Abstract

The spatial resolution and recovered contrast of images reconstructed from diffuse fluorescence tomography data are limited by the high scattering properties of light propagation in biological tissue. As a result, the image reconstruction process can be exceedingly vulnerable to inaccurate prior knowledge of tissue optical properties and stochastic noise. In light of these limitations, the optimal source-detector geometry for a fluorescence tomography system is non-trivial, requiring analytical methods to guide design. Analysis of the singular value decomposition of the matrix to be inverted for image reconstruction is one potential approach, providing key quantitative metrics, such as singular image mode spatial resolution and singular data mode frequency as a function of singular mode. In the present study, these metrics are used to analyze the effects of different sources of noise and model errors as related to image quality in the form of spatial resolution and contrast recovery. The image quality is demonstrated to be inherently noise-limited even when detection geometries were increased in complexity to allow maximal tissue sampling, suggesting that detection noise characteristics outweigh detection geometry for achieving optimal reconstructions.

© 2010 OSA

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. V. Ntziachristos and R. Weissleder, “Charge-coupled-device based scanner for tomography of fluorescent near-infrared probes in turbid media,” Med. Phys. 29(5), 803–809 (2002).
    [CrossRef] [PubMed]
  2. D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
    [CrossRef] [PubMed]
  3. D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80(4), 043701 (2009).
    [CrossRef] [PubMed]
  4. V. Ntziachristos and R. Weissleder, “Experimental three-dimensional fluorescence reconstruction of diffuse media by use of a normalized Born approximation,” Opt. Lett. 26(12), 893–895 (2001).
    [CrossRef] [PubMed]
  5. F. Leblond, H. Dehghani, D. Kepshire, and B. W. Pogue, “Early-photon fluorescence tomography: spatial resolution improvements and noise stability considerations,” J. Opt. Soc. Am. A 26(6), 1444–1457 (2009).
    [CrossRef] [PubMed]
  6. J. P. Culver, V. Ntziachristos, M. J. Holboke, and A. G. Yodh, “Optimization of optode arrangements for diffuse optical tomography: a singular-value analysis,” Opt. Lett. 26(10), 701–703 (2001).
    [CrossRef] [PubMed]
  7. E. E. Graves, J. P. Culver, J. Ripoll, R. Weissleder, and V. Ntziachristos, “Singular-value analysis and optimization of experimental parameters in fluorescence molecular tomography,” J. Opt. Soc. Am. A 21(2), 231–241 (2004).
    [CrossRef] [PubMed]
  8. P. C. Hansen, Rank-Deficient and Discrete Ill-Posed Problems (SIAM, 1998).
  9. S. C. Davis, B. W. Pogue, H. Dehghani, and K. D. Paulsen, “Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction,” J. Biomed. Opt. 10(5), 050501 (2005).
    [CrossRef] [PubMed]
  10. T. Lasser and V. Ntziachristos, “Optimization of 360° projection fluorescence molecular tomography,” Med. Image Anal. 11(4), 389–399 (2007).
    [CrossRef] [PubMed]
  11. F. Leblond, S. C. Davis, P. A. Valdés, and B. W. Pogue, “Pre-clinical whole-body fluorescence imaging: Review of instruments, methods and applications,” J. Photochem. Photobiol. B 98(1), 77–94 (2010).
    [CrossRef] [PubMed]
  12. B. W. Pogue, F. Leblond, V. Krishnaswamy, and K. D. Paulsen, “Radiologic and near-infrared/optical spectroscopic imaging: where is the synergy?” AJR Am. J. Roentgenol. 195(2), 321–332 (2010).
    [CrossRef] [PubMed]

2010

F. Leblond, S. C. Davis, P. A. Valdés, and B. W. Pogue, “Pre-clinical whole-body fluorescence imaging: Review of instruments, methods and applications,” J. Photochem. Photobiol. B 98(1), 77–94 (2010).
[CrossRef] [PubMed]

B. W. Pogue, F. Leblond, V. Krishnaswamy, and K. D. Paulsen, “Radiologic and near-infrared/optical spectroscopic imaging: where is the synergy?” AJR Am. J. Roentgenol. 195(2), 321–332 (2010).
[CrossRef] [PubMed]

2009

D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
[CrossRef] [PubMed]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80(4), 043701 (2009).
[CrossRef] [PubMed]

F. Leblond, H. Dehghani, D. Kepshire, and B. W. Pogue, “Early-photon fluorescence tomography: spatial resolution improvements and noise stability considerations,” J. Opt. Soc. Am. A 26(6), 1444–1457 (2009).
[CrossRef] [PubMed]

2007

T. Lasser and V. Ntziachristos, “Optimization of 360° projection fluorescence molecular tomography,” Med. Image Anal. 11(4), 389–399 (2007).
[CrossRef] [PubMed]

2005

S. C. Davis, B. W. Pogue, H. Dehghani, and K. D. Paulsen, “Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction,” J. Biomed. Opt. 10(5), 050501 (2005).
[CrossRef] [PubMed]

2004

2002

V. Ntziachristos and R. Weissleder, “Charge-coupled-device based scanner for tomography of fluorescent near-infrared probes in turbid media,” Med. Phys. 29(5), 803–809 (2002).
[CrossRef] [PubMed]

2001

Culver, J. P.

Davis, S. C.

F. Leblond, S. C. Davis, P. A. Valdés, and B. W. Pogue, “Pre-clinical whole-body fluorescence imaging: Review of instruments, methods and applications,” J. Photochem. Photobiol. B 98(1), 77–94 (2010).
[CrossRef] [PubMed]

S. C. Davis, B. W. Pogue, H. Dehghani, and K. D. Paulsen, “Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction,” J. Biomed. Opt. 10(5), 050501 (2005).
[CrossRef] [PubMed]

Dehghani, H.

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80(4), 043701 (2009).
[CrossRef] [PubMed]

F. Leblond, H. Dehghani, D. Kepshire, and B. W. Pogue, “Early-photon fluorescence tomography: spatial resolution improvements and noise stability considerations,” J. Opt. Soc. Am. A 26(6), 1444–1457 (2009).
[CrossRef] [PubMed]

D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
[CrossRef] [PubMed]

S. C. Davis, B. W. Pogue, H. Dehghani, and K. D. Paulsen, “Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction,” J. Biomed. Opt. 10(5), 050501 (2005).
[CrossRef] [PubMed]

Gibbs-Strauss, S. L.

D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
[CrossRef] [PubMed]

Graves, E. E.

Gruber, J.

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80(4), 043701 (2009).
[CrossRef] [PubMed]

Holboke, M. J.

Hutchins, M.

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80(4), 043701 (2009).
[CrossRef] [PubMed]

D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
[CrossRef] [PubMed]

Hypnarowski, J.

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80(4), 043701 (2009).
[CrossRef] [PubMed]

Kepshire, D.

F. Leblond, H. Dehghani, D. Kepshire, and B. W. Pogue, “Early-photon fluorescence tomography: spatial resolution improvements and noise stability considerations,” J. Opt. Soc. Am. A 26(6), 1444–1457 (2009).
[CrossRef] [PubMed]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80(4), 043701 (2009).
[CrossRef] [PubMed]

Kepshire, D. S.

D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
[CrossRef] [PubMed]

Khayat, M.

D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
[CrossRef] [PubMed]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80(4), 043701 (2009).
[CrossRef] [PubMed]

Krishnaswamy, V.

B. W. Pogue, F. Leblond, V. Krishnaswamy, and K. D. Paulsen, “Radiologic and near-infrared/optical spectroscopic imaging: where is the synergy?” AJR Am. J. Roentgenol. 195(2), 321–332 (2010).
[CrossRef] [PubMed]

Lasser, T.

T. Lasser and V. Ntziachristos, “Optimization of 360° projection fluorescence molecular tomography,” Med. Image Anal. 11(4), 389–399 (2007).
[CrossRef] [PubMed]

Leblond, F.

F. Leblond, S. C. Davis, P. A. Valdés, and B. W. Pogue, “Pre-clinical whole-body fluorescence imaging: Review of instruments, methods and applications,” J. Photochem. Photobiol. B 98(1), 77–94 (2010).
[CrossRef] [PubMed]

B. W. Pogue, F. Leblond, V. Krishnaswamy, and K. D. Paulsen, “Radiologic and near-infrared/optical spectroscopic imaging: where is the synergy?” AJR Am. J. Roentgenol. 195(2), 321–332 (2010).
[CrossRef] [PubMed]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80(4), 043701 (2009).
[CrossRef] [PubMed]

F. Leblond, H. Dehghani, D. Kepshire, and B. W. Pogue, “Early-photon fluorescence tomography: spatial resolution improvements and noise stability considerations,” J. Opt. Soc. Am. A 26(6), 1444–1457 (2009).
[CrossRef] [PubMed]

D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
[CrossRef] [PubMed]

Mincu, N.

D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
[CrossRef] [PubMed]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80(4), 043701 (2009).
[CrossRef] [PubMed]

Ntziachristos, V.

O’Hara, J. A.

D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
[CrossRef] [PubMed]

Paulsen, K. D.

B. W. Pogue, F. Leblond, V. Krishnaswamy, and K. D. Paulsen, “Radiologic and near-infrared/optical spectroscopic imaging: where is the synergy?” AJR Am. J. Roentgenol. 195(2), 321–332 (2010).
[CrossRef] [PubMed]

S. C. Davis, B. W. Pogue, H. Dehghani, and K. D. Paulsen, “Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction,” J. Biomed. Opt. 10(5), 050501 (2005).
[CrossRef] [PubMed]

Pogue, B. W.

F. Leblond, S. C. Davis, P. A. Valdés, and B. W. Pogue, “Pre-clinical whole-body fluorescence imaging: Review of instruments, methods and applications,” J. Photochem. Photobiol. B 98(1), 77–94 (2010).
[CrossRef] [PubMed]

B. W. Pogue, F. Leblond, V. Krishnaswamy, and K. D. Paulsen, “Radiologic and near-infrared/optical spectroscopic imaging: where is the synergy?” AJR Am. J. Roentgenol. 195(2), 321–332 (2010).
[CrossRef] [PubMed]

D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
[CrossRef] [PubMed]

F. Leblond, H. Dehghani, D. Kepshire, and B. W. Pogue, “Early-photon fluorescence tomography: spatial resolution improvements and noise stability considerations,” J. Opt. Soc. Am. A 26(6), 1444–1457 (2009).
[CrossRef] [PubMed]

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80(4), 043701 (2009).
[CrossRef] [PubMed]

S. C. Davis, B. W. Pogue, H. Dehghani, and K. D. Paulsen, “Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction,” J. Biomed. Opt. 10(5), 050501 (2005).
[CrossRef] [PubMed]

Ripoll, J.

Srinivasan, S.

D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
[CrossRef] [PubMed]

Valdés, P. A.

F. Leblond, S. C. Davis, P. A. Valdés, and B. W. Pogue, “Pre-clinical whole-body fluorescence imaging: Review of instruments, methods and applications,” J. Photochem. Photobiol. B 98(1), 77–94 (2010).
[CrossRef] [PubMed]

Weissleder, R.

Yodh, A. G.

AJR Am. J. Roentgenol.

B. W. Pogue, F. Leblond, V. Krishnaswamy, and K. D. Paulsen, “Radiologic and near-infrared/optical spectroscopic imaging: where is the synergy?” AJR Am. J. Roentgenol. 195(2), 321–332 (2010).
[CrossRef] [PubMed]

J. Biomed. Opt.

S. C. Davis, B. W. Pogue, H. Dehghani, and K. D. Paulsen, “Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction,” J. Biomed. Opt. 10(5), 050501 (2005).
[CrossRef] [PubMed]

D. S. Kepshire, S. L. Gibbs-Strauss, J. A. O’Hara, M. Hutchins, N. Mincu, F. Leblond, M. Khayat, H. Dehghani, S. Srinivasan, and B. W. Pogue, “Imaging of glioma tumor with endogenous fluorescence tomography,” J. Biomed. Opt. 14(3), 030501 (2009).
[CrossRef] [PubMed]

J. Opt. Soc. Am. A

J. Photochem. Photobiol. B

F. Leblond, S. C. Davis, P. A. Valdés, and B. W. Pogue, “Pre-clinical whole-body fluorescence imaging: Review of instruments, methods and applications,” J. Photochem. Photobiol. B 98(1), 77–94 (2010).
[CrossRef] [PubMed]

Med. Image Anal.

T. Lasser and V. Ntziachristos, “Optimization of 360° projection fluorescence molecular tomography,” Med. Image Anal. 11(4), 389–399 (2007).
[CrossRef] [PubMed]

Med. Phys.

V. Ntziachristos and R. Weissleder, “Charge-coupled-device based scanner for tomography of fluorescent near-infrared probes in turbid media,” Med. Phys. 29(5), 803–809 (2002).
[CrossRef] [PubMed]

Opt. Lett.

Rev. Sci. Instrum.

D. Kepshire, N. Mincu, M. Hutchins, J. Gruber, H. Dehghani, J. Hypnarowski, F. Leblond, M. Khayat, and B. W. Pogue, “A microcomputed tomography guided fluorescence tomography system for small animal molecular imaging,” Rev. Sci. Instrum. 80(4), 043701 (2009).
[CrossRef] [PubMed]

Other

P. C. Hansen, Rank-Deficient and Discrete Ill-Posed Problems (SIAM, 1998).

Cited By

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

Alert me when this article is cited.


Figures (9)

Fig. 1
Fig. 1

Optical detection geometries evaluated for diffuse fluorescence tomography simulations. In the limit that the number of detectors, Nd , is large and the angle between adjacent detection points, θ, is small, wide-field detection is achieved.

Fig. 3
Fig. 3

(a) Simulated data vectors associated with the distribution of inclusions shown in (c) (right-most image) for which a 10:1 fluorescent contrast with respect to the background has been assigned. Two of the curves in (a) represent raw fluorescence data sets, with one curve being associated with an homogenous medium (10% stochastic noise added) and the other one being associated with an heterogeneous medium (no stochastic noise added) where different optical properties (absorption and reduced scattering coefficients) were associated with the different regions shown in (c) (left-most image). The third curved in (a) (normalized by multiplying with the homogenous transmittance data set) represents the fluorescence-to-transmission ratio data set. (b) Graphs showing the percentage difference between a fluorescence data set generated for a homogenous medium (0% noise) and the data sets shown in (a).

Fig. 2
Fig. 2

(a) Singular image (SI) vectors associated with increasing singular order. Mode images are plotted on a log-scale to convey the idea that increasing the order of the mode typically leads to spatial frequency increases. (b) Cross-section of the modes shown in (a) along the dotted line.

Fig. 5
Fig. 5

Figure showing the metric labeled mode resolution as a function of singular mode order. The resolution of each SI mode is defined here as the average distance (in mm) between two consecutive maxima along the dotted line shown in Fig. 2. Higher spatial frequency is therefore equivalent to smaller mode resolution. The red dots represent (for each geometry) the maximum mode order (minimum mode resolution) that can be used to reconstruct an image when 1% stochastic noise is added to the simulated data set (see Table 3). The dotted line should be used as a visual guide indicating the approximate location of the noise floor threshold for all geometries.

Fig. 6
Fig. 6

(a) Representative singular data (SD) vectors associated with imaging geometry IV (see Table 1). Inspection of the modes of order i = 1, i = 10 and i = 20 show that the frequency (as a function of the source-detector pair index label measurement number) increases as a function of the singular order i. (b) Frequency of SD modes as a function of singular mode order for the five imaging geometries presented in Table 1. The curves demonstrate that the frequency, Eq. (6), tends to increase monotonically within a certain range, but that phases of sudden decrease always occur for large values of i. Red dots on the curves represent the order from which modes cannot be used for reconstruction because of noise propagation (see Table 3).

Fig. 4
Fig. 4

Singular values as a function of the singular mode order for the source-detection geometries presented in Table 1. The red dots represent (for each geometry) the minimum singular value (maximum mode order) that can be used to reconstruct an image when 1% stochastic noise is added to the simulated data set (see Table 3 for numerical values). The dotted line should be used as a visual guide indicating the approximate location of the noise floor threshold for all geometries.

Fig. 7
Fig. 7

Images reconstructed from data simulated for different imaging geometries. Even minute levels of stochastic noise (0.001% - 1%) are shown to significantly degrade image quality for all detection geometries. The simulation target image used to generate the synthetic data is that shown in Fig. 3c. Images are scaled with respect to the maximum value of the target image.

Fig. 8
Fig. 8

Images illustrating the impact of low and high frequency noise on fluorescence images reconstructed using a model assuming homogeneous absorption and scattering values for two different imaging geometries (Geometries II and V). The first column shows images where the forward model and reconstruction models were the same and no noise has been added to the synthetic data. The images found in the next three columns illustrate how different sources of noise degrade image quality: 1% stochastic noise added to the data vector (2nd column), 1% noise added plus low-frequency noise due to absorption heterogeneities in Table 2 (3rd column), same as column 3 but data vector used to reconstruct consists in fluorescence-to-transmission (F/T) ratio (column 4).

Fig. 9
Fig. 9

Different simulation scenarios where FT is used to monitor the progress of a tumor with center-of-mass the same as top inclusion shown on the fluorescence image in Fig. 3c. Reconstruction results are presented as a cross-section of the corresponding tomography image through a horizontal line across the center-of-mass of the tumor. Scenario A shows a tumor of constant contrast-to-background ratio (10:1) but with decreasing size, Scenario B shows a tumors of decreasing contrast but with constant size, Scenario C shows tumor stages where both size and contrast vary. For each scenario, four tumor stages are shown and each image is reconstructed with three different imaging configurations, namely geometries I, II, V (Table 1).

Tables (4)

Tables Icon

Table 1 Technical specifications associated with the five representative imaging geometries for which the performance is compared. The last column in the table shows the numerical rank of the forward model fluorescence matrix associated with the geometries. The number of projections is in reference to the system described in Section 2.1, and corresponds to the number of laser positions used (manipulated by rotating the gantry). In other words, the number of measurements per 2D slice is equal to the number of projections times the number of detectors.

Tables Icon

Table 2 Optical properties (absorption and reduced scattering coefficients) used for the light transport simulations presented in this work

Tables Icon

Table 3 Tabulation of the number of singular modes that can be used to reconstruct images in situations where different levels of noise and model mismatch are added to simulated tomography data sets. In cases where no noise is added, all singular modes (100% of rank value) can be used to reconstruct an image. In situations where noise is added there is a dramatic decrease in the number of useful singular modes.

Tables Icon

Table 4 Figures of merit used to assess a priori and a posteriori performance of the imaging geometries presented in Table 1. For each set of figures of merit, a ranking is provided for geometries I-V starting with the one for which a figure of merit predicts the optimal performance.

Equations (8)

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

D ρ = a = 1 N v A ρ a C a    ρ = 1 , 2 , , N m ,
A ρ a ( r s , r d ; r a ) = Q F ε F τ Φ x ( r s , r a ) Φ e ( r a , r d ) ,
A = U Σ V T = i = 1 N v d i s i c i T ,
C a = i = 1 N N r a n k F i d i T D s i ( c a ) i ,
K i = d i T D s i ,
Mode resolution (mm)  = ( N e x S I L ) 1 .
Mode frequency  = N e x S D N m .
D = D homo + D high-f + D low-f ,

Metrics