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Optics Express

Optics Express

  • Editor: J. H. Eberly
  • Vol. 1, Iss. 13 — Dec. 22, 1997
  • pp: 404–413
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A fundamental limitation of linearized algorithms for diffuse optical tomography

D. A. Boas  »View Author Affiliations


Optics Express, Vol. 1, Issue 13, pp. 404-413 (1997)
http://dx.doi.org/10.1364/OE.1.000404


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Abstract

Diffuse Optical Tomography is rapidly developing as a new imaging modality for characterizing the spatially varying optical properties of media which strongly scatter light (e.g. tissue). Numerous imaging algorithms exist, and more are being developed. Many of these algorithms rely on assumptions which linearize the relationship between the optical contrast and the perturbed signal. We show that this linear approximation makes quantitative imaging of spatially varying optical properties impossible. The explanation for this result is presented and the implication for Diffuse Optical Tomography is discussed.

© Optical Society of America

When light traveling through a highly scattering medium is more likely to experience a scattering event than to be absorbed, the transport of light is well-modeled by the photon diffusion equation [12

12. A. IshimaruWave Propagation and Scattering in Random Media (Academic Press, Inc., San Diego, 1978).

,13

13. M. S. Patterson, B. Chance, and B. C. Wilson, “Time resolved reflectance and transmittance for the non-invasive measurement of tissue optical properties,” Appl. Opt. 28,2331–2336 (1989). [CrossRef] [PubMed]

]

(D2+vμa+t)Φ(r,t)=vS(r,t).
(1)

An analytic solution of the diffusion equation exists for the scattering of DPDW’s from spherical objects embedded in otherwise homogeneous media. To leading order in the radius (a) and optical properties of the object(μs’ and μa), the monopole (Φl=0(r,t)), dipole (Φl=1(r,t)), and quadrupole (Φl=2(r,t)) moments of the scattered wave are [20

20. D. A. Boas, M. A. O'Leary, B. Chance, and A. G. Yodh, “Detection and characterization of opticalinhomogeneities with diffuse photon density waves: a signal-to-noise analysis,” Appl. Opt. 36,75–92 (1997). [CrossRef] [PubMed]

]:

Φl=0(rs,rd)=vSACexp(ikrs)4πDrsexp(ikrd)4πrd[4πa33][vδμaD]
(2a)
Φl=1(rs,rd)=vSACexp(ikrs)4πDrsexp(ikrd)4πrd[ik1rs][ik1rd][4πa3D][3cosθδμs'3μs'+2δμs']
(2b)
Φl=2(rs,rd)=vSACexp(ikrs)4πDrsexp(ikrd)4πrd[k2+3ikrs3rs2][k2+3ikrd3rd2]
[3cos2θ1][4πa545][δμs'5μs'+3δμ']
(2c)

Here, δμa is the difference in the absorption coefficient between the object and the background medium (or delta absorption coefficient), δμs is the difference in the reduced scattering coefficient, k 2=(-a +)/D, ω is the source angular modulation frequency, μa is the absorption coefficient of the background medium, D is the photon diffusion coefficient of the background medium, a is the radius of the object, and rs , rd , and θ are defined in fig. 1. Note that to leading order the monopole depends on the product of δμa and a 3, and the dipole depends on the product of δμs ’ and a 3. Thus for an absorbing object, it is not possible to simultaneously determine the delta absorption coefficient and the radius if only the monopole term is detectable. Simultaneous determination requires, in the least, detection of two different moments. Likewise for a scattering object, at least two moments must be detectable in order to simultaneously determine the reduced scattering coefficient and the radius. For a general inhomogeneity with absorption and scattering contrast, at least three moments need to be detectable to permit full characterization of the object. These results are detailed in Boas et al. [20

20. D. A. Boas, M. A. O'Leary, B. Chance, and A. G. Yodh, “Detection and characterization of opticalinhomogeneities with diffuse photon density waves: a signal-to-noise analysis,” Appl. Opt. 36,75–92 (1997). [CrossRef] [PubMed]

].

Fig. 1 The geometry used for calculating the scattered DPDW (see text).

Common examples of linearized image reconstruction algorithms for DOT are based on the Born approximation [5

5. S. R. Arridge and J. C. Hebden, “Optical Imaging in Medicine: II. Modelling and reconstruction,” Phys. Med. Biol. 42:841–854 (1997). [CrossRef]

,9

9. M. A. O'Leary, D. A. Boas, B. Chance, and A. G. Yodh, “Experimental images of heterogeneous turbid media by frequency-domain diffusing-photon tomography,” Opt. Lett. 20,426–428 (1995). [CrossRef] [PubMed]

,11

11. Y. Yao, Y. Wang, Y. Pei, W. Zhu, and R. L. Barbour, “Frequency-domain optical imaging of absorption andscattering distributions using a born iterative method,” J. Opt. Soc. Am. A 14,325–342 (1997). [CrossRef]

] and Rytov approximation [9

9. M. A. O'Leary, D. A. Boas, B. Chance, and A. G. Yodh, “Experimental images of heterogeneous turbid media by frequency-domain diffusing-photon tomography,” Opt. Lett. 20,426–428 (1995). [CrossRef] [PubMed]

,21

21. A. C. Kak and M. SlaneyPrinciples of Computerized Tomographic Imaging (IEEE Press, New York, 1988).

]. The Born approximation assumes that the DPDW is given by Φ(r s,r d) = Φ inc (r s,r d) + Φ sc(r s,r d) while the Rytov approximation assumes that the DPDW is given by Φ(r s,r d) = Φ inc(r s,r d) exp(Φ sc(r s,r d)). Φ inc(r s,r d) is the DPDW measured without any inhomogeneities in the optical properties for a light source at position r s and a detector at position r d. The scattered DPDW is given by Φ sc(r s,r d). For the Born approximation,

Φsc(rs,rd)=Φinc(rs,rd)L(r)G(rs,rd)dr.
(3)

while for the Rytov approximation,

Φsc(rs,rd)=1Φinc(rs,rd)Φinc(rs,rd)L(r)G(rs,rd)dr.
(4)

L(r) is an operator which depends on the spatially varying absorption coefficient and the spatially varying reduced scattering coefficient[9

9. M. A. O'Leary, D. A. Boas, B. Chance, and A. G. Yodh, “Experimental images of heterogeneous turbid media by frequency-domain diffusing-photon tomography,” Opt. Lett. 20,426–428 (1995). [CrossRef] [PubMed]

]. Images of the spatially varying optical properties are reconstructed given measurements of Φ sc(r s,r d), calculations of Φ inc(r s,r d) and G(r s,r d) from the known background optical properties, and inverting eq. (3) or eq. (4) to find L(r). G(r s,r d) is the Green’s function solution of the photon diffusion equation for the spatial uniform background optical properties. This inversion generally requires discretizing the integral equation to obtain a matrix equation which is then solved by matrix inversion or algebraic reconstruction techniques.

Fig. 2 Comparison of the first order Born approximation with the exact solution for a 1 cm diameter absorbing object with different absorption coefficients.

Quantitative imaging is possible only if the linearized solutions, eqs. (3) and (4), produce scattered waves which agree well with exact solutions. The fact that sensitivity to the moments of the scattered wave is critical to quantitative characterization of optical inhomogeneities, as described above, suggests that we should analyze the moments of the linear solutions used for image reconstruction algorithms. To do this, we calculated eqs. (3) and (4) for a spherical object embedded in an otherwise uniform, infinite medium, and compared the numerical results with the analytic solution for DPDW’s scattering from spherical objects [17–19

17. P. N. den Outer, T. M. Nieuwenhuizen, and A. Lagendijk, “Location of objects in multiple-scattering media,” J. Opt. Soc. Am. A 10,1209–1218 (1993). [CrossRef]

]. The geometry used for the calculations is indicated in fig. 1. The optical properties of the infinite medium were μs ’=10 cm-1 and μa=0.05 cm-1. The source was positioned 3 cm along the z axis from the center of the object, which was placed at the origin. The source had a modulation frequency of 200 MHz (or angular modulation frequency of 2π 200 MHz). Detectors were placed every 2° in a circle 2.5 cm from the center of the object. The spherical object had a fixed reduced scattering coefficient, μs’=10 cm-1, and a varying absorption coefficient and radius (0.1 ≤ μa ≤ 1.0 cm-1 and 0.1 ≤ a ≤ 1.0 cm). The integrals in eq. (3) and eq. (4) were evaluated by dividing the bounding cube of the spherical object into 10x10x10 voxels and summing the contribution from each voxel. The moments were calculated from the projection of the detected complex scattered wave onto spherical harmonics Yl.m(θ) for m=0 (no dependence on azimuthal angle). We do not explore the dependence on the azimuthal index m as our geometry was chosen for the moments with m≠0 in order to have zero amplitude.

Fig. 3 Comparison of the first order Born approximation with the exact solution for an absorbing object with an absorption coefficient of 0.3 cm-1 and different radii.

We first consider the Born approximation, eq. (3). In fig. 2 we plot the relative amplitudes and phases of the monopole, dipole, and quadrupole moments of the scattered wave for the Born approximation and the exact solution, to show their differences. Results are graphed versus the absorption coefficient of the object for a 1.0 cm diameter object.

When the absorption coefficient of the object is 0.10 cm-1, the amplitude (phase) of the exact and Born moments agree within a few percent (1 degree). As the object absorption coefficient increases, the amplitudes disagree by more than 20% and the phases disagree by more than a few degrees.

In fig. 3 we plot the relative amplitudes and phases of the monopole, dipole, and quadrupole moments of the scattered wave versus radius for an object with an absorption coefficient of 0.3 cm-1. Similar discrepancies are observed. The results in fig. 2 and fig. 3 clearly demonstrate that the first order Born approximation does not accurately predict the amplitude and phase of the scattered DPDW. Thus, image reconstruction algorithms based on the first order Born approximation will not accurately quantify the optical properties of the medium.

Fig. 4 Comparison of the Rytov approximation with the exact solution for a 1 cm diameter absorbing object with different absorption coefficients.

The trend observed in fig. 2 for the Born moments is interesting. As the object absorption coefficient increases, the amplitudes of the moments increase proportionally and the phases of the moments remain fixed. This result is expected. Within the Born approximation, a voxel of the discretized integral produces a monopole scattered wave (given a voxel with an absorption coefficient different than the background). The strength of this monopole wave is linearly proportional to the absorption difference between the voxel and background. Doubling the absorption difference will double the monopole moment. An object comprised of a single absorbing voxel will not produce higher order moments (e.g. dipole and quadrupole). It is only from a group of absorbing voxels that these higher moments are produced. Furthermore, as the existence of these moments depends only on the spatial distribution of voxels, increasing the delta absorption coefficient by a multiplicative factor X will increase the amplitudes of the moments by a factor X. Thus, the relative amplitude of the moments is not dependent on the absorption coefficient (contrary to that observed with the exact solution). Furthermore, the phase remains constant because of this linear dependence on the absorption difference.

It is useful to consider what effect this discrepancy between the Born approximation and the exact solution has on image reconstructions. Reconstruction algorithms typically find the image which gives the best fit to the experimental data by means of minimizing the least-squares difference between the experimental measurement (in this case the exact solution) and the model used by the algorithm (in this case the first Born approximation). This is not strictly true as some algorithms use regularization methods that also minimize a norm of the image [7

7. S. R. Arridge, “Forward and inverse problems in time-resolved infrared imaging,” in Medical Optical Tomography: Functional Imaging and Monitoring, ed. G. Muller, B. Chance, R. Alfano, S. Arridge, J. Beuthan, E. Gratton, M. Kaschke, B. Masters, S. Svanberg, and P. van der Zee, Proc. SPIE IS11, 35–64 (1993).

]. Consider a 1.0 cm diameter spherical absorber with an absorption coefficient of 0.3 cm-1. From fig. 2 and fig. 3 we see that minimizing the least-squares difference of the amplitude can be accomplished by decreasing the size and/or absorption coefficient of the object. Or the size can be overly decreased and compensated by an increase in the absorption coefficient, or vice-versa. To minimize the least-squares difference in the phase, the size of the object must increase, as changing the absorption coefficient has no effect. This suggests that the reconstruction algorithm would reconstruct an object which has a larger diameter and smaller absorption coefficient than the real object parameters. More insight on specific algorithms operating on particular clinical problems could be obtained by correlating the performance of the algorithm with observations like those described in this paragraph.

Fig. 5 Comparison of the Rytov approximation with the exact solution for an absorbing object with and absorption coefficient of 0.3 cm-1 and different radii.

The results in fig. 4 and fig. 5 clearly demonstrate that the Rytov approximation does not accurately predict the amplitude and phase of the scattered DPDW and thus image reconstruction algorithms based on the Rytov approximation will not accurately quantify the optical properties of the medium. The major difference between these Rytov results and the Born results presented in fig. 2 and 3 is that the dipole moment calculated by the Rytov approximation has a discrepancy about four times greater than that of the Born approximation. It is interesting to note that the fractional difference in the amplitude is nearly the same for all moments suggesting that the Rytov approximation may be empirically modified.

These results indicate that linear algorithms for diffuse optical tomography cannot accurately quantify spatially varying optical properties. The solution to this problem is to use non-linear reconstruction algorithms which implement a forward problem solver which accurately calculates the moments of the scattered DPDW. Examples of such algorithms include finite-element and finite-difference solutions of the differential equation, perhaps using multi-grid methods [5

5. S. R. Arridge and J. C. Hebden, “Optical Imaging in Medicine: II. Modelling and reconstruction,” Phys. Med. Biol. 42:841–854 (1997). [CrossRef]

,22

22. H. Jiang, K. D. Paulsen, U. L. Osterberg, B. W. Pogue, and M. S. Patterson, “Simultaneous reconstruction of optical absorption and scattering maps in turbid media from near-infrared frequency-domain data,” Opt. Lett. 20,2128–2130 (1995). [CrossRef] [PubMed]

]. Solving the full non-linear problem is computationally expensive such that near real-time image reconstruction would require super-computers. It is desirable to find less costly methods that gain a speed advantage by not calculating the full non-linear forward problem, but still maintain accuracy.

Figure 6 initially shows amplitude versus object absorption coefficient for a 6 mm diameter object. The amplitude difference between the Born and exact calculations is greater than 10% for the monopole and quadrupole moments for an object absorption coefficient greater than 0.3 cm-1. This is seen by looking at the amplitude ratio (ΦBornl -ΦExactl )/ΦExactl . (Note that there is a systematic difference between the exact and Born and Rytov calculations because of the difference in object volume arising from dividing a sphere into a group of cubes. This systematic difference is generally small compared to the intrinsic difference between the Born approximation and the exact solution for small N but does prevent us from obtaining perfect agreement as we increase N.) When N is 5 the difference is less than 10% for absorption coefficients less than 1 cm-1. The fifth order Born approximation provides a significant improvement. As we increase the diameter of the object to 1 cm the agreement remains good for absorption coefficients less than 0.5 cm-1, but the agreement quickly deteriorates for larger absorption coefficients. As N increases, the agreement at an absorption coefficient of 1.0 cm-1 gets exponentially worse. This can be observed by viewing the amplitude ratio versus N. It appears as if an instability occurs in the Nth order Born approximation. Looking at the graph of amplitude ration versus N in fig. 6, we observe that this instability sets in when the product of the radius and absorption coefficient is between 3 and 4. Similar behavior is observed for the phase data. If an imaging algorithm were to be created based on the Nth order Born approximation, then this behavior needs to be better understood. This discrepancy is most likely a result of ignoring the self-interaction term described by Ostermeyer and Jacques (see eq. (13) of [23

23. M. R. Ostermeyer and S. L. Jacques, “Perturbation theory for diffuse light transport in complex biological tissues,” J. Opt. Soc. Am. A 14,255–261 (1997). [CrossRef]

]). The message from fig. 6 is that an imaging algorithm based on the fifth order Born approximation would be suitable for object diameters less than 8 mm and absorption coefficients less than 0.4 cm-1.

Fig. 6 This is a view of the interactive applet for browsing the four dimensional data set. The data here is for an absorber with a 0.6 cm diameter. Click on the figure to start the applet. [Media 1]

An imaging algorithm based on an Nth order Born approximation is likely to follow the same strategy used in other non-linear approaches that use finite-difference or finite-element schemes. That is: (1) an initial guess of the spatial variations in the optical properties is made; (2) the fluence rate is calculated for that initial guess using non-linear method X (where X is finite-difference, finite-element, Nth order Born, etc.); (3) the difference between the calculated and measured fluence rates is used in an integral equation like eq. (3) or eq. (4) to solve for an update in the initial guess of the optical properties; (4) iterate back to step 2) until some convergence criterion is met. Step (2) is performed using a so-called forward problem solver (such as finite-difference, finite-element, or Nth order Born) and step (3) needs a so-called inverse problem solver. Each step is generally computationally expensive. The Nth order Born approximation requires approximately NxM2 multiplications where M is the number of voxels in the image. For comparison, multi-grid methods using finite-difference or finite-element schemes generally require M ln (1/ϵ) multiplications where e is the desired accuracy [24

24. W. L. BriggsA Multigrid Tutorial (Society for Industrial and Applied Mathematics, Philadelphia, 1987).

]. Solving the forward problem is therefore much more efficient with a multi-grid method than with the Nth order Born approximation.

In summary, we have shown that linearized algorithms for diffuse optical tomography do not accurately model the amplitude and phase of scattered DPDW’s. Quantitative imaging is thus not feasible as it relies on the use of accurate models for predicting the amplitude and phase of the scattered DPDW. The most common linearized algorithms are based on the first order Born approximation and the Rytov approximation. Each approximation poorly models the scattered DPDW. The Rytov approximation calculates the monopole and quadrupole moments scattered from an absorbing object more accurately than the Born approximation, but the Born approximation is much more accurate for the dipole moment. The main consequence of the results presented here is that nonlinear imaging algorithms must be used to achieve quantitative imaging. We have shown that higher order Born approximations can more accurately calculate the scattered wave. When the absorption coefficient is less than 0.4 cm-1 and the diameter is less than 8 mm, the fifth order Born approximation generally provides sufficient accuracy. However, implementing an Nth order Born approximation in a non-linear imaging algorithm is much more computationally expensive than using multi-grid finite-difference or finite-element methods.

References and links

1.

B. Chance, Photon Migration in Tissues (Plenum Press, New York, 1988).

2.

G. Muller, B. Chance, R. Alfano, S. Arridge, J. Beuthan, E. Gratton, M. Kaschke, B. Masters, S. Svanberg, and P. van der Zee, Medical Optical Tomography: Functional Imaging and Monitoring, Proc. SPIEIS11 (1993).

3.

A. J. Welch and M. J. C. van Gemert, Optical-Thermal Response of Laser-Irradiated Tissue (Plenum Press, New York, 1995).

4.

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

5.

S. R. Arridge and J. C. Hebden, “Optical Imaging in Medicine: II. Modelling and reconstruction,” Phys. Med. Biol. 42:841–854 (1997). [CrossRef]

6.

R. L. Barbour, H. L. Graber, Y. Wang, J. Chang, and R. Aronson, “Perturbation approach for optical diffusiontomography using continuous-wave and time-resolved data,” in Medical Optical Tomography: Functional Imaging and Monitoring, ed. G. Muller, B. Chance, R. Alfano, S. Arridge, J. Beuthan, E. Gratton, M. Kaschke, B. Masters, S. Svanberg, and P. van der Zee, Proc. SPIE IS11,87–120 (1993).

7.

S. R. Arridge, “Forward and inverse problems in time-resolved infrared imaging,” in Medical Optical Tomography: Functional Imaging and Monitoring, ed. G. Muller, B. Chance, R. Alfano, S. Arridge, J. Beuthan, E. Gratton, M. Kaschke, B. Masters, S. Svanberg, and P. van der Zee, Proc. SPIE IS11, 35–64 (1993).

8.

D. A. Benaron, D. C. Ho, S. Spilman, J. P. Van Houten, and D. K. Stevenson, “Tomographic time-of-flight optical imaging device,” Adv. Exp. Med. Biol. 361,609–617 (1994). [CrossRef]

9.

M. A. O'Leary, D. A. Boas, B. Chance, and A. G. Yodh, “Experimental images of heterogeneous turbid media by frequency-domain diffusing-photon tomography,” Opt. Lett. 20,426–428 (1995). [CrossRef] [PubMed]

10.

H. Jiang, K. D. Paulsen, U. L. Osterberg, B. W. Pogue, and M. S. Patterson, “Optical image reconstruction using frequency-domain data: Simulations and experiments,” J. Opt. Soc. Am. A 13,253–266 (1996). [CrossRef]

11.

Y. Yao, Y. Wang, Y. Pei, W. Zhu, and R. L. Barbour, “Frequency-domain optical imaging of absorption andscattering distributions using a born iterative method,” J. Opt. Soc. Am. A 14,325–342 (1997). [CrossRef]

12.

A. IshimaruWave Propagation and Scattering in Random Media (Academic Press, Inc., San Diego, 1978).

13.

M. S. Patterson, B. Chance, and B. C. Wilson, “Time resolved reflectance and transmittance for the non-invasive measurement of tissue optical properties,” Appl. Opt. 28,2331–2336 (1989). [CrossRef] [PubMed]

14.

M. A. O'Leary, D. A. Boas, B. Chance, and A. G. Yodh, “Refraction of diffuse photon density waves,” Phys. Rev. Lett. 69,2658–2661 (1992). [CrossRef] [PubMed]

15.

J. B. Fishkin and E. Gratton, “Propagation of photon density waves in strongly scattering media containing an absorbing ‘semi-infinite’ plane bounded by a straight edge,” J. Opt. Soc. Am. A 10,127–140 (1993). [CrossRef] [PubMed]

16.

D. A. Boas, M. A. O'Leary, B. Chance, and A. G. Yodh, “Scattering and wavelength transduction of diffuse photon density waves,” Phys. Rev. E 47,R2999 (1993). [CrossRef]

17.

P. N. den Outer, T. M. Nieuwenhuizen, and A. Lagendijk, “Location of objects in multiple-scattering media,” J. Opt. Soc. Am. A 10,1209–1218 (1993). [CrossRef]

18.

D. A. Boas, M. A. O'Leary, B. Chance, and A. G. Yodh, “Scattering of diffuse photon density waves by spherical inhomogeneties within turbid media: analytic solution and applications,” Proc. Natl. Acad. Sci. USA 91, 4887–4891 (1994). [CrossRef] [PubMed]

19.

S. Feng, F. Zeng, and B. Chance, “Photon migration in the presence of a single defect: a perturbation analysis,” Appl. Opt. 34,3826–3837 (1995). [CrossRef] [PubMed]

20.

D. A. Boas, M. A. O'Leary, B. Chance, and A. G. Yodh, “Detection and characterization of opticalinhomogeneities with diffuse photon density waves: a signal-to-noise analysis,” Appl. Opt. 36,75–92 (1997). [CrossRef] [PubMed]

21.

A. C. Kak and M. SlaneyPrinciples of Computerized Tomographic Imaging (IEEE Press, New York, 1988).

22.

H. Jiang, K. D. Paulsen, U. L. Osterberg, B. W. Pogue, and M. S. Patterson, “Simultaneous reconstruction of optical absorption and scattering maps in turbid media from near-infrared frequency-domain data,” Opt. Lett. 20,2128–2130 (1995). [CrossRef] [PubMed]

23.

M. R. Ostermeyer and S. L. Jacques, “Perturbation theory for diffuse light transport in complex biological tissues,” J. Opt. Soc. Am. A 14,255–261 (1997). [CrossRef]

24.

W. L. BriggsA Multigrid Tutorial (Society for Industrial and Applied Mathematics, Philadelphia, 1987).

OCIS Codes
(170.5270) Medical optics and biotechnology : Photon density waves
(170.5280) Medical optics and biotechnology : Photon migration
(170.6960) Medical optics and biotechnology : Tomography

ToC Category:
Focus Issue: Biomedical optics

History
Original Manuscript: October 8, 1997
Revised Manuscript: October 8, 1997
Published: December 22, 1997

Citation
David Boas, "A fundamental limitation of linearized algorithms for diffuse optical tomography," Opt. Express 1, 404-413 (1997)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-1-13-404


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References

  1. B. Chance, Photon Migration in Tissues (Plenum Press, New York, 1988).
  2. G. Muller, B. Chance, R. Alfano, S. Arridge, J. Beuthan, E. Gratton, M. Kaschke, B. Masters, S. Svanberg and P. van der Zee, Medical Optical Tomography: Functional Imaging and Monitoring, Proc. SPIE IS11 (1993).
  3. A. J. Welch and M. J. C. van Gemert, Optical-Thermal Response of Laser-Irradiated Tissue (Plenum Press, New York, 1995).
  4. A. Yodh and B. Chance, "Spectroscopy and imaging with diffusing light," Phys. Today 48,34-40 (1995). [CrossRef]
  5. S. R. Arridge and J. C. Hebden, "Optical Imaging in Medicine: II. Modelling and reconstruction," Phys. Med. Biol. 42:841-854 (1997). [CrossRef]
  6. R. L. Barbour, H. L. Graber, Y. Wang, J. Chang and R. Aronson, "Perturbation approach for optical diffusion tomography using continuous-wave and time-resolved data," in Medical Optical Tomography: Functional Imaging and Monitoring, ed. G. Muller, B. Chance, R. Alfano, S. Arridge, J. Beuthan, E. Gratton, M. Kaschke, B. Masters, S. Svanberg and P. van der Zee, Proc. SPIE IS11,87-120 (1993).
  7. S. R. Arridge, "Forward and inverse problems in time-resolved infrared imaging," in Medical Optical Tomography: Functional Imaging and Monitoring, ed. G. Muller, B. Chance, R. Alfano, S. Arridge, J. Beuthan, E. Gratton, M. Kaschke, B. Masters, S. Svanberg and P. van der Zee, Proc. SPIE IS11,35-64 (1993).
  8. D. A. Benaron, D. C. Ho, S. Spilman, J. P. Van Houten and D. K. Stevenson, "Tomographic time-of-flight optical imaging device," Adv. Exp. Med. Biol. 361,609-617 (1994). [CrossRef]
  9. M. A. O'Leary, D. A. Boas, B. Chance and A. G. Yodh, "Experimental images of heterogeneous turbid media by frequency-domain diffusing-photon tomography," Opt. Lett. 20,426-428 (1995). [CrossRef] [PubMed]
  10. H. Jiang, K. D. Paulsen, U. L. Osterberg, B. W. Pogue and M. S. Patterson, "Optical image reconstruction using frequency-domain data: Simulations and experiments," J. Opt. Soc. Am. A 13,253-266 (1996). [CrossRef]
  11. Y. Yao, Y. Wang, Y. Pei, W. Zhu and R. L. Barbour, "Frequency-domain optical imaging of absorption and scattering distributions using a born iterative method," J. Opt. Soc. Am. A 14,325-342 (1997). [CrossRef]
  12. A. Ishimaru Wave Propagation and Scattering in Random Media (Academic Press, Inc., San Diego, 1978).
  13. M. S. Patterson, B. Chance and B. C. Wilson, "Time resolved reflectance and transmittance for the non-invasive measurement of tissue optical properties," Appl. Opt. 28,2331-2336 (1989). [CrossRef] [PubMed]
  14. [CrossRef] [PubMed]
  15. J. B. Fishkin and E. Gratton, "Propagation of photon density waves in strongly scattering media containing an absorbing 'semi-infinite' plane bounded by a straight edge," J. Opt. Soc. Am. A 10,127-140 (1993). [CrossRef] [PubMed]
  16. D. A. Boas, M. A. O'Leary, B. Chance and A. G. Yodh, "Scattering and wavelength transduction of diffuse photon density waves," Phys. Rev. E 47, R2999 (1993). [CrossRef]
  17. P. N. den Outer, T. M. Nieuwenhuizen and A. Lagendijk, "Location of objects in multiple-scattering media," J. Opt. Soc. Am. A 10,1209-1218 (1993). [CrossRef]
  18. D. A. Boas, M. A. O'Leary, B. Chance and A. G. Yodh, "Scattering of diffuse photon density waves by spherical inhomogeneties within turbid media: analytic solution and applications," Proc. Natl. Acad. Sci. USA 91,4887-4891 (1994). [CrossRef] [PubMed]
  19. S. Feng, F. Zeng and B. Chance, "Photon migration in the presence of a single defect: a perturbation analysis," Appl. Opt. 34,3826-3837 (1995). [CrossRef] [PubMed]
  20. D. A. Boas, M. A. O'Leary, B. Chance and A. G. Yodh, "Detection and characterization of optical inhomogeneities with diffuse photon density waves: a signal-to-noise analysis," Appl. Opt. 36,75-92 (1997). [CrossRef] [PubMed]
  21. A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (IEEE Press, New York, 1988).
  22. H. Jiang, K. D. Paulsen, U. L. Osterberg, B. W. Pogue and M. S. Patterson, "Simultaneous reconstruction of optical absorption and scattering maps in turbid media from near-infrared frequency-domain data," Opt. Lett. 20,2128-2130 (1995). [CrossRef] [PubMed]
  23. M. R. Ostermeyer and S. L. Jacques, "Perturbation theory for diffuse light transport in complex biological tissues," J. Opt. Soc. Am. A 14,255-261 (1997). [CrossRef]
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