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Optics Express

Optics Express

  • Editor: Michael Duncan
  • Vol. 13, Iss. 7 — Apr. 4, 2005
  • pp: 2263–2275
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Time Domain Fluorescent Diffuse Optical Tomography: analytical expressions

S. Lam, F. Lesage, and X. Intes  »View Author Affiliations


Optics Express, Vol. 13, Issue 7, pp. 2263-2275 (2005)
http://dx.doi.org/10.1364/OPEX.13.002263


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Abstract

Light propagation in tissue is known to be favored in the Near Infrared spectral range. Capitalizing on this fact, new classes of molecular contrast agents are engineered to fluoresce in the Near Infrared. The potential of these new agents is vast as it allows tracking non-invasively and quantitatively specific molecular events in-vivo. However, to monitor the bio-distribution of such compounds in thick tissue proper physical models of light propagation are necessary. To recover 3D concentrations of the compound distribution, it is necessary to perform a model based inverse problem: Diffuse Optical Tomography. In this work, we focus on Fluorescent Diffuse Optical Tomography expressed within the normalized Born approach. More precisely, we investigate the performance of Fluorescent Diffuse Optical Tomography in the case of time resolved measurements. The different moments of the time point spread function were analytically derived to construct the forward model. The derivation was performed from the zero order moment to the second order moment. This new forward model approach was validated with simulations based on relevant configurations. Enhanced performance of Fluorescent Diffuse Optical Tomography was achieved using these new analytical solutions when compared to the current formulations.

© 2005 Optical Society of America

1. Introduction

Optical techniques based on the Near-Infrared (NIR) spectral window have made significant progress in biomedical research in recent years. The relative low absorption and low scattering in the 600–1000 nm spectral range allow detection of photons that have traveled through several centimeters of biological tissue [1

1. A Yodh and B. Chance, “Spectroscopy and imaging with diffusing light,” Phys. Today 48, 34–40 (1995). [CrossRef]

]. Coupled with accurate models of light propagation, NIR techniques enable imaging of deep tissue with boundary measurements using non-ionizing, low dose radiation.

The interest in NIR techniques is fueled by the ability of the techniques to monitor functional tissue parameters such as oxy- and deoxy-hemoglobin [2

2. X. Intes and B. Chance, “Non-PET Functional Imaging Techniques Optical,” Clin. No. Am. 43, 221–234 (2005).

,3

3. F. Jobsis, “Noninvasive infrared monitoring of cerebral and myocardial sufficiency and circulatory parameters,” Science 198, 1264–1267 (1977). [CrossRef] [PubMed]

] and the development of appropriate low cost instrumentation [4

4. Y. Lin, G. Lech, S. Nioka, X. Intes, and B. Chance, “Noninvasive, low-noise, fast imaging of blood volume and deoxygenation changes in muscles using light-emitting diode continuous-wave imager,” Rev. Sci. Instrum. 73, 3065–3074 (2002). [CrossRef]

]. Based on these qualities, NIR optical imaging is expected to play a key role in breast cancer detection, characterization [6

6. B. Tromberg, N. Shah, R. Lanning, A. Cerussi, J. Espinoza, and T. Pham, et al., “Non-invasive in vivo characterization of breast tumors using photon migration spectroscopy,” Neoplasia 2, 26–40 (2000). [CrossRef] [PubMed]

11

11. X. Intes, S. Djeziri, Z. Ichalalene, N. Mincu, Y. Wang, P. St. -Jean, F. Lesage, D. Hall, D. A. Boas, and M. Polyzos, “Time-Domain Optical Mammography Softscan®: Initial Results on Detection and Characterization of Breast Tumors”, Proc. SPIE 5578, 188–197 (2004). [CrossRef]

] and monitoring through therapy [12

12. D. B. Jakubowski, A. E. Cerussi, F. Bevilacqua, N. Shah, D. Hsiang, J. Butler, and B. J. Tromberg, “Monitoring neoadjuvant chemotherapy in breast cancer using quantitative diffuse optical spectroscopy: a case study,” J Biomed Opt. 9, 230–238 (2004). [CrossRef] [PubMed]

]; brain functional imaging [13

13. G. Strangman, D. A. Boas, and J. Sutton, “Non-invasive neuroimaging using Near-Infrared light,” Biol. Psychiatry 52, 679–693 (2002). [CrossRef] [PubMed]

,] and stroke monitoring [15

15. M. Stankovic, D. Maulik, W. Rosenfeld, P. Stubblefield, A. Kofinas, and E. Gratton, et al., “Role of frequency domain optical spectroscopy in the detection of neonatal brain hemorrhage- a newborn piglet study,” J. Matern. Fetal Med. 9, 142–149 (2000). [CrossRef] [PubMed]

,16

16. J. C. Hebden, A. Gibson, T. Austin, R. M. Yusof, N. Everdell, D. T. Delpy, S. R. Arridge, J. H. Meek, and J. S. Wyatt, “Imaging changes in blood volume and oxygenation in the newborn infant brain using three-dimensional optical tomography,” Phys Med Biol. 49, 1117–1130 (2004). [CrossRef] [PubMed]

]; muscle physiological and peripheral vascular disease imaging [17

17. V. Quaresima, R. Lepanto, and M. Ferrari, “The use of near infrared spectroscopy in sports medicine,” J. Sports Med. Phys. Fitness 43, 1–13 (2003). [PubMed]

]. For all these applications, NIR techniques rely on endogenous contrast such as tissue hemodynamics. Another potential application of NIR techniques is to monitor exogenous contrast [18

18. X. Intes, J. Ripoll, Y. Chen, S. Nioka, A. G. Yodh, and B. Chance, “In vivo continuous-wave optical breast imaging enhanced with Indocyanine Green,” Med. Phys. 30, 1039–1047 (2003). [CrossRef] [PubMed]

]. Especially, we see the emergence of an optical molecular imaging field that bears great promises in clinical applications [19

19. R. Weissleder and U. Mahmood, “Molecular imaging,” Radiology 219, 316–333 (2001). [PubMed]

].

NIR fluorescence optical imaging is rapidly evolving as a new modality to monitor functional and/or molecular events in either human or animal tissue. The developments of new contrast agents that target specific molecular events [20

20. J. V. Frangioni, “In vivo near-infrared fluorescence imaging,” Curr. Opin. Chem. Biol. 7, 626–634 (2003). [CrossRef] [PubMed]

,21

21. K. Licha, “Contrast agents for optical imaging,” Topics in Current Chemistry 222, 1–29 (2002). [CrossRef]

] are particularly promising. By specifically binding [23

23. S. Achilefu, R. Dorshow, J. Bugaj, and R. Rajagopalan, “Novel receptor-targeted fluorescent contrast agents for in-vivo tumor imaging,” Invest. Radiol. 35, 479–485 (2000). [CrossRef] [PubMed]

,24

24. Y. Chen, G. Zheng, Z. Zhang, D. Blessington, M. Zhang, and H. Li, et al., “Metabolism Enhanced Tumor Localization by Fluorescence Imaging: In Vivo Animal Studies,” Opt. Lett. 28, 2070–2072 (2003). [CrossRef] [PubMed]

] or being activated in tumors [25

25. R. Weissleder, C. H. Tung, U. Mahmood, and A. Bogdanov, “In vivo imaging with protease-activated near-infrared fluorescent probes,” Nat. Biotech. 17, 375–378 (1999). [CrossRef]

], molecular probe detection can be achieved in the early stages of molecular changes prior to structural modification [26

26. R. Weinberg, “How Does Cancer Arise,” Sci. Am. 275, 62–71 (1996). [CrossRef] [PubMed]

]. Moreover, the endogenous fluorescence in the NIR spectral window is weak leading to good fluorescence sensitivity [27

27. X. Intes, Y. Chen, X. Li, and B. Chance, “Detection limit enhancement of fluorescent heterogeneities in turbid media by dual-interfering excitation,” Appl. Opt. 41, 3999–4007 (2002). [CrossRef] [PubMed]

].

As of today, NIR molecular imaging is confined to small animal models [28

28. J. Lewis, S. Achilefu, J. R. Garbow, R. Laforest, and M. J. Welch, “Small animal imaging: current technology and perspectives for oncological imaging,” Eur. J.o Cancer 38, 2173–88 (2002). [CrossRef]

] and the translation to human imaging is foreseen as imminent. However, the technical problems encountered in imaging larger tissue volumes are challenging. Besides sensitive instrumentation, robust and accurate models for fluorescent light propagation are needed. Tomographic algorithms in the continuous mode [29

29. V. Ntziachristos and R. Weissleder, “Experimental three-dimensional fluorescence reconstruction of diffuse media by use of a normalized Born approximation,” Opt. Lett. 26, 893–895 (2001). [CrossRef]

] and in the frequency domain [30

30. M. J. Eppstein, D. J. Hawrysz, A. Godavarty, and E. M. Sevick-Muraca, “Three-dimensional, Bayesian image reconstruction from sparse and noisy data sets: Near-infrared fluorescence tomography,” Proc. Nat. Acad. Sci. Am. 99, 9619–9624 (2002). [CrossRef]

,31

31. A. B. Milstein, J.J. Stott, S. Oh, D. A. Boas, R. P. Millane, C. A. Bouman, and K. J. Webb, “Fluorescence optical diffusion tomography using multiple-frequency data,” J. Opt. Soc. Am. A 21, 1035–1049 (2004). [CrossRef]

] have been proposed. Both numerical and analytical models exist and have been applied successfully to experimental data.

In this work, we propose an analytical fluorescent diffuse optical algorithm extended to the time domain. For this purpose, we derive analytical solutions of the heterogeneous fluorescent diffusion equation for the 0th, the 1st and the 2nd moments of the fluorescent time point spread function (FTPSF). The formalism of the fluorescent normalized Born approximation [29

29. V. Ntziachristos and R. Weissleder, “Experimental three-dimensional fluorescence reconstruction of diffuse media by use of a normalized Born approximation,” Opt. Lett. 26, 893–895 (2001). [CrossRef]

] is used to obtain the mathematical expressions of the forward model employed for the diffuse optical tomography (DOT) procedure. The algorithm is tested with synthetic data mimicking relevant breast geometry. These simulations highlight the advantage of incorporating higher moments in the fluorescent DOT problem.

2. Theory

In this section we describe the theoretical framework of light propagation in biological tissue and the specific theoretical frame we used to obtain the analytical solutions of the fluorescent time domain diffusion equation.

2.1. Light propagation in tissue

Light propagation in tissue is well modeled by the diffusion equation. In the time domain the mathematical expression modeling light propagation in a homogenous medium is:

1vtΦ(r,t)DΔ2Φ(r,t)+μaΦ(r,t)=S(r,t)
(1)

where Φ(r, t) is the photon fluence rate, D is the diffusion coefficient expressed as D=1/3µ′s with µ′s being the reduced scattering coefficient, µa is the linear absorption coefficient, v is the speed of light in the medium and S(r, t) is the source term (assumed to be a δ function in our case).

From Eq. (1), we can estimate the value of the field in each position in the investigated medium. In turn, the knowledge of the value of the field locally allows modeling accurately the reemission of a fluorescent field by endogenous or exogenous markers. Indeed, the fluorescent field is due to excited molecules that reemit photons at a constant wavelength. This phenomenon of reemission can be modeled as a source term embedded in the medium. The propagation from these sources to the detector is then modeled in the same frame as in Eq. (1). The temporal behavior of the excited fluorophore population at a given point is expressed by [32

32. X. Li, “Fluorescence and diffusive wave diffraction tomographic probes in turbid media,” PhD University of Pennsylvania (1996).

]:

tNex(r,t)=1τNex(r,t)+σ·Φλ1(r,t)[Ntot(r,t)2Nex(r,t)]
(2)

where Nex(r, t) is the concentration of excited molecules at position r and time t, Ntot(r, t) is the concentration of total molecules of fluorophores (excited or not), τ is the radiative lifetime of the fluorescent compound (sec. or nanoseconds.), σ is the absorption cross section of the fluorophore (cm2) and Φλ1(r, t) is the photon fluence rate (Nb photons.s-1.cm-2) at the excitation wavelength λ1. Considering that the number of excited molecule is low compared to the total molecules and taking the Fourier transform yield the expression for the concentration of excited molecules:

Nex(r,ω)τ=σ·Ntot(r)1iωτ·Φλ1(r,ω)
(3)

where ω1 is the angular frequency associated with t.

Then, the total fluorescent field is the sum of the contributions of all the secondary fluorescent sources over the entire volume. In the case of a point source located at rs, the fluorescent field detected at a position r d is modeled by:

Φλ2(rs,rd,ω)=ηvolumeNex(r,ω)·Φλ2(r,rd,ω)·d3r
(4)

where Φλ2(r, r d, ω) represent a propagation term of the fluorescent field from the element of volume at r to the detector position r d et the reemission wavelength λ2. Then, by using Eq. (4) we obtain the complete expression:

Φλ2(rs,rd,ω)=volumeΦλ1(rs,r,ω)·Qeff·Ntot(r)1iωτ·Φλ2(r,rd,ω)·d3r
(5)

where Qeff=q·η.σ is the quantum efficiency, product of q the quenching factor, η the quantum yield and σ the absorption cross section of the fluorophore, Φλ1(r, t) is the photon fluence rate at the excitation wavelength λ1 and at time t, and τ is the radiative lifetime of the fluorescent compound. Note that the product σ·Ntot(r) corresponds to the absorption coefficient of the fluorochrome and can be expressed also as ε·Ctot(r) where ε is the extinction coefficient of the fluorophore (cm-1. Mol-1) and Ctot(r) the concentration of the fluorochrome (Mol) at a position r.

2.2. Fluorescent moment analytical expression.

Following the derivation of Eq. (5) performed by O’Leary [33

33. M. O’Leary, “Imaging with diffuse photon density waves,” PhD University of Pennsylvania (1996).

], Ntziachristos and Weissleder [29

29. V. Ntziachristos and R. Weissleder, “Experimental three-dimensional fluorescence reconstruction of diffuse media by use of a normalized Born approximation,” Opt. Lett. 26, 893–895 (2001). [CrossRef]

] proposed an approach to fluorescent diffuse optical tomography less susceptible to system source-detector couplings and tissue inhomogeneities. They cast the forward model in the frame of the normalized first order Born approximation that is mathematically expressed as:

Φλ2(rs,rd,ω)Φ0λ1(rs,rd,ω)=1Φ0λ1(rs,rd,ω)volumeΦ0λ1(rs,r,ω)·Qeff·Ctot(r)1iωτ·Φ0λ2(r,rd,ω)·d3r
(6)

The difference between Eq. (5) and (6) resides in the normalization achieved with the homogeneous excitation field reaching the detector. The gain attained here is that the left hand side can be determined purely from measurements thereby canceling any source-detector couplings. Following the expression of M. O’Leary [33

33. M. O’Leary, “Imaging with diffuse photon density waves,” PhD University of Pennsylvania (1996).

], this expression is used to construct the forward model for DOT and then the analytical expression of the weight function is:

Φλ2(rs,rd,ω)Φ0λ1(rs,rd,ω)=Dλ1Gλ1(rs,rd,ω)voxels1Dλ1Gλ1(rs,rv,ω)·Qeff·Ctot(rv)1iωτ·1Dλ2Gλ2(rv,rd,ω)·h3
(7)

where Gλj(r1,r2,ω)=e(ikjr1r2)r1r2 is the system’s Green’s function with kj2=(-vμaλj+iω)/Dλj, at the considered wavelength λj∈[λ1, λ2].

Fig. 1. Typical TPSF and respective moments. The IFS curve corresponds to a typical instrument response function.

The Eq. (7) is defined in the frequency domain. We propose to find similar analytical expressions in the time domain. Such analytical solutions for the absorption case have been proposed in the past for the 0th, 1st and 2nd moment of the TPSF [34

34. E. Hillman, “Experimental and theoretical investigations of near infrared tomographic imaging methods and clinical applications,” PhD University College London (2002).

]. The correspondence of these moments to the TPSF is illustrated in Fig. 1. The 0th moment corresponds to the integration of the counts (equivalent to the continuous mode), the 1st moment corresponds to the mean time of arrival of the photon and the 2nd moment to the variance of arrival of the photon.

The normalized moments of order k>1 of a distribution function p(t) are defined by [35

35. A. Liebert, H. Wabnitz, D. Grosenick, M. Moller, R. Macdonald, and H. Rinnerberg, “Evaluation of optical properties of highly scattering media by moments of distributions of times of flight of photons,” Appl. Opt. 42, 5785–5792 (2003). [CrossRef] [PubMed]

]:

mk=tk=+tk·p(t)dt+p(t)dt
(8)

In our notations, for k=0, we use the simple integration without division. We employed this formalism in the case of the normalized first order born approximation. Hence the normalized 0th moment is expressed as :

m0λ2(rs,rd)=ΦNλ2(rs,rd,ω=0)=voxelsGλ1(rs,rv,ω=0)·Gλ2(rv,rd,ω=0)Gλ1(rs,rd,ω=0)×Qeffh3Dλ2×Ctot(rv)
(9)

This expression corresponds to Eq. (7) for the continuous mode. From now on, we assume that the excitation and emission wavelengths are similar (λ12=λ), i.e. typically the shift incurred under the fluorescent process will not be significant and the same propagator describes the propagation of light for both excitation and emission. Then normalizing the 1st and the 2nd moment to this first moment yields the analytical solutions: Normalized 1st moment

m0λ2(rs,rd)×m1λ2(rs,rd)=voxels{(τ+rsrv+rsrd2.vμaλDλrvrd2.vμaDλ)×Gλ(rs,rv,ω=0)·Gλ(rv,rd,ω=0)Gλ(rs,rd,ω=0)×Qeffh3Dλ×Ctot(rv)}
(10)

Normalized 2nd moment

m0λ2(rs,rd)·m2λ2(r2,rd)=voxels{(τ2+rsrv+rsrd4.v2μaλμaλDλ+rvrd4.v2μaλμaλDλ+{τ+rsrv2.vμaλDλ+rvrd2.vμaλDλ}2tλ2(rs,rd)·{τ+rsrv+rvrd2.vμaλDλrvrd2.vμaλDλ})×Gλ(rs,rv,ω=0)·Gλ(rv,rd,ω=0)Gλ(rs,rd,ω=0)×Qeffh2Dλ×Ctot(rv)}
(11)

Where t-λ2(rs,rd) corresponds to the fluorescent mean time for the particular source-detector pair considered. For simplicity, we do not present in this paper the full derivation of these analytical solutions.

The fluorescent DOT problem in the time domain is based on the analytical expression derived above and summarized in the set of linear equations:

m0λ2(rs1,rd1)m0λ2(rsm,rdm)m0λ2(rs1,rd1)·m1λ2(rs1,rd1)m0λ2(rsm,rdm)·m1λ2(rsm,rdm)m0λ2(rs1,rd1)·m2λ2(rs1,rd1)m0λ2(rsm,rdm)·m2λ2(rsm,rdm)=W11m0λ2Wlnm0λ2Wmlm0λ2Wmnm0λ2W11m0λ2·m1λ2Wlnm0λ2·m1λ2Wm1m0λ2·m1λ2Wmnm0λ2·m1λ2W11m0λ2·m2λ2Wlnm0λ2·m2λ2Wm1m0λ2·m2λ2Wmnm0λ2·m2λ2·Ctot(rvl)Ctot(rvn)
(12)

where Wijm0λ2, Wijm0λ2·m1λ2 and Wijm0λ2·m2λ2, the weight function for the ith source-detector pair and the jth voxel are directly derived respectively from Eqs. (9), (10) and (11). In this inverse problem, the object function is defined as the fluorophore concentration. For the cases presented herein, we implemented boundary conditions using the extrapolated boundary conditions [36

36. R. C. Haskell, L. O. Svaasand, T. Tsay, T. Feng, M. S. McAdams, and B. J. Tromberg, “Boundary conditions for the diffusion equation in radiative transfer,” J. Opt. Soc. Am A 11, 2727–41 (1994). [CrossRef]

] and image sources.

2.3. Inverse problem

Many different approaches exist to tackle the inverse problem. In this work we choose to employ the algebraic reconstruction technique (ART) due to its modest memory requirements for large inversion problems and the calculation speed it attains.

Algebraic techniques are well known and broadly used in the biomedical community [37

37. R. Gordon, R. Bender, and G. Herman, “Algebraic reconstruction techniques (ART) for the three dimensional electron microscopy and X-Ray photography,” J. Theoret. Biol. 69, 471–482 (1970). [CrossRef]

]. These techniques operate on a system of linear equations such as the ones seen in Eq. (12). We can rewrite Eq. (12) as:

b=A·x
(13)

where b is a vector holding the measurements for each source-detector pair, A is the matrix of the forward model (weight matrix), and x is the vector of unknowns (object function). ART solves this linear system by sequentially projecting a solution estimate onto the hyperplanes defined by each row of the linear system. The technique is used in an iterative scheme and the projection at the end of the kth iteration becomes the estimate for the (k+1)th iteration. This projection process can be expressed mathematically as [38

38. A. Kak and M. Slaney, “Computerized tomographic Imaging”, IEEE Press, N-Y (1987).

]:

xj(k+1)=xj(k)+ξbiiaijxj(k)iaijaijiaij
(14)

where xj(k) is the kth estimate of jth element of the object function, bi the ith measurement, aij the i-jth element of the weight matrix A and ξ the relaxation parameter.

2.3. Simulations

We tested the formulation derived in Section 2.2 with simulations. First we constructed a synthetic phantom with parameters relevant to the softly compressed human breast in dimension (6cm thickness) and for the optical endogenous properties. Second we simulated a homogeneous fluorochrome distribution over the volume with 1 cm3 heterogeneities exhibiting a contrast of 10 in concentration. The different parameters of the simulations are provided in Table 1.

The fluorescent signal is dependent on the intrinsic characteristics of the fluorochrome employed. Simulations were carried out with three representative compounds: Cy 7, Cy 5.5 and Cy 3B. These fluorochromes were selected due to the span of lifetimes they do exhibit, which is characteristic of cyanine dyes [22

22. G. Zheng, Y. Chen, X. Intes, B. Chance, and J. Glickson, “Contrast-Enhanced NIR Optical Imaging for subsurface cancer detection,” J. Porphyrin and Phthalocyanines 8, 1106–1118 (2004). [CrossRef]

]. The different properties of these fluorochromes are provided in Table 2.

Table 1. Parameters used in the simulations

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Fig. 2. Configuration used for the simulations herein. The source (detectors) locations are depicted by red (blue) dots

The synthetic phantom was probed with a 25×25 constellation of source detectors. This constellation was distributed evenly 1.5 cm apart in both dimensions. The phantom configuration is provided in Fig. 2.

Table 2. Fluorochrome investigated herein.

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2.3. Noise model

Higher order moments are sensitive to noise. Thus, it is important to investigate the performance of the algorithm in the presence of noise. Analytical noise models exist for the intrinsic NIR higher moments for homogeneous cases [35

35. A. Liebert, H. Wabnitz, D. Grosenick, M. Moller, R. Macdonald, and H. Rinnerberg, “Evaluation of optical properties of highly scattering media by moments of distributions of times of flight of photons,” Appl. Opt. 42, 5785–5792 (2003). [CrossRef] [PubMed]

]. However, the derivation of the same analytical model for tomographic purposes is overly complex. We decided thus to employ a heuristically derived noise model.

We generated synthetic homogeneous TPSF and considered a Poisson noise of the temporal distribution of photon time of flights. The TPSF was normalized at 500 counts at the maximum bin mimicking real acquisition scenarios. From the noised TPSF, we estimated one set of energy, meantime and variance. The same estimation was performed over 1,000 trials. The statistics of these estimates were used as our noise model. An example of noisy moments value distribution is given in Fig. 3.

Fig. 3. Results of repartition of energy, meantimes and variance of 1,000 randomly generated noised TPSF.

Gaussian distribution approximated the noise model. The different values of the noise model employed for the three moments evaluated herein is given in Table 3.

Table 3. Noise model used. The standard deviations are expressed in percent of the mean.

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3. Results

In this section we present preliminary results concerning fluorescent DOT based on the formulation by normalized moments.

3.1. Sensitivy matrix

The pattern described by the photon migration is often referred as a banana shape. Especially, in the case of continuous mode, the measurements are highly sensitive to the surface. Such dependence of the data type can be visualized through the mapping of the sensitivity matrix. Indeed, each line of the linear system described in Eq. (12) represents the sensitivity to a local perturbation for the correspondent source-detector pair. Thus by mapping this local dependence, we render the spatial sensitivity of this particular source-detector pair for this specific configuration and specific data type.

We propose in Fig. 4 some examples of sensitivity matrices for the transmittance case. We limited ourselves to depict slices across the discrete volume, but by construction, the banana shapes are in 3D. The optical and fluorochrome properties characterizing this medium are provided in Table 1 and Table 2.

Fig. 4. Example of sensitivity matrices. a) and b) correspond respectively to m0λ2(r s,r d) and m0λ2(r s,r dm2λ2(r s,r d) for a 6cm thick slab with source-detector facing each other and a 0.1 µM background of Cy 7. c) and d) correspond to the same parameters for a 0.1 µM background of Cy 5.5. Last, e) and f) correspond to the same parameters for a 0.1 µM background of Cy 3B.

The examples in Fig. 4 underline interesting features of the time domain moment fluorescent DOT. First, as seen in Figs. 4(a),–(c) and (e), the normalized 0th order Born approximation in continuous mode is highly sensitive to surface voxels. This is a well-known behavior that is both present in absorption and fluorescent mode. This also demonstrates the poor sensitivity of planar fluorescent techniques to deep fluorescent inclusions due to overwhelming dependence on surface interactions.

3.2. Reconstructions

One should note that the background fluorochrome concentration is non-negligible. This background simulates non-perfect compound uptake/trapping and represents a challenging case for all FDOT approaches.

We use then the formulation of (12) to generate synthetic measurements from the phantom. We simulated a 25 source and 25 detectors array as described in Fig. 2(a). The value of the fluorescent mean time and the fluorescent variance were evaluated to be around ~3 ns and 1 ns respectively. These values are in agreement with expected values for real cases. In this simulation no noise was added. The reconstructions obtained by constrained ART are provided in Fig. 5. We propose in this figure the reconstructions based on the 0th normalized moment of the fluorescent TPSF and with the combined three normalized moments.

In all three cases presented, the inclusions were successfully reconstructed. Their locations were well retrieved and the objects clearly discriminated from the background. However, we can notice some differences in terms of reconstruction quality between the three different inverse problems solved. Especially, in the case using only the 0th normalized moment of the fluorescent TPSF, the reconstructions exhibit strong artifacts on the boundary, artifacts that scale with the reconstructed heterogeneity. On the other hand the reconstructions based on the three moments combined (as reconstructions based on the 2nd normalized fluorescent moment solely; results not shown here) do not exhibit such strong surface artifacts. In this last case, the homogenous background fluorophore is more accurately reconstructed.

Last, the reconstructions based on the three different compounds are very similar when using only the 0th normalized moment. However, and as expected from Section 3.1, the reconstructions employing the 2nd normalized moment exhibit different performances. In the case of relatively short lifetimes, i.e., Cy 7and Cy 5.5, the reconstructions are similar and provide accurate recovery of the three heterogeneities. However, in the case of longer lifetime, i.e., Cy 3B, even though, the reconstructions are far superior when using the 3 moments simultaneously in the inverse problem, the objects are less well defined. This fact is linked to the close similarity between the spatial distributions of the 0th normalized and the 2nd normalized fluorescent moments. One should note also that the constellation of source-detector selected herein is quite sparse and such reconstructed structure is expected as seen in Ref [41

41. E. Graves, J. 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, 231–241 (2004). [CrossRef]

].

Fig. 5. Reconstruction from synthetic data for Cy 7: a) 0th moment only, b) 0th, 1st and 2nd moments; Cy 5.5 : c) 0th moment only, d) 0th, 1st and 2nd moments; and Cy 3B: e) 0th moment only, f) 0th, 1st and 2nd moments. The quantitative values are expressed in µM.

3.2. Noisy reconstructions

The noise model described in section 2.3 was applied to the measurements for the Cy 5.5 case. The reconstructions based on this noisy simulation are provided in Fig. 6. We restricted the reconstruction to the Cy 5.5 case only for conciseness.

Fig. 6. Reconstruction from synthetic data for Cy 5.5 using all three moments noisy data.

As one can see, the algorithm is still performing satisfactorily in the case of noise. Even though the 2nd normalized moments are sensitive to noise, the incorporation of this information benefits the inverse problem. The objects are reconstructed with fidelity and the surface artifacts are still minimized due to the inherent spatial information of the 2nd normalized moment.

4. Conclusion

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Y. Lin, G. Lech, S. Nioka, X. Intes, and B. Chance, “Noninvasive, low-noise, fast imaging of blood volume and deoxygenation changes in muscles using light-emitting diode continuous-wave imager,” Rev. Sci. Instrum. 73, 3065–3074 (2002). [CrossRef]

5.

Y. Chen, C. Mu, X. Intes, D. Blessington, and B. Chance, “Frequency domain phase cancellation instrument for fast and accurate localization of fluorescent heterogeneity,” Rev. Sci. Instrum. 74, 3466–3473 (2003). [CrossRef]

6.

B. Tromberg, N. Shah, R. Lanning, A. Cerussi, J. Espinoza, and T. Pham, et al., “Non-invasive in vivo characterization of breast tumors using photon migration spectroscopy,” Neoplasia 2, 26–40 (2000). [CrossRef] [PubMed]

7.

D. Grosenick, H. Wabnitz, K. Moesta, J. Mucke, M. Moller, C. Stroszczunski, J. Stobel, B. Wassermann, P. Schlag, and H. Rinnerberg, “Concentration and oxygen saturation of haemoglobin of 50 breast tumours determined by time-domain optical mammography,” Phys Med. Biol. 49, 1165–1181 (2004). [CrossRef] [PubMed]

8.

H. Jiang, N. Iftimia, J. Eggert, L. Fajardo, and K. Klove, “Near-infrared optical imaging of the breast with model-based reconstruction,” Acad. Radiol. 9, 186–194 (2002). [CrossRef] [PubMed]

9.

M. Franceschini, K. Moesta, S. Fantini, G. Gaida, E. Gratton, and H. Jess, et al., “Frequency-domain techniques enhance optical mammography: Initial clinical results,” Proc. Nat. Acad. Sci. Am. 94, 6468–6473 (1997). [CrossRef]

10.

S. Colak, M. van der Mark, G. Hooft, J. Hoogenraad, E. van der Linden, and F. Kuijpers, “Clinical optical tomography and NIR spectroscopy for breast cancer detection,” IEEE J. Sel. Top. Quatum Electron. 5, 1143–1158 (1999). [CrossRef]

11.

X. Intes, S. Djeziri, Z. Ichalalene, N. Mincu, Y. Wang, P. St. -Jean, F. Lesage, D. Hall, D. A. Boas, and M. Polyzos, “Time-Domain Optical Mammography Softscan®: Initial Results on Detection and Characterization of Breast Tumors”, Proc. SPIE 5578, 188–197 (2004). [CrossRef]

12.

D. B. Jakubowski, A. E. Cerussi, F. Bevilacqua, N. Shah, D. Hsiang, J. Butler, and B. J. Tromberg, “Monitoring neoadjuvant chemotherapy in breast cancer using quantitative diffuse optical spectroscopy: a case study,” J Biomed Opt. 9, 230–238 (2004). [CrossRef] [PubMed]

13.

G. Strangman, D. A. Boas, and J. Sutton, “Non-invasive neuroimaging using Near-Infrared light,” Biol. Psychiatry 52, 679–693 (2002). [CrossRef] [PubMed]

14.

Y. Chen, D. Tailor, X. Intes, and B. Chance, “Quantitative correlation between Near-Infrared spectroscopy (NIRS) and magnetic resonance imaging (MRI) on rat brain oxygenation modulation,” Phys. Med. Biol. 48, 417–427 (2003). [CrossRef] [PubMed]

15.

M. Stankovic, D. Maulik, W. Rosenfeld, P. Stubblefield, A. Kofinas, and E. Gratton, et al., “Role of frequency domain optical spectroscopy in the detection of neonatal brain hemorrhage- a newborn piglet study,” J. Matern. Fetal Med. 9, 142–149 (2000). [CrossRef] [PubMed]

16.

J. C. Hebden, A. Gibson, T. Austin, R. M. Yusof, N. Everdell, D. T. Delpy, S. R. Arridge, J. H. Meek, and J. S. Wyatt, “Imaging changes in blood volume and oxygenation in the newborn infant brain using three-dimensional optical tomography,” Phys Med Biol. 49, 1117–1130 (2004). [CrossRef] [PubMed]

17.

V. Quaresima, R. Lepanto, and M. Ferrari, “The use of near infrared spectroscopy in sports medicine,” J. Sports Med. Phys. Fitness 43, 1–13 (2003). [PubMed]

18.

X. Intes, J. Ripoll, Y. Chen, S. Nioka, A. G. Yodh, and B. Chance, “In vivo continuous-wave optical breast imaging enhanced with Indocyanine Green,” Med. Phys. 30, 1039–1047 (2003). [CrossRef] [PubMed]

19.

R. Weissleder and U. Mahmood, “Molecular imaging,” Radiology 219, 316–333 (2001). [PubMed]

20.

J. V. Frangioni, “In vivo near-infrared fluorescence imaging,” Curr. Opin. Chem. Biol. 7, 626–634 (2003). [CrossRef] [PubMed]

21.

K. Licha, “Contrast agents for optical imaging,” Topics in Current Chemistry 222, 1–29 (2002). [CrossRef]

22.

G. Zheng, Y. Chen, X. Intes, B. Chance, and J. Glickson, “Contrast-Enhanced NIR Optical Imaging for subsurface cancer detection,” J. Porphyrin and Phthalocyanines 8, 1106–1118 (2004). [CrossRef]

23.

S. Achilefu, R. Dorshow, J. Bugaj, and R. Rajagopalan, “Novel receptor-targeted fluorescent contrast agents for in-vivo tumor imaging,” Invest. Radiol. 35, 479–485 (2000). [CrossRef] [PubMed]

24.

Y. Chen, G. Zheng, Z. Zhang, D. Blessington, M. Zhang, and H. Li, et al., “Metabolism Enhanced Tumor Localization by Fluorescence Imaging: In Vivo Animal Studies,” Opt. Lett. 28, 2070–2072 (2003). [CrossRef] [PubMed]

25.

R. Weissleder, C. H. Tung, U. Mahmood, and A. Bogdanov, “In vivo imaging with protease-activated near-infrared fluorescent probes,” Nat. Biotech. 17, 375–378 (1999). [CrossRef]

26.

R. Weinberg, “How Does Cancer Arise,” Sci. Am. 275, 62–71 (1996). [CrossRef] [PubMed]

27.

X. Intes, Y. Chen, X. Li, and B. Chance, “Detection limit enhancement of fluorescent heterogeneities in turbid media by dual-interfering excitation,” Appl. Opt. 41, 3999–4007 (2002). [CrossRef] [PubMed]

28.

J. Lewis, S. Achilefu, J. R. Garbow, R. Laforest, and M. J. Welch, “Small animal imaging: current technology and perspectives for oncological imaging,” Eur. J.o Cancer 38, 2173–88 (2002). [CrossRef]

29.

V. Ntziachristos and R. Weissleder, “Experimental three-dimensional fluorescence reconstruction of diffuse media by use of a normalized Born approximation,” Opt. Lett. 26, 893–895 (2001). [CrossRef]

30.

M. J. Eppstein, D. J. Hawrysz, A. Godavarty, and E. M. Sevick-Muraca, “Three-dimensional, Bayesian image reconstruction from sparse and noisy data sets: Near-infrared fluorescence tomography,” Proc. Nat. Acad. Sci. Am. 99, 9619–9624 (2002). [CrossRef]

31.

A. B. Milstein, J.J. Stott, S. Oh, D. A. Boas, R. P. Millane, C. A. Bouman, and K. J. Webb, “Fluorescence optical diffusion tomography using multiple-frequency data,” J. Opt. Soc. Am. A 21, 1035–1049 (2004). [CrossRef]

32.

X. Li, “Fluorescence and diffusive wave diffraction tomographic probes in turbid media,” PhD University of Pennsylvania (1996).

33.

M. O’Leary, “Imaging with diffuse photon density waves,” PhD University of Pennsylvania (1996).

34.

E. Hillman, “Experimental and theoretical investigations of near infrared tomographic imaging methods and clinical applications,” PhD University College London (2002).

35.

A. Liebert, H. Wabnitz, D. Grosenick, M. Moller, R. Macdonald, and H. Rinnerberg, “Evaluation of optical properties of highly scattering media by moments of distributions of times of flight of photons,” Appl. Opt. 42, 5785–5792 (2003). [CrossRef] [PubMed]

36.

R. C. Haskell, L. O. Svaasand, T. Tsay, T. Feng, M. S. McAdams, and B. J. Tromberg, “Boundary conditions for the diffusion equation in radiative transfer,” J. Opt. Soc. Am A 11, 2727–41 (1994). [CrossRef]

37.

R. Gordon, R. Bender, and G. Herman, “Algebraic reconstruction techniques (ART) for the three dimensional electron microscopy and X-Ray photography,” J. Theoret. Biol. 69, 471–482 (1970). [CrossRef]

38.

A. Kak and M. Slaney, “Computerized tomographic Imaging”, IEEE Press, N-Y (1987).

39.

D. Ros, C. Falcon, I. Juvells, and J. Pavia, “The influence of a relaxation parameter on SPECT iterative reconstruction algorithms,” Phys. Med. Biol. 41, 925–937 (1996). [CrossRef] [PubMed]

40.

X. Intes, V. Ntziachristos, J. Culver, A. G. Yodh, and B. Chance, “Projection access order in Algebraic Reconstruction Techniques for Diffuse Optical Tomography,” Phys. Med. Biol. 47, N1–N10 (2002). [CrossRef] [PubMed]

41.

E. Graves, J. 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, 231–241 (2004). [CrossRef]

OCIS Codes
(170.3660) Medical optics and biotechnology : Light propagation in tissues
(170.5270) Medical optics and biotechnology : Photon density waves
(170.5280) Medical optics and biotechnology : Photon migration
(170.6920) Medical optics and biotechnology : Time-resolved imaging
(260.2510) Physical optics : Fluorescence

ToC Category:
Research Papers

History
Original Manuscript: January 24, 2005
Revised Manuscript: February 25, 2005
Published: April 4, 2005

Citation
S. Lam, F. Lesage, and X. Intes, "Time Domain Fluorescent Diffuse Optical Tomography: analytical expressions," Opt. Express 13, 2263-2275 (2005)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-13-7-2263


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References

  1. A Yodh and B. Chance, �??Spectroscopy and imaging with diffusing light,�?? Phys. Today 48, 34-40 (1995). [CrossRef]
  2. X. Intes and B. Chance, �??Non-PET Functional Imaging Techniques Optical,�?? Clin. No. Am. 43, 221-234 (2005).
  3. F. Jobsis, �??Noninvasive infrared monitoring of cerebral and myocardial sufficiency and circulatory parameters,�?? Science 198, 1264-1267 (1977). [CrossRef] [PubMed]
  4. Y. Lin, G. Lech, S. Nioka, X. Intes and B. Chance, �??Noninvasive, low-noise, fast imaging of blood volume and deoxygenation changes in muscles using light-emitting diode continuous-wave imager,�?? Rev. Sci. Instrum. 73, 3065-3074 (2002). [CrossRef]
  5. Y. Chen, C. Mu, X. Intes, D. Blessington and B. Chance, �??Frequency domain phase cancellation instrument for fast and accurate localization of fluorescent heterogeneity,�?? Rev. Sci. Instrum. 74, 3466-3473 (2003). [CrossRef]
  6. B. Tromberg, N. Shah, R. Lanning, A. Cerussi, J. Espinoza, T. Pham, et al., �??Non-invasive in vivo characterization of breast tumors using photon migration spectroscopy,�?? Neoplasia 2, 26-40 (2000). [CrossRef] [PubMed]
  7. D. Grosenick, H. Wabnitz, K. Moesta, J. Mucke, M. Moller, C. Stroszczunski, J. Stobel, B. Wassermann, P. Schlag and H. Rinnerberg, �??Concentration and oxygen saturation of haemoglobin of 50 breast tumours determined by time-domain optical mammography,�?? Phys Med. Biol. 49, 1165-1181 (2004). [CrossRef] [PubMed]
  8. H. Jiang, N. Iftimia, J. Eggert, L. Fajardo and K. Klove, �??Near-infrared optical imaging of the breast with model-based reconstruction,�?? Acad. Radiol. 9, 186-194 (2002). [CrossRef] [PubMed]
  9. M. Franceschini, K. Moesta, S. Fantini, G. Gaida, E. Gratton, H. Jess, et al., �??Frequency-domain techniques enhance optical mammography: Initial clinical results,�?? Proc. Nat. Acad. Sci. Am. 94, 6468-6473 (1997). [CrossRef]
  10. S. Colak, M. van der Mark, G. Hooft, J. Hoogenraad, E. van der Linden, F. Kuijpers, �??Clinical optical tomography and NIR spectroscopy for breast cancer detection,�?? IEEE J. Sel. Top. Quatum Electron. 5, 1143-1158 (1999). [CrossRef]
  11. X. Intes, S. Djeziri, Z. Ichalalene, N. Mincu, Y. Wang, P. St.-Jean, F. Lesage, D. Hall, D. A. Boas, M. Polyzos, �??Time-Domain Optical Mammography Softscan®: Initial Results on Detection and Characterization of Breast Tumors�??, Proc. SPIE 5578, 188-197 (2004). [CrossRef]
  12. D. B. Jakubowski, A. E. Cerussi, F. Bevilacqua, N. Shah, D. Hsiang, J. Butler, B. J. Tromberg, �??Monitoring neoadjuvant chemotherapy in breast cancer using quantitative diffuse optical spectroscopy: a case study,�?? J. Biomed Opt. 9, 230-238 (2004). [CrossRef] [PubMed]
  13. G. Strangman, D. A. Boas, J. Sutton, �??Non-invasive neuroimaging using Near-Infrared light,�?? Biol. Psychiatry 52, 679-693 (2002). [CrossRef] [PubMed]
  14. Y. Chen, D. Tailor, X. Intes and B. Chance, �??Quantitative correlation between Near-Infrared spectroscopy (NIRS) and magnetic resonance imaging (MRI) on rat brain oxygenation modulation,�?? Phys. Med. Biol. 48, 417-427 (2003). [CrossRef] [PubMed]
  15. M. Stankovic, D. Maulik, W. Rosenfeld, P. Stubblefield, A. Kofinas, E. Gratton, et al., �??Role of frequency domain optical spectroscopy in the detection of neonatal brain hemorrhage- a newborn piglet study,�?? J. Matern. Fetal Med. 9, 142-149 (2000). [CrossRef] [PubMed]
  16. J. C. Hebden, A. Gibson, T. Austin, R. M. Yusof, N. Everdell, D. T. Delpy, S. R. Arridge, J. H. Meek and J. S. Wyatt, �??Imaging changes in blood volume and oxygenation in the newborn infant brain using three-dimensional optical tomography,�?? Phys Med Biol. 49, 1117-1130 (2004). [CrossRef] [PubMed]
  17. V. Quaresima, R. Lepanto and M. Ferrari, �??The use of near infrared spectroscopy in sports medicine,�?? J. Sports Med. Phys. Fitness 43, 1-13 (2003). [PubMed]
  18. X. Intes, J. Ripoll, Y. Chen, S. Nioka, A. G. Yodh and B. Chance, �??In vivo continuous-wave optical breast imaging enhanced with Indocyanine Green,�?? Med. Phys. 30, 1039-1047 (2003). [CrossRef] [PubMed]
  19. R. Weissleder and U. Mahmood, �??Molecular imaging,�?? Radiology 219, 316-333 (2001). [PubMed]
  20. J. V. Frangioni, �??In vivo near-infrared fluorescence imaging,�?? Curr. Opin. Chem. Biol. 7, 626�??634 (2003). [CrossRef] [PubMed]
  21. K. Licha, �??Contrast agents for optical imaging,�?? Topics in Current Chemistry 222, 1-29 (2002). [CrossRef]
  22. G. Zheng, Y. Chen, X. Intes, B. Chance and J. Glickson, �??Contrast-Enhanced NIR Optical Imaging for subsurface cancer detection,�?? J. Porphyrin and Phthalocyanines 8, 1106- 1118 (2004). [CrossRef]
  23. S. Achilefu, R. Dorshow, J. Bugaj and R. Rajagopalan, �??Novel receptor-targeted fluorescent contrast agents for in-vivo tumor imaging,�?? Invest. Radiol. 35, 479-485 (2000). [CrossRef] [PubMed]
  24. Y. Chen, G. Zheng, Z. Zhang, D. Blessington, M. Zhang, H. Li, et al., �??Metabolism Enhanced Tumor Localization by Fluorescence Imaging: In Vivo Animal Studies,�?? Opt. Lett. 28, 2070-2072 (2003). [CrossRef] [PubMed]
  25. R. Weissleder, C. H. Tung, U. Mahmood, A. Bogdanov, �??In vivo imaging with protease-activated near-infrared fluorescent probes,�?? Nat. Biotech. 17, 375-378 (1999). [CrossRef]
  26. R. Weinberg, �??How Does Cancer Arise,�?? 275, 62-71 (1996). [CrossRef] [PubMed]
  27. X. Intes, Y. Chen, X. Li and B. Chance, �??Detection limit enhancement of fluorescent heterogeneities in turbid media by dual-interfering excitation,�?? Appl. Opt. 41, 3999-4007 (2002). [CrossRef] [PubMed]
  28. J. Lewis, S. Achilefu, J. R. Garbow, R. Laforest, M. J. Welch, �??Small animal imaging: current technology and perspectives for oncological imaging,�?? Eur. Jour. Cancer 38, 2173�??88 (2002). [CrossRef]
  29. V. Ntziachristos and R. Weissleder, �??Experimental three-dimensional fluorescence reconstruction of diffuse media by use of a normalized Born approximation,�?? Opt. Lett. 26, 893-895 (2001). [CrossRef]
  30. M. J. Eppstein, D. J. Hawrysz, A. Godavarty and E. M. Sevick-Muraca, �??Three-dimensional, Bayesian image reconstruction from sparse and noisy data sets: Near-infrared fluorescence tomography,�?? Proc. Nat. Acad. Sci. Am. 99, 9619-9624 (2002). [CrossRef]
  31. A. B. Milstein, J.J. Stott, S. Oh, D. A. Boas, R. P. Millane, C. A. Bouman and K. J. Webb, �??Fluorescence optical diffusion tomography using multiple-frequency data,�?? J. Opt. Soc. Am. A 21, 1035-1049 (2004). [CrossRef]
  32. X. Li, �??Fluorescence and diffusive wave diffraction tomographic probes in turbid media,�?? PhD University of Pennsylvania (1996).
  33. M. O�??Leary, �??Imaging with diffuse photon density waves,�?? PhD University of Pennsylvania (1996).
  34. E. Hillman, �??Experimental and theoretical investigations of near infrared tomographic imaging methods and clinical applications,�?? PhD University College London (2002).
  35. A. Liebert, H. Wabnitz, D. Grosenick, M. Moller, R. Macdonald and H. Rinnerberg, �??Evaluation of optical properties of highly scattering media by moments of distributions of times of flight of photons,�?? Appl. Opt. 42, 5785-5792 (2003). [CrossRef] [PubMed]
  36. R. C. Haskell, L. O. Svaasand, T. Tsay, T. Feng, M. S. McAdams and B. J. Tromberg, �??Boundary conditions for the diffusion equation in radiative transfer,�?? J. Opt. Soc. Am A 11, 2727-41 (1994). [CrossRef]
  37. R. Gordon, R. Bender and G. Herman, �??Algebraic reconstruction techniques (ART) for the three dimensional electron microscopy and X-Ray photography,�?? J. Theoret. Biol. 69, 471-482 (1970). [CrossRef]
  38. A. Kak and M. Slaney, �??Computerized tomographic Imaging�??, IEEE Press, N.Y. (1987).
  39. D. Ros, C. Falcon, I. Juvells and J. Pavia, �??The influence of a relaxation parameter on SPECT iterative reconstruction algorithms,�?? Phys. Med. Biol. 41, 925-937 (1996). [CrossRef] [PubMed]
  40. X. Intes, V. Ntziachristos, J. Culver, A. G. Yodh and B. Chance, �??Projection access order in Algebraic Reconstruction Techniques for Diffuse Optical Tomography,�?? Phys. Med. Biol. 47, N1-N10 (2002). [CrossRef] [PubMed]
  41. E. Graves, J. 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, 231-241 (2004). [CrossRef]

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