramanujam, “A robust-Monte Carlo model for the extraction of biological absorption and scattering in vivo,” IEEE Trans. Biomed. Eng. 56(4), 960–968 (2009).
    [Crossref]
  6. M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U. S. A. 104(49), 19494–19499 (2007).
    [Crossref]
  7. A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
    [Crossref]
  8. C. Zhu, S. Chen, C. H. Chui, B. K. Tan, and Q. Liu, “Early prediction of skin viability using visible diffuse reflectance spectroscopy and autofluorescence spectroscopy,” Plast. Reconstr. Surg. 134(2), 240e–247e (2014).
    [Crossref]
  9. C. Zhu, A. F. Martinez, H. L. Martin, M. Li, B. T. Crouch, D. A. Carlson, T. A. J. Haystead, and N. Ramanujam, “Near-simultaneous intravital microscopy of glucose uptake and mitochondrial membrane potential, key endpoints that reflect major metabolic axes in cancer,” Sci. Rep. 7(1), 13772 (2017).
    [Crossref]
  10. A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
    [Crossref]
  11. C. Zhu, Hannah L. Martin, Brian T. Crouch, Amy F. Martinez, Martin Li, Gregory M. Palmer, Mark W. Dewhirst, and N. Ramanujam, “Near-simultaneous quantification of glucose uptake, mitochondrial membrane potential, and vascular parameters in murine flank tumors using quantitative diffuse reflectance and fluorescence spectroscopy,” Biomed. Opt. Express 9(7), 3399–3412 (2018).
    [Crossref]
  12. U. Utzinger and R. R. Richards-Kortum, “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt. 8(1), 121–147 (2003).
    [Crossref]
  13. G. Greening, A. Mundo, N. Rajaram, and T. J. Muldoon, “Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts,” J. Biomed. Opt. 23(08), 1–14 (2018).
    [Crossref]
  14. R. Hennessy, W. Goth, M. Sharma, M. K. Markey, and J. W. Tunnell, “Effect of probe geometry and optical properties on the sampling depth for diffuse reflectance spectroscopy,” J. Biomed. Opt. 19(10), 107002 (2014).
    [Crossref]
  15. S. H. Tseng, C. K. Hayakawa, J. Spanier, and A. J. Durkin, “Determination of optical properties of superficial volumes of layered tissue phantoms,” IEEE Trans. Biomed. Eng. 55(1), 335–339 (2008).
    [Crossref]
  16. Q. Liu and N. Ramanujam, “Sequential estimation of optical properties of a two-layered epithelial tissue model from depth-resolved ultraviolet-visible diffuse reflectance spectra,” Appl. Opt. 45(19), 4776–4790 (2006).
    [Crossref]
  17. T. Papaioannou, N. W. Preyer, Q. Fang, A. Brightwell, M. Carnohan, G. Cottone, R. Ross, L. R. Jones, and L. Marcu, “Effects of fiber-optic probe design and probe-to-target distance on diffuse reflectance measurements of turbid media: an experimental and computational study at 337 nm,” Appl. Opt. 43(14), 2846–2860 (2004).
    [Crossref]
  18. C. Zhu and Q. Liu, “Numerical investigation of lens based setup for depth sensitive diffuse reflectance measurements in an epithelial cancer model,” Opt. Express 20(28), 29807–29822 (2012).
    [Crossref]
  19. Y. H. Ong, C. Zhu, and Q. Liu, “Phantom validation of Monte Carlo modeling for noncontact depth sensitive fluorescence measurements in an epithelial tissue model,” J. Biomed. Opt. 19(8), 085006 (2014).
    [Crossref]
  20. C. Zhu and Q. Liu, “Validity of the semi-infinite tumor model in tissue optics: a Monte Carlo study,” in 2010 Photonics Global Conference (PGC) (IEEE2010), pp. 1–4.
  21. N. Rajaram, A. F. Reesor, C. S. Mulvey, A. E. Frees, and N. Ramanujam, “Non-invasive, simultaneous quantification of vascular oxygenation and glucose uptake in tissue,” PLoS One 10(1), e0117132 (2015).
    [Crossref]
  22. I. Serganova, A. Rizwan, X. Ni, S. B. Thakur, J. Vider, J. Russell, R. Blasberg, and J. A. Koutcher, “Metabolic imaging: a link between lactate dehydrogenase A, lactate, and tumor phenotype,” Clin. Cancer Res. 17(19), 6250–6261 (2011).
    [Crossref]
  23. S. K. Chang, D. Arifler, R. Drezek, M. Follen, and R. Richards-Kortum, “Analytical model to describe fluorescence spectra of normal and preneoplastic epithelial tissue: comparison with Monte Carlo simulations and clinical measurements,” J. Biomed. Opt. 9(3), 511–522 (2004).
    [Crossref]
  24. C. P. Sabino, A. M. Deana, T. M. Yoshimura, D. F. da Silva, C. M. Franca, M. R. Hamblin, and M. S. Ribeiro, “The optical properties of mouse skin in the visible and near infrared spectral regions,” J. Photochem. Photobiol., B 160, 72–78 (2016).
    [Crossref]
  25. Q. Liu, C. Zhu, and N. Ramanujam, “Experimental validation of Monte Carlo modeling of fluorescence in tissues in the UV-visible spectrum,” J. Biomed. Opt. 8(2), 223–236 (2003).
    [Crossref]
  26. L. V. Wang and H.-I. Wu, Biomedical Optics: Principles and Imaging (John Wiley & Sons, 2012).
  27. P. Farzam, “Hybrid diffuse optics for monitoring of tissue hemodynamics with applications in oncology,” Dissertation, Harvard Medical School (2014).
  28. C. Zhu and Q. Liu, “Validity of the semi-infinite tumor model in diffuse reflectance spectroscopy for epithelial cancer diagnosis: a Monte Carlo study,” Opt. Express 19(18), 17799–17812 (2011).
    [Crossref]

2019 (1)

C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
[Crossref]

2018 (3)

G. Greening, A. Mundo, N. Rajaram, and T. J. Muldoon, “Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts,” J. Biomed. Opt. 23(08), 1–14 (2018).
[Crossref]

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

C. Zhu, Hannah L. Martin, Brian T. Crouch, Amy F. Martinez, Martin Li, Gregory M. Palmer, Mark W. Dewhirst, and N. Ramanujam, “Near-simultaneous quantification of glucose uptake, mitochondrial membrane potential, and vascular parameters in murine flank tumors using quantitative diffuse reflectance and fluorescence spectroscopy,” Biomed. Opt. Express 9(7), 3399–3412 (2018).
[Crossref]

2017 (1)

C. Zhu, A. F. Martinez, H. L. Martin, M. Li, B. T. Crouch, D. A. Carlson, T. A. J. Haystead, and N. Ramanujam, “Near-simultaneous intravital microscopy of glucose uptake and mitochondrial membrane potential, key endpoints that reflect major metabolic axes in cancer,” Sci. Rep. 7(1), 13772 (2017).
[Crossref]

2016 (1)

C. P. Sabino, A. M. Deana, T. M. Yoshimura, D. F. da Silva, C. M. Franca, M. R. Hamblin, and M. S. Ribeiro, “The optical properties of mouse skin in the visible and near infrared spectral regions,” J. Photochem. Photobiol., B 160, 72–78 (2016).
[Crossref]

2015 (2)

N. Rajaram, A. F. Reesor, C. S. Mulvey, A. E. Frees, and N. Ramanujam, “Non-invasive, simultaneous quantification of vascular oxygenation and glucose uptake in tissue,” PLoS One 10(1), e0117132 (2015).
[Crossref]

H. C. Hendargo, Y. Zhao, T. Allenby, and G. M. Palmer, “Snap-shot multispectral imaging of vascular dynamics in a mouse window-chamber model,” Opt. Lett. 40(14), 3292–3295 (2015).
[Crossref]

2014 (4)

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref]

C. Zhu, S. Chen, C. H. Chui, B. K. Tan, and Q. Liu, “Early prediction of skin viability using visible diffuse reflectance spectroscopy and autofluorescence spectroscopy,” Plast. Reconstr. Surg. 134(2), 240e–247e (2014).
[Crossref]

Y. H. Ong, C. Zhu, and Q. Liu, “Phantom validation of Monte Carlo modeling for noncontact depth sensitive fluorescence measurements in an epithelial tissue model,” J. Biomed. Opt. 19(8), 085006 (2014).
[Crossref]

R. Hennessy, W. Goth, M. Sharma, M. K. Markey, and J. W. Tunnell, “Effect of probe geometry and optical properties on the sampling depth for diffuse reflectance spectroscopy,” J. Biomed. Opt. 19(10), 107002 (2014).
[Crossref]

2012 (2)

C. Zhu and Q. Liu, “Numerical investigation of lens based setup for depth sensitive diffuse reflectance measurements in an epithelial cancer model,” Opt. Express 20(28), 29807–29822 (2012).
[Crossref]

D. Evers, B. Hendriks, G. Lucassen, and T. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).
[Crossref]

2011 (2)

I. Serganova, A. Rizwan, X. Ni, S. B. Thakur, J. Vider, J. Russell, R. Blasberg, and J. A. Koutcher, “Metabolic imaging: a link between lactate dehydrogenase A, lactate, and tumor phenotype,” Clin. Cancer Res. 17(19), 6250–6261 (2011).
[Crossref]

C. Zhu and Q. Liu, “Validity of the semi-infinite tumor model in diffuse reflectance spectroscopy for epithelial cancer diagnosis: a Monte Carlo study,” Opt. Express 19(18), 17799–17812 (2011).
[Crossref]

2009 (1)

J. E. Bender, K. Vishwanath, L. K. Moore, J. Q. Brown, V. Chang, G. M. Palmer, and N. Ramanujam, “A robust-Monte Carlo model for the extraction of biological absorption and scattering in vivo,” IEEE Trans. Biomed. Eng. 56(4), 960–968 (2009).
[Crossref]

2008 (2)

E. H. Moriyama, A. Kim, A. Bogaards, L. Lilge, and B. C. Wilson, “A ratiometric fluorescence imaging system for surgical guidance,” Adv. Opt. Technol. 2008, 1–10 (2008).
[Crossref]

S. H. Tseng, C. K. Hayakawa, J. Spanier, and A. J. Durkin, “Determination of optical properties of superficial volumes of layered tissue phantoms,” IEEE Trans. Biomed. Eng. 55(1), 335–339 (2008).
[Crossref]

2007 (1)

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U. S. A. 104(49), 19494–19499 (2007).
[Crossref]

2006 (1)

2004 (2)

T. Papaioannou, N. W. Preyer, Q. Fang, A. Brightwell, M. Carnohan, G. Cottone, R. Ross, L. R. Jones, and L. Marcu, “Effects of fiber-optic probe design and probe-to-target distance on diffuse reflectance measurements of turbid media: an experimental and computational study at 337 nm,” Appl. Opt. 43(14), 2846–2860 (2004).
[Crossref]

S. K. Chang, D. Arifler, R. Drezek, M. Follen, and R. Richards-Kortum, “Analytical model to describe fluorescence spectra of normal and preneoplastic epithelial tissue: comparison with Monte Carlo simulations and clinical measurements,” J. Biomed. Opt. 9(3), 511–522 (2004).
[Crossref]

2003 (2)

Q. Liu, C. Zhu, and N. Ramanujam, “Experimental validation of Monte Carlo modeling of fluorescence in tissues in the UV-visible spectrum,” J. Biomed. Opt. 8(2), 223–236 (2003).
[Crossref]

U. Utzinger and R. R. Richards-Kortum, “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt. 8(1), 121–147 (2003).
[Crossref]

Allenby, T.

Arifler, D.

S. K. Chang, D. Arifler, R. Drezek, M. Follen, and R. Richards-Kortum, “Analytical model to describe fluorescence spectra of normal and preneoplastic epithelial tissue: comparison with Monte Carlo simulations and clinical measurements,” J. Biomed. Opt. 9(3), 511–522 (2004).
[Crossref]

Arteaga, C. L.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref]

Ashcraft, K.

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

Aurisicchio, L.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref]

Bender, J. E.

J. E. Bender, K. Vishwanath, L. K. Moore, J. Q. Brown, V. Chang, G. M. Palmer, and N. Ramanujam, “A robust-Monte Carlo model for the extraction of biological absorption and scattering in vivo,” IEEE Trans. Biomed. Eng. 56(4), 960–968 (2009).
[Crossref]

Blasberg, R.

I. Serganova, A. Rizwan, X. Ni, S. B. Thakur, J. Vider, J. Russell, R. Blasberg, and J. A. Koutcher, “Metabolic imaging: a link between lactate dehydrogenase A, lactate, and tumor phenotype,” Clin. Cancer Res. 17(19), 6250–6261 (2011).
[Crossref]

Bogaards, A.

E. H. Moriyama, A. Kim, A. Bogaards, L. Lilge, and B. C. Wilson, “A ratiometric fluorescence imaging system for surgical guidance,” Adv. Opt. Technol. 2008, 1–10 (2008).
[Crossref]

Brightwell, A.

Brown, J. Q.

J. E. Bender, K. Vishwanath, L. K. Moore, J. Q. Brown, V. Chang, G. M. Palmer, and N. Ramanujam, “A robust-Monte Carlo model for the extraction of biological absorption and scattering in vivo,” IEEE Trans. Biomed. Eng. 56(4), 960–968 (2009).
[Crossref]

Carlson, D. A.

C. Zhu, A. F. Martinez, H. L. Martin, M. Li, B. T. Crouch, D. A. Carlson, T. A. J. Haystead, and N. Ramanujam, “Near-simultaneous intravital microscopy of glucose uptake and mitochondrial membrane potential, key endpoints that reflect major metabolic axes in cancer,” Sci. Rep. 7(1), 13772 (2017).
[Crossref]

Carnohan, M.

Chang, S. K.

S. K. Chang, D. Arifler, R. Drezek, M. Follen, and R. Richards-Kortum, “Analytical model to describe fluorescence spectra of normal and preneoplastic epithelial tissue: comparison with Monte Carlo simulations and clinical measurements,” J. Biomed. Opt. 9(3), 511–522 (2004).
[Crossref]

Chang, V.

J. E. Bender, K. Vishwanath, L. K. Moore, J. Q. Brown, V. Chang, G. M. Palmer, and N. Ramanujam, “A robust-Monte Carlo model for the extraction of biological absorption and scattering in vivo,” IEEE Trans. Biomed. Eng. 56(4), 960–968 (2009).
[Crossref]

Chen, S.

C. Zhu, S. Chen, C. H. Chui, B. K. Tan, and Q. Liu, “Early prediction of skin viability using visible diffuse reflectance spectroscopy and autofluorescence spectroscopy,” Plast. Reconstr. Surg. 134(2), 240e–247e (2014).
[Crossref]

Chui, C. H.

C. Zhu, S. Chen, C. H. Chui, B. K. Tan, and Q. Liu, “Early prediction of skin viability using visible diffuse reflectance spectroscopy and autofluorescence spectroscopy,” Plast. Reconstr. Surg. 134(2), 240e–247e (2014).
[Crossref]

Ciliberto, G.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref]

Cook, R. S.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref]

Cottone, G.

Crouch, B. T.

C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
[Crossref]

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

C. Zhu, A. F. Martinez, H. L. Martin, M. Li, B. T. Crouch, D. A. Carlson, T. A. J. Haystead, and N. Ramanujam, “Near-simultaneous intravital microscopy of glucose uptake and mitochondrial membrane potential, key endpoints that reflect major metabolic axes in cancer,” Sci. Rep. 7(1), 13772 (2017).
[Crossref]

Crouch, Brian T.

da Silva, D. F.

C. P. Sabino, A. M. Deana, T. M. Yoshimura, D. F. da Silva, C. M. Franca, M. R. Hamblin, and M. S. Ribeiro, “The optical properties of mouse skin in the visible and near infrared spectral regions,” J. Photochem. Photobiol., B 160, 72–78 (2016).
[Crossref]

Deana, A. M.

C. P. Sabino, A. M. Deana, T. M. Yoshimura, D. F. da Silva, C. M. Franca, M. R. Hamblin, and M. S. Ribeiro, “The optical properties of mouse skin in the visible and near infrared spectral regions,” J. Photochem. Photobiol., B 160, 72–78 (2016).
[Crossref]

Dewhirst, M.

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

Dewhirst, M. W.

C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
[Crossref]

Dewhirst, Mark W.

Drezek, R.

S. K. Chang, D. Arifler, R. Drezek, M. Follen, and R. Richards-Kortum, “Analytical model to describe fluorescence spectra of normal and preneoplastic epithelial tissue: comparison with Monte Carlo simulations and clinical measurements,” J. Biomed. Opt. 9(3), 511–522 (2004).
[Crossref]

Durkin, A. J.

S. H. Tseng, C. K. Hayakawa, J. Spanier, and A. J. Durkin, “Determination of optical properties of superficial volumes of layered tissue phantoms,” IEEE Trans. Biomed. Eng. 55(1), 335–339 (2008).
[Crossref]

Eickhoff, J.

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U. S. A. 104(49), 19494–19499 (2007).
[Crossref]

Eliceiri, K. W.

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U. S. A. 104(49), 19494–19499 (2007).
[Crossref]

Erkanli, A.

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

Evers, D.

D. Evers, B. Hendriks, G. Lucassen, and T. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).
[Crossref]

Fang, Q.

Farzam, P.

P. Farzam, “Hybrid diffuse optics for monitoring of tissue hemodynamics with applications in oncology,” Dissertation, Harvard Medical School (2014).

Follen, M.

S. K. Chang, D. Arifler, R. Drezek, M. Follen, and R. Richards-Kortum, “Analytical model to describe fluorescence spectra of normal and preneoplastic epithelial tissue: comparison with Monte Carlo simulations and clinical measurements,” J. Biomed. Opt. 9(3), 511–522 (2004).
[Crossref]

Fontanella, A.

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

Franca, C. M.

C. P. Sabino, A. M. Deana, T. M. Yoshimura, D. F. da Silva, C. M. Franca, M. R. Hamblin, and M. S. Ribeiro, “The optical properties of mouse skin in the visible and near infrared spectral regions,” J. Photochem. Photobiol., B 160, 72–78 (2016).
[Crossref]

Frees, A. E.

N. Rajaram, A. F. Reesor, C. S. Mulvey, A. E. Frees, and N. Ramanujam, “Non-invasive, simultaneous quantification of vascular oxygenation and glucose uptake in tissue,” PLoS One 10(1), e0117132 (2015).
[Crossref]

Gendron-Fitzpatrick, A.

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U. S. A. 104(49), 19494–19499 (2007).
[Crossref]

Goth, W.

R. Hennessy, W. Goth, M. Sharma, M. K. Markey, and J. W. Tunnell, “Effect of probe geometry and optical properties on the sampling depth for diffuse reflectance spectroscopy,” J. Biomed. Opt. 19(10), 107002 (2014).
[Crossref]

Greening, G.

G. Greening, A. Mundo, N. Rajaram, and T. J. Muldoon, “Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts,” J. Biomed. Opt. 23(08), 1–14 (2018).
[Crossref]

Hamblin, M. R.

C. P. Sabino, A. M. Deana, T. M. Yoshimura, D. F. da Silva, C. M. Franca, M. R. Hamblin, and M. S. Ribeiro, “The optical properties of mouse skin in the visible and near infrared spectral regions,” J. Photochem. Photobiol., B 160, 72–78 (2016).
[Crossref]

Hayakawa, C. K.

S. H. Tseng, C. K. Hayakawa, J. Spanier, and A. J. Durkin, “Determination of optical properties of superficial volumes of layered tissue phantoms,” IEEE Trans. Biomed. Eng. 55(1), 335–339 (2008).
[Crossref]

Haystead, T. A. J.

C. Zhu, A. F. Martinez, H. L. Martin, M. Li, B. T. Crouch, D. A. Carlson, T. A. J. Haystead, and N. Ramanujam, “Near-simultaneous intravital microscopy of glucose uptake and mitochondrial membrane potential, key endpoints that reflect major metabolic axes in cancer,” Sci. Rep. 7(1), 13772 (2017).
[Crossref]

Hendargo, H. C.

Hendriks, B.

D. Evers, B. Hendriks, G. Lucassen, and T. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).
[Crossref]

Hennessy, R.

R. Hennessy, W. Goth, M. Sharma, M. K. Markey, and J. W. Tunnell, “Effect of probe geometry and optical properties on the sampling depth for diffuse reflectance spectroscopy,” J. Biomed. Opt. 19(10), 107002 (2014).
[Crossref]

Jones, L. R.

Kim, A.

E. H. Moriyama, A. Kim, A. Bogaards, L. Lilge, and B. C. Wilson, “A ratiometric fluorescence imaging system for surgical guidance,” Adv. Opt. Technol. 2008, 1–10 (2008).
[Crossref]

Koutcher, J. A.

I. Serganova, A. Rizwan, X. Ni, S. B. Thakur, J. Vider, J. Russell, R. Blasberg, and J. A. Koutcher, “Metabolic imaging: a link between lactate dehydrogenase A, lactate, and tumor phenotype,” Clin. Cancer Res. 17(19), 6250–6261 (2011).
[Crossref]

Lee, M.

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

Li, M.

C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
[Crossref]

C. Zhu, A. F. Martinez, H. L. Martin, M. Li, B. T. Crouch, D. A. Carlson, T. A. J. Haystead, and N. Ramanujam, “Near-simultaneous intravital microscopy of glucose uptake and mitochondrial membrane potential, key endpoints that reflect major metabolic axes in cancer,” Sci. Rep. 7(1), 13772 (2017).
[Crossref]

Li, Martin

Lilge, L.

E. H. Moriyama, A. Kim, A. Bogaards, L. Lilge, and B. C. Wilson, “A ratiometric fluorescence imaging system for surgical guidance,” Adv. Opt. Technol. 2008, 1–10 (2008).
[Crossref]

Liu, Q.

C. Zhu, S. Chen, C. H. Chui, B. K. Tan, and Q. Liu, “Early prediction of skin viability using visible diffuse reflectance spectroscopy and autofluorescence spectroscopy,” Plast. Reconstr. Surg. 134(2), 240e–247e (2014).
[Crossref]

Y. H. Ong, C. Zhu, and Q. Liu, “Phantom validation of Monte Carlo modeling for noncontact depth sensitive fluorescence measurements in an epithelial tissue model,” J. Biomed. Opt. 19(8), 085006 (2014).
[Crossref]

C. Zhu and Q. Liu, “Numerical investigation of lens based setup for depth sensitive diffuse reflectance measurements in an epithelial cancer model,” Opt. Express 20(28), 29807–29822 (2012).
[Crossref]

C. Zhu and Q. Liu, “Validity of the semi-infinite tumor model in diffuse reflectance spectroscopy for epithelial cancer diagnosis: a Monte Carlo study,” Opt. Express 19(18), 17799–17812 (2011).
[Crossref]

Q. Liu and N. Ramanujam, “Sequential estimation of optical properties of a two-layered epithelial tissue model from depth-resolved ultraviolet-visible diffuse reflectance spectra,” Appl. Opt. 45(19), 4776–4790 (2006).
[Crossref]

Q. Liu, C. Zhu, and N. Ramanujam, “Experimental validation of Monte Carlo modeling of fluorescence in tissues in the UV-visible spectrum,” J. Biomed. Opt. 8(2), 223–236 (2003).
[Crossref]

C. Zhu and Q. Liu, “Validity of the semi-infinite tumor model in tissue optics: a Monte Carlo study,” in 2010 Photonics Global Conference (PGC) (IEEE2010), pp. 1–4.

Lucassen, G.

D. Evers, B. Hendriks, G. Lucassen, and T. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).
[Crossref]

Madonna, M. C.

C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
[Crossref]

Marcu, L.

Markey, M. K.

R. Hennessy, W. Goth, M. Sharma, M. K. Markey, and J. W. Tunnell, “Effect of probe geometry and optical properties on the sampling depth for diffuse reflectance spectroscopy,” J. Biomed. Opt. 19(10), 107002 (2014).
[Crossref]

Martin, H.

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

Martin, H. L.

C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
[Crossref]

C. Zhu, A. F. Martinez, H. L. Martin, M. Li, B. T. Crouch, D. A. Carlson, T. A. J. Haystead, and N. Ramanujam, “Near-simultaneous intravital microscopy of glucose uptake and mitochondrial membrane potential, key endpoints that reflect major metabolic axes in cancer,” Sci. Rep. 7(1), 13772 (2017).
[Crossref]

Martin, Hannah L.

Martinez, A.

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

Martinez, A. F.

C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
[Crossref]

C. Zhu, A. F. Martinez, H. L. Martin, M. Li, B. T. Crouch, D. A. Carlson, T. A. J. Haystead, and N. Ramanujam, “Near-simultaneous intravital microscopy of glucose uptake and mitochondrial membrane potential, key endpoints that reflect major metabolic axes in cancer,” Sci. Rep. 7(1), 13772 (2017).
[Crossref]

Martinez, Amy F.

McCachren, S.

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

Moore, L. K.

J. E. Bender, K. Vishwanath, L. K. Moore, J. Q. Brown, V. Chang, G. M. Palmer, and N. Ramanujam, “A robust-Monte Carlo model for the extraction of biological absorption and scattering in vivo,” IEEE Trans. Biomed. Eng. 56(4), 960–968 (2009).
[Crossref]

Moriyama, E. H.

E. H. Moriyama, A. Kim, A. Bogaards, L. Lilge, and B. C. Wilson, “A ratiometric fluorescence imaging system for surgical guidance,” Adv. Opt. Technol. 2008, 1–10 (2008).
[Crossref]

Muldoon, T. J.

G. Greening, A. Mundo, N. Rajaram, and T. J. Muldoon, “Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts,” J. Biomed. Opt. 23(08), 1–14 (2018).
[Crossref]

Mulvey, C. S.

N. Rajaram, A. F. Reesor, C. S. Mulvey, A. E. Frees, and N. Ramanujam, “Non-invasive, simultaneous quantification of vascular oxygenation and glucose uptake in tissue,” PLoS One 10(1), e0117132 (2015).
[Crossref]

Mundo, A.

G. Greening, A. Mundo, N. Rajaram, and T. J. Muldoon, “Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts,” J. Biomed. Opt. 23(08), 1–14 (2018).
[Crossref]

Murphy, H.

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

Ni, X.

I. Serganova, A. Rizwan, X. Ni, S. B. Thakur, J. Vider, J. Russell, R. Blasberg, and J. A. Koutcher, “Metabolic imaging: a link between lactate dehydrogenase A, lactate, and tumor phenotype,” Clin. Cancer Res. 17(19), 6250–6261 (2011).
[Crossref]

Ong, Y. H.

Y. H. Ong, C. Zhu, and Q. Liu, “Phantom validation of Monte Carlo modeling for noncontact depth sensitive fluorescence measurements in an epithelial tissue model,” J. Biomed. Opt. 19(8), 085006 (2014).
[Crossref]

Palmer, G. M.

C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
[Crossref]

H. C. Hendargo, Y. Zhao, T. Allenby, and G. M. Palmer, “Snap-shot multispectral imaging of vascular dynamics in a mouse window-chamber model,” Opt. Lett. 40(14), 3292–3295 (2015).
[Crossref]

J. E. Bender, K. Vishwanath, L. K. Moore, J. Q. Brown, V. Chang, G. M. Palmer, and N. Ramanujam, “A robust-Monte Carlo model for the extraction of biological absorption and scattering in vivo,” IEEE Trans. Biomed. Eng. 56(4), 960–968 (2009).
[Crossref]

Palmer, Gregory M.

Papaioannou, T.

Preyer, N. W.

Rajaram, N.

G. Greening, A. Mundo, N. Rajaram, and T. J. Muldoon, “Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts,” J. Biomed. Opt. 23(08), 1–14 (2018).
[Crossref]

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

N. Rajaram, A. F. Reesor, C. S. Mulvey, A. E. Frees, and N. Ramanujam, “Non-invasive, simultaneous quantification of vascular oxygenation and glucose uptake in tissue,” PLoS One 10(1), e0117132 (2015).
[Crossref]

Ramanujam, N.

C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
[Crossref]

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

C. Zhu, Hannah L. Martin, Brian T. Crouch, Amy F. Martinez, Martin Li, Gregory M. Palmer, Mark W. Dewhirst, and N. Ramanujam, “Near-simultaneous quantification of glucose uptake, mitochondrial membrane potential, and vascular parameters in murine flank tumors using quantitative diffuse reflectance and fluorescence spectroscopy,” Biomed. Opt. Express 9(7), 3399–3412 (2018).
[Crossref]

C. Zhu, A. F. Martinez, H. L. Martin, M. Li, B. T. Crouch, D. A. Carlson, T. A. J. Haystead, and N. Ramanujam, “Near-simultaneous intravital microscopy of glucose uptake and mitochondrial membrane potential, key endpoints that reflect major metabolic axes in cancer,” Sci. Rep. 7(1), 13772 (2017).
[Crossref]

N. Rajaram, A. F. Reesor, C. S. Mulvey, A. E. Frees, and N. Ramanujam, “Non-invasive, simultaneous quantification of vascular oxygenation and glucose uptake in tissue,” PLoS One 10(1), e0117132 (2015).
[Crossref]

J. E. Bender, K. Vishwanath, L. K. Moore, J. Q. Brown, V. Chang, G. M. Palmer, and N. Ramanujam, “A robust-Monte Carlo model for the extraction of biological absorption and scattering in vivo,” IEEE Trans. Biomed. Eng. 56(4), 960–968 (2009).
[Crossref]

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U. S. A. 104(49), 19494–19499 (2007).
[Crossref]

Q. Liu and N. Ramanujam, “Sequential estimation of optical properties of a two-layered epithelial tissue model from depth-resolved ultraviolet-visible diffuse reflectance spectra,” Appl. Opt. 45(19), 4776–4790 (2006).
[Crossref]

Q. Liu, C. Zhu, and N. Ramanujam, “Experimental validation of Monte Carlo modeling of fluorescence in tissues in the UV-visible spectrum,” J. Biomed. Opt. 8(2), 223–236 (2003).
[Crossref]

Reesor, A. F.

N. Rajaram, A. F. Reesor, C. S. Mulvey, A. E. Frees, and N. Ramanujam, “Non-invasive, simultaneous quantification of vascular oxygenation and glucose uptake in tissue,” PLoS One 10(1), e0117132 (2015).
[Crossref]

Ribeiro, M. S.

C. P. Sabino, A. M. Deana, T. M. Yoshimura, D. F. da Silva, C. M. Franca, M. R. Hamblin, and M. S. Ribeiro, “The optical properties of mouse skin in the visible and near infrared spectral regions,” J. Photochem. Photobiol., B 160, 72–78 (2016).
[Crossref]

Richards-Kortum, R.

S. K. Chang, D. Arifler, R. Drezek, M. Follen, and R. Richards-Kortum, “Analytical model to describe fluorescence spectra of normal and preneoplastic epithelial tissue: comparison with Monte Carlo simulations and clinical measurements,” J. Biomed. Opt. 9(3), 511–522 (2004).
[Crossref]

Richards-Kortum, R. R.

U. Utzinger and R. R. Richards-Kortum, “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt. 8(1), 121–147 (2003).
[Crossref]

Riching, K. M.

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U. S. A. 104(49), 19494–19499 (2007).
[Crossref]

Rizwan, A.

I. Serganova, A. Rizwan, X. Ni, S. B. Thakur, J. Vider, J. Russell, R. Blasberg, and J. A. Koutcher, “Metabolic imaging: a link between lactate dehydrogenase A, lactate, and tumor phenotype,” Clin. Cancer Res. 17(19), 6250–6261 (2011).
[Crossref]

Ross, R.

Ruers, T.

D. Evers, B. Hendriks, G. Lucassen, and T. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).
[Crossref]

Russell, J.

I. Serganova, A. Rizwan, X. Ni, S. B. Thakur, J. Vider, J. Russell, R. Blasberg, and J. A. Koutcher, “Metabolic imaging: a link between lactate dehydrogenase A, lactate, and tumor phenotype,” Clin. Cancer Res. 17(19), 6250–6261 (2011).
[Crossref]

Sabino, C. P.

C. P. Sabino, A. M. Deana, T. M. Yoshimura, D. F. da Silva, C. M. Franca, M. R. Hamblin, and M. S. Ribeiro, “The optical properties of mouse skin in the visible and near infrared spectral regions,” J. Photochem. Photobiol., B 160, 72–78 (2016).
[Crossref]

Sanders, M. E.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref]

Serganova, I.

I. Serganova, A. Rizwan, X. Ni, S. B. Thakur, J. Vider, J. Russell, R. Blasberg, and J. A. Koutcher, “Metabolic imaging: a link between lactate dehydrogenase A, lactate, and tumor phenotype,” Clin. Cancer Res. 17(19), 6250–6261 (2011).
[Crossref]

Sharma, M.

R. Hennessy, W. Goth, M. Sharma, M. K. Markey, and J. W. Tunnell, “Effect of probe geometry and optical properties on the sampling depth for diffuse reflectance spectroscopy,” J. Biomed. Opt. 19(10), 107002 (2014).
[Crossref]

Skala, M. C.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref]

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U. S. A. 104(49), 19494–19499 (2007).
[Crossref]

Spanier, J.

S. H. Tseng, C. K. Hayakawa, J. Spanier, and A. J. Durkin, “Determination of optical properties of superficial volumes of layered tissue phantoms,” IEEE Trans. Biomed. Eng. 55(1), 335–339 (2008).
[Crossref]

Tan, B. K.

C. Zhu, S. Chen, C. H. Chui, B. K. Tan, and Q. Liu, “Early prediction of skin viability using visible diffuse reflectance spectroscopy and autofluorescence spectroscopy,” Plast. Reconstr. Surg. 134(2), 240e–247e (2014).
[Crossref]

Thakur, S. B.

I. Serganova, A. Rizwan, X. Ni, S. B. Thakur, J. Vider, J. Russell, R. Blasberg, and J. A. Koutcher, “Metabolic imaging: a link between lactate dehydrogenase A, lactate, and tumor phenotype,” Clin. Cancer Res. 17(19), 6250–6261 (2011).
[Crossref]

Tseng, S. H.

S. H. Tseng, C. K. Hayakawa, J. Spanier, and A. J. Durkin, “Determination of optical properties of superficial volumes of layered tissue phantoms,” IEEE Trans. Biomed. Eng. 55(1), 335–339 (2008).
[Crossref]

Tunnell, J. W.

R. Hennessy, W. Goth, M. Sharma, M. K. Markey, and J. W. Tunnell, “Effect of probe geometry and optical properties on the sampling depth for diffuse reflectance spectroscopy,” J. Biomed. Opt. 19(10), 107002 (2014).
[Crossref]

Utzinger, U.

U. Utzinger and R. R. Richards-Kortum, “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt. 8(1), 121–147 (2003).
[Crossref]

Vider, J.

I. Serganova, A. Rizwan, X. Ni, S. B. Thakur, J. Vider, J. Russell, R. Blasberg, and J. A. Koutcher, “Metabolic imaging: a link between lactate dehydrogenase A, lactate, and tumor phenotype,” Clin. Cancer Res. 17(19), 6250–6261 (2011).
[Crossref]

Vincent, T.

C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
[Crossref]

Vishwanath, K.

J. E. Bender, K. Vishwanath, L. K. Moore, J. Q. Brown, V. Chang, G. M. Palmer, and N. Ramanujam, “A robust-Monte Carlo model for the extraction of biological absorption and scattering in vivo,” IEEE Trans. Biomed. Eng. 56(4), 960–968 (2009).
[Crossref]

Walsh, A. J.

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref]

Wang, L. V.

L. V. Wang and H.-I. Wu, Biomedical Optics: Principles and Imaging (John Wiley & Sons, 2012).

White, J. G.

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U. S. A. 104(49), 19494–19499 (2007).
[Crossref]

Wilson, B. C.

E. H. Moriyama, A. Kim, A. Bogaards, L. Lilge, and B. C. Wilson, “A ratiometric fluorescence imaging system for surgical guidance,” Adv. Opt. Technol. 2008, 1–10 (2008).
[Crossref]

Wu, H.-I.

L. V. Wang and H.-I. Wu, Biomedical Optics: Principles and Imaging (John Wiley & Sons, 2012).

Yoshimura, T. M.

C. P. Sabino, A. M. Deana, T. M. Yoshimura, D. F. da Silva, C. M. Franca, M. R. Hamblin, and M. S. Ribeiro, “The optical properties of mouse skin in the visible and near infrared spectral regions,” J. Photochem. Photobiol., B 160, 72–78 (2016).
[Crossref]

Zhao, Y.

Zhu, C.

C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
[Crossref]

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

C. Zhu, Hannah L. Martin, Brian T. Crouch, Amy F. Martinez, Martin Li, Gregory M. Palmer, Mark W. Dewhirst, and N. Ramanujam, “Near-simultaneous quantification of glucose uptake, mitochondrial membrane potential, and vascular parameters in murine flank tumors using quantitative diffuse reflectance and fluorescence spectroscopy,” Biomed. Opt. Express 9(7), 3399–3412 (2018).
[Crossref]

C. Zhu, A. F. Martinez, H. L. Martin, M. Li, B. T. Crouch, D. A. Carlson, T. A. J. Haystead, and N. Ramanujam, “Near-simultaneous intravital microscopy of glucose uptake and mitochondrial membrane potential, key endpoints that reflect major metabolic axes in cancer,” Sci. Rep. 7(1), 13772 (2017).
[Crossref]

C. Zhu, S. Chen, C. H. Chui, B. K. Tan, and Q. Liu, “Early prediction of skin viability using visible diffuse reflectance spectroscopy and autofluorescence spectroscopy,” Plast. Reconstr. Surg. 134(2), 240e–247e (2014).
[Crossref]

Y. H. Ong, C. Zhu, and Q. Liu, “Phantom validation of Monte Carlo modeling for noncontact depth sensitive fluorescence measurements in an epithelial tissue model,” J. Biomed. Opt. 19(8), 085006 (2014).
[Crossref]

C. Zhu and Q. Liu, “Numerical investigation of lens based setup for depth sensitive diffuse reflectance measurements in an epithelial cancer model,” Opt. Express 20(28), 29807–29822 (2012).
[Crossref]

C. Zhu and Q. Liu, “Validity of the semi-infinite tumor model in diffuse reflectance spectroscopy for epithelial cancer diagnosis: a Monte Carlo study,” Opt. Express 19(18), 17799–17812 (2011).
[Crossref]

Q. Liu, C. Zhu, and N. Ramanujam, “Experimental validation of Monte Carlo modeling of fluorescence in tissues in the UV-visible spectrum,” J. Biomed. Opt. 8(2), 223–236 (2003).
[Crossref]

C. Zhu and Q. Liu, “Validity of the semi-infinite tumor model in tissue optics: a Monte Carlo study,” in 2010 Photonics Global Conference (PGC) (IEEE2010), pp. 1–4.

Adv. Opt. Technol. (1)

E. H. Moriyama, A. Kim, A. Bogaards, L. Lilge, and B. C. Wilson, “A ratiometric fluorescence imaging system for surgical guidance,” Adv. Opt. Technol. 2008, 1–10 (2008).
[Crossref]

Appl. Opt. (2)

Biomed. Opt. Express (1)

Cancer Res. (1)

A. J. Walsh, R. S. Cook, M. E. Sanders, L. Aurisicchio, G. Ciliberto, C. L. Arteaga, and M. C. Skala, “Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer,” Cancer Res. 74(18), 5184–5194 (2014).
[Crossref]

Clin. Cancer Res. (1)

I. Serganova, A. Rizwan, X. Ni, S. B. Thakur, J. Vider, J. Russell, R. Blasberg, and J. A. Koutcher, “Metabolic imaging: a link between lactate dehydrogenase A, lactate, and tumor phenotype,” Clin. Cancer Res. 17(19), 6250–6261 (2011).
[Crossref]

Future Oncol. (1)

D. Evers, B. Hendriks, G. Lucassen, and T. Ruers, “Optical spectroscopy: current advances and future applications in cancer diagnostics and therapy,” Future Oncol. 8(3), 307–320 (2012).
[Crossref]

IEEE Trans. Biomed. Eng. (2)

J. E. Bender, K. Vishwanath, L. K. Moore, J. Q. Brown, V. Chang, G. M. Palmer, and N. Ramanujam, “A robust-Monte Carlo model for the extraction of biological absorption and scattering in vivo,” IEEE Trans. Biomed. Eng. 56(4), 960–968 (2009).
[Crossref]

S. H. Tseng, C. K. Hayakawa, J. Spanier, and A. J. Durkin, “Determination of optical properties of superficial volumes of layered tissue phantoms,” IEEE Trans. Biomed. Eng. 55(1), 335–339 (2008).
[Crossref]

J. Biomed. Opt. (6)

Y. H. Ong, C. Zhu, and Q. Liu, “Phantom validation of Monte Carlo modeling for noncontact depth sensitive fluorescence measurements in an epithelial tissue model,” J. Biomed. Opt. 19(8), 085006 (2014).
[Crossref]

S. K. Chang, D. Arifler, R. Drezek, M. Follen, and R. Richards-Kortum, “Analytical model to describe fluorescence spectra of normal and preneoplastic epithelial tissue: comparison with Monte Carlo simulations and clinical measurements,” J. Biomed. Opt. 9(3), 511–522 (2004).
[Crossref]

Q. Liu, C. Zhu, and N. Ramanujam, “Experimental validation of Monte Carlo modeling of fluorescence in tissues in the UV-visible spectrum,” J. Biomed. Opt. 8(2), 223–236 (2003).
[Crossref]

U. Utzinger and R. R. Richards-Kortum, “Fiber optic probes for biomedical optical spectroscopy,” J. Biomed. Opt. 8(1), 121–147 (2003).
[Crossref]

G. Greening, A. Mundo, N. Rajaram, and T. J. Muldoon, “Sampling depth of a diffuse reflectance spectroscopy probe for in-vivo physiological quantification of murine subcutaneous tumor allografts,” J. Biomed. Opt. 23(08), 1–14 (2018).
[Crossref]

R. Hennessy, W. Goth, M. Sharma, M. K. Markey, and J. W. Tunnell, “Effect of probe geometry and optical properties on the sampling depth for diffuse reflectance spectroscopy,” J. Biomed. Opt. 19(10), 107002 (2014).
[Crossref]

J. Biophotonics (1)

C. Zhu, M. Li, T. Vincent, H. L. Martin, B. T. Crouch, A. F. Martinez, M. C. Madonna, G. M. Palmer, M. W. Dewhirst, and N. Ramanujam, “Simultaneous in vivo optical quantification of key metabolic and vascular endpoints reveals tumor metabolic diversity in murine breast tumor models,” J. Biophotonics 12(4), e201800372 (2019).
[Crossref]

J. Photochem. Photobiol., B (1)

C. P. Sabino, A. M. Deana, T. M. Yoshimura, D. F. da Silva, C. M. Franca, M. R. Hamblin, and M. S. Ribeiro, “The optical properties of mouse skin in the visible and near infrared spectral regions,” J. Photochem. Photobiol., B 160, 72–78 (2016).
[Crossref]

Opt. Express (2)

Opt. Lett. (1)

Plast. Reconstr. Surg. (1)

C. Zhu, S. Chen, C. H. Chui, B. K. Tan, and Q. Liu, “Early prediction of skin viability using visible diffuse reflectance spectroscopy and autofluorescence spectroscopy,” Plast. Reconstr. Surg. 134(2), 240e–247e (2014).
[Crossref]

PLoS One (1)

N. Rajaram, A. F. Reesor, C. S. Mulvey, A. E. Frees, and N. Ramanujam, “Non-invasive, simultaneous quantification of vascular oxygenation and glucose uptake in tissue,” PLoS One 10(1), e0117132 (2015).
[Crossref]

Proc. Natl. Acad. Sci. U. S. A. (1)

M. C. Skala, K. M. Riching, A. Gendron-Fitzpatrick, J. Eickhoff, K. W. Eliceiri, J. G. White, and N. Ramanujam, “In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia,” Proc. Natl. Acad. Sci. U. S. A. 104(49), 19494–19499 (2007).
[Crossref]

Sci. Rep. (2)

C. Zhu, A. F. Martinez, H. L. Martin, M. Li, B. T. Crouch, D. A. Carlson, T. A. J. Haystead, and N. Ramanujam, “Near-simultaneous intravital microscopy of glucose uptake and mitochondrial membrane potential, key endpoints that reflect major metabolic axes in cancer,” Sci. Rep. 7(1), 13772 (2017).
[Crossref]

A. Martinez, S. McCachren, M. Lee, H. Murphy, C. Zhu, B. T. Crouch, H. Martin, A. Erkanli, N. Rajaram, K. Ashcraft, A. Fontanella, M. Dewhirst, and N. Ramanujam, “Metaboloptics: Visualization of the tumor functional landscape via metabolic and vascular imaging,” Sci. Rep. 8(1), 4171 (2018).
[Crossref]

Other (3)

C. Zhu and Q. Liu, “Validity of the semi-infinite tumor model in tissue optics: a Monte Carlo study,” in 2010 Photonics Global Conference (PGC) (IEEE2010), pp. 1–4.

L. V. Wang and H.-I. Wu, Biomedical Optics: Principles and Imaging (John Wiley & Sons, 2012).

P. Farzam, “Hybrid diffuse optics for monitoring of tissue hemodynamics with applications in oncology,” Dissertation, Harvard Medical School (2014).

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Figures (9)

Fig. 1.
Fig. 1. (a) Probe configurations with one source fiber and six detector fibers. The source and detector distances (SD) were set to 0.5 mm, 1.0 mm, 1.5 mm, 2.0 mm, 2.5 mm, and 3.0 mm respectively. (b) The tilt angles of both illumination and collection fibers were 0 degree relative to the normal axis of the tissue surface. The two cylinders in both sets represent the source and detector fibers and the arrows indicate the direction of light propagation.
Fig. 2.
Fig. 2. (a) Semi-infinite layered tumor model to simulate advanced murine subcutaneous tumors. The normal skin thickness was set to be 0.7 mm or 1.0 mm. (b) Finite spherical tumor model to simulate small murine subcutaneous tumors. The tumor depth (distance between the tumor surface and the skin surface) was set to be 1.0 mm.
Fig. 3.
Fig. 3. WVF (refelctance) and fluorescenc frequency (fluorescence) simulated from semi-infinite tumor models with a tumor depth of 0.7 mm and 1.0 mm. WVF (a) and weighted fluorescence frequency (b) simulated at different SD for tumor model with a tumor depth of 0.7 mm. WVF (c) and weighted fluorescence frequency (d) simulated at different SD for tumor model with a tumor depth of 1.0 mm. The SD values were varied from 0.5 mm to 3.0 mm. In all simulations, the fiber diameter was fixed to be 0.2 mm and the NA was fixed to be 0.22.
Fig. 4.
Fig. 4. Simulated tumor contrast and light intensities (diffuse reflectance and fluorescence) from semi-infinite tumor models with a tumor depth of 0.7 mm (thick line) and 1.0 mm (thin line). (a) TCR and (b) TCF simluated from depth-resoloved measurements and that from averaged optical measurement. The total sum value of the light intensities simulated from all six collection fibers represent the averaged optical measurement (solid). The values of the light intensity simulated from each of the specific fibers with different SD refer to the depth-resolved optical measurements (dashed). (c) Diffuse reflectance intensities and (d) fluorescence intensities simulated at different SD values. (e) log scale of diffuse refelctance intensities and (f) log scale of fluorescence intensities at different SD values.
Fig. 5.
Fig. 5. Simulated WVF (a-f)) and weighted fluorescence frequency (h-l) for a finite tumor model with a tumor dimater of 1 mm, 2 mm, 3 mm, 5 mm, 6 mm, and 8 mm. The tumor depth was fixed to be 1 mm.
Fig. 6.
Fig. 6. TCR (a-f) and TCF (g-l) for a finite tumor model with a tumor dimater of 1 mm, 2 mm, 3 mm, 5 mm, 6 mm, and 8 mm. The tumor depth was fixed to be 1 mm.
Fig. 7.
Fig. 7. The effect of fiber diameter on tumor contrast and collected diffuse refelctance and fluorescence intensities. TCR (a), TCF (b), diffuse refelctance intensity (c), and fluorescence intensity (d) simulated with different source fiber diameters. The detector fiber diameter was fixed to be 0.2 mm in (a-d). TCR (e), TCF (f), diffuse refelctance intensity (g), and fluorescence intensity (h) simulated with different detector fiber diameters. The source fiber diameter was fixed to be 0.2 mm in (e-h). The NA of all fibers was fixed to be 0.22. The tumor diameter was 6 mm, and the top layer thickness was 1 mm. The SD was fixed to be 3 mm.
Fig. 8.
Fig. 8. The effect of fiber numerical apeture (NA) on tumor contrast and collected diffuse refelctance and fluorescence intensities. TCR (a), TCF (b), diffuse refelctance intensity (c), and fluorescence intensity (d) simulated with different source fiber NA. The detector fiber NA was fixed to be 0.22 in (a-d). TCR (e), TCF (f), diffuse refelctance intensity (g), and fluorescence intensity (h) simulated with different detector fiber diameters. The source fiber NA was fixed to be 0.22 in (e-h). The diameter of all fibers was fixed to be 0.2 mm. The tumor diameter was 6 mm, and the top layer thickness was 1 mm. The SD was fixed to be 3 mm.
Fig. 9.
Fig. 9. Illustration of potential photon travel paths in layered tumor model (a) and tiny spherical tumor model (b) for different fiber SD values. D1 represent a detection fiber that has a small SD, while D3 represent a detection fiber that has a large SD.

Tables (2)

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Table 1. Optical properties of normal skin tissue and murine subcutaneous tumor a

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Table 2. Rule of thumb SD values for optical measurements on the infinite tumor models

Equations (2)

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T C R = W V F t u m o r W V F t u m o r + W V F n o r m a l
T C F = F t u m o r F t u m o r + F n o r m a l

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