1 September 2010 Identification of the optimal wavelengths for optical topography: a photon measurement density function analysis
Author Affiliations +
J. of Biomedical Optics, 15(5), 056002 (2010). doi:10.1117/1.3484747
A method is presented to select the optimal wavelengths for multispectral optical topography, which not only gives good separation between chromophores, absorption, and scattering, but also minimizes the differences between interrogated volumes. This method uses the sum of squared differences to compare photon measurement density functions, which were generated for wavelengths in the near-infrared (NIR) range for a suitable model of tissue optical properties. It is found that including this condition significantly influences the range of optimal wavelengths. However, for the adult human head, the differences between interrogated volumes at NIR wavelengths are very small and image reconstruction is only slightly improved when measurements with overlapping sensitivities are used.
Correia, Gibson, and Hebden: Identification of the optimal wavelengths for optical topography: a photon measurement density function analysis



Diffuse optical imaging is a noninvasive technique used to obtain the spatial distribution of optical properties and chromophore concentrations of tissue from measurements of near-infrared (NIR) light, which propagates through the tissue. The technique can be used to monitor cerebral oxygenation and hemodynamics, to provide information about cerebral pathological conditions, to measure functional brain activation during a physical or mental task, and for breast cancer detection.1, 2, 3, 4, 5, 6 Two different approaches are considered in NIR imaging: optical topography and optical tomography. Optical topography is used to describe systems that obtain diffuse reflectance measurements from an array of source-detector pairs placed on the surface of the tissue, whereas optical tomography involves reconstructing a 3-D volume using multiple measurement of transmitted light across the whole tissue volume.7

A few NIR spectroscopy studies have been published that explore the selection of an optimal wavelength pair that minimizes cross-talk, where a change in oxy-hemoglobin concentration [HbO2] may replicate a change in deoxy-hemoglobin concentration [HHb] and vice versa, and minimizes the influence of noise on calculated chromophore concentration changes.8, 9, 10, 11, 12, 13 These studies revealed that, for measurements performed at two different NIR wavelengths, one of the wavelengths should be in the range 650750nm and the other > 820nm .

Image reconstruction using continuous wave (CW) measurements at a single wavelength is an inverse problem with nonunique solutions, where multiple sets of optical properties can yield similar data.14 As a consequence, cross-talk between the images of absorption and scattering occurs. Increasing the number of measurement wavelengths only increases the number of unknowns because absorption and scattering need to be reconstructed for each wavelength. However, the multispectral method proposed by Corlu 15, 16 directly reconstructs the chromophore concentrations and scattering factors that are wavelength independent, meaning that the number of unknowns remains constant when the number of measurement wavelengths increases, representing a clear advantage over the conventional method.

Corlu 15, 16 introduced an approach for selecting the optimal wavelengths for optical tomography based on their multispectral method. This approach sought to maximize separation between absorption and scattering separation while also optimizing separation between the concentrations of main breast tissue chromophores ( HbO2 , HHb, water, and lipid). However, this overlooks the possibility that the volumes over which measurements are sensitive to changes in absorption and scattering may be different at the selected wavelengths. This issue is of importance in order to accurately locate optical property changes occurring within the object of study, because different wavelengths may have different sensitivities to the same changes in the medium, and the same measured change could be associated with changes in different regions within the reconstructed volume. In this study, the optimal wavelength set is estimated by incorporating a third condition, which takes into account the interrogated volume. The spatial similarities between sensitivity profiles for different wavelengths are analyzed and quantified.




Reconstruction Method

The conventional image reconstruction method involves finding the optical properties of the medium from a set of measurements made at the surface. The optical properties are then combined to calculate the chromophore concentrations at wavelength λ using the following equation:


where εi is the extinction coefficient and ci is the concentration of the i ’th chromophore. The reduced scattering coefficient μs is approximately given by Mie scattering theory as


where a is the scatter amplitude and b the scatter power. In this work, the latter is considered to be constant for simplicity.

The multispectral method involves reconstructing chromophore concentrations and scatter parameters using all measurements at all wavelengths simultaneously.16 The problem is nonlinear but can be linearized and reduced to the so-called difference method, based on the assumption that the true chromophore concentrations are close to an initial guess. For CW measurements, only intensity A is measured, hence changes in the measured data ΔA at n measurement wavelengths are related to changes in the chromophore concentrations Δci and scatter amplitude Δa through matrix J̃ ,


where the elements of the matrix J̃ are given by




where κ is the diffusion coefficient. The Jacobian matrix Jλ represents the sensitivity of a measurement to changes in the optical properties within the medium. The Jacobian can be calculated by solving the diffusion equation using the finite element method, for both the forward and adjoint solutions.17 In the Rytov approximation, changes in the log intensity are used instead.17, 18

Chromophore concentrations and scatter parameters can be obtained by solving an ill-posed inverse problem at all wavelengths. In the work presented here, this is aided using the zero-order Tikhonov regularization,


where α=diag(JTJ+γ)12 is the NOSER-type regularization parameter,19, 20 with γ=βσmax1 , where β is a scaling factor and σmax is the maximum singular value of JTJ . The constant γ ensures that the regularization parameter is never too small and is approximately equal to the noise present in the data.


Identification of the Optimal Wavelengths in Optical Topography


Unique and simultaneous reconstruction

The nonuniqueness problem associated with using CW measurements results in cross-talk between absorption and scattering. Minimizing the nonuniqueness of the multispectral method by selecting the optimal wavelengths represents one of the conditions introduced by Corlu 16 Rewriting the nonuniqueness conditions introduced by Arridge and Lionheart14 in terms of the wavelength-independent variables in the multispectral method, it follows that the inverse problem has a nonunique solution when the residual norm16 R :


is zero. Thus, a maximization of R is required. E is a wavelength-dependent matrix of the form



The ability to separate chromophore concentrations can be evaluated from the matrix form of Eq. 1,16


using the condition number k of the extinction coefficient matrix εi(λ) , which is the ratio of the largest to the smallest singular value of the matrix. A small k[εi(λ)] indicates a smooth singular value decay resulting in a reduced sensitivity of the system to errors in the measurements, and therefore, chromophores contribute evenly to absorption. In order to distinguish between chromophore concentrations, the wavelength sets chosen should minimize the condition number k[εi(λ)] , which defines the second condition introduced by Corlu 16 This condition number also describes the linear independence of the system of equations. When k[εi(λ)] is small, the matrix has full rank and, therefore, the wavelength set provides distinct and independent information.


Interrogated volume

In optical topography brain activation studies using multiple wavelengths, it is invariably assumed that measurements at each wavelength contain information from the same cerebral region. If not, data at some wavelength could be measuring a region where the activation occurs, whereas the data at others could be more sensitive to surrounding areas and the study may return erroneous results. Each row of the Jacobian matrix J represents the measurement sensitivity of a source-detector pair to a perturbation in the optical properties of the object of study, which is known as the photon measurement density function (PMDF).21 In terms of image reconstruction, if the forward model is calculated for the same optical properties but the measurement wavelengths have different sensitivities to changes in absorption and scattering, then for a perturbation in the optical properties of the tissue, the reconstructed perturbation will occur in different spatial locations. In practice, the optical properties of tissues beneath the probe are unknown, and in order to reconstruct changes in tissue properties as accurately as possible, it is necessary to acquire data at wavelengths where the sensitivities at each wavelength coincide.

In this work, a method is proposed to find which combinations of wavelengths have overlapping sensitivities, which is an indicator of the similarity between interrogated volumes, by comparing PMDFs for a certain source-detector pair using different measurement wavelengths. The influence of using different NIR measurements is analyzed in terms of image reconstruction and volume sensitivities. This is less relevant when considering transmission measurements (e.g., across a head of breast) because there is an intrinsically greater overlap of the PMDFs.



The software package TOAST (time-resolved optical absorption and scattering tomography)22 was used to model the propagation of light in tissue at wavelengths from 650to990nm spaced in 10-nm intervals. To generate PMDFs for each wavelength, one needs to insert the corresponding optical properties of the medium in the forward problem. Initially, the medium is considered to be homogeneous with optical properties of gray matter. Subsequently, a three-layer model is used to generate PMDFs, which consists of two extracerebral layers with skin and skull optical properties and an intracerebral layer with gray-matter optical properties. A single source-detector pair 40mm apart was used in the forward model because simulations have indicated that this is sufficient to measure hemodynamic changes in adult gray matter.23


Adult Head Model


Homogeneous model

The first model consisted of a uniform slab with dimensions 85×85×30mm with intracerebral tissue optical properties derived from a combination of chromophores present in brain tissue: hemoglobin, water, lipid, and cytochromes.

Brain absorption spectrum

Total absorption coefficient for brain tissue is calculated from Eq. 1 as


μa,brain(λ)=εHbO2[HbT]SO2+εHHb[HbT](1SO2)+μa,H2OW+μa,LipidL+aa3,b,c{εoxCytx[oxCytx]Ox +εrCytx[rCytx](1Ox)}+B,
where [HbT] is the total hemoglobin concentration [in micromolar (μM)], SO2 is the oxygen saturation, W is the fraction of water content, μa,H2O is the absorption coefficient of 100% water ( mm1 ), L is the fraction of lipid content, μa,Lipid is the absorption coefficient of 100% fat ( mm1 ), εHHb is the specific absorption coefficient of HHb (μM1mm1) , εHbO2 is the specific absorption coefficient of HbO2 (μM1mm1) , εrCytx is the specific absorption coefficient of reduced cytochrome x (μM1mm1) , εoxCytx is the specific absorption coefficient of oxidised cytochrome x (μM1mm1) , and Ox is the fraction of oxidized Cyt x . The parameter B is the background absorption, which was set to B=0.012mm1 so that the total absorption matches the value μa(800nm)=0.036mm1 used by Okada and Delpy.31 The extinction coefficients used in this work were obtained from the literature.26, 32, 33, 34 The estimated concentrations in the gray matter for each chromophore are summarized in Table 1 .

Table 1

Chromophore concentrations used to calculate the absorption coefficients for the skin and gray matter.

Gray matterWater content (%)2480
Lipid content (%)2511.6
[HbT] (μM) 2680
SO2 (%)2670
[oxCyt aa3] (μM) 276.4
[rCyt aa3] (μM) 271.6
Oaa3 (%)2780
[oxCyt b] (μM) 272.37
[rCyt b] (μM) 270.89
Oc (%)2750
[oxCyt c] (μM) 271.36
[rCyt c] (μM) 270.68
Ob 2740
SkinWater content (%)2860
[HbT] (μM) 2965
SO2 (%)2871
Melanin content (%)3013

Brain scattering spectrum

Matcher 35 measured μs of the forehead of different human adults over the wavelength range of 760900nm . They fitted the experimental spectra to Eq. 2 and obtained a=32.08(mm1nm1) and b=0.53(mm1) . Meanwhile, van der Zee27 measured μs for gray matter in vitro and found μs(800nm)=2.2mm1 , which can be used as a reference number and included in the exponential relation found by Matcher, 35 which then becomes




Three-layer model

Figure 1 shows the second adult head model, which is a slab with the same dimensions as the homogeneous model, consisting of three layers that imitate the scalp, skull, and intracerebral tissue. Scalp is represented by a 4-mm thickness layer, and the skull is 7mm thick. Absorption and scattering spectra of skull are described in Firbank 36

Fig. 1

Three-layer adult head model and probe placement.


Skin absorption spectrum

Three main chromophores were considered for scalp: hemoglobin, water, and melanin. Their estimated concentrations in skin are summarized in Table 1. The melanin absorption coefficient can be approximated by the expression30


in mm1 , where λ is the wavelength represented in units of nanometers. Considering the contributions from all skin chromophores, the total absorption coefficient of the skin layer can be represented as


where M is the fraction of melanin content. It was not necessary to consider a background absorption for the skin layer, because the absorption coefficient values already show a good agreement with the values presented by Simpson 37

Skin scattering spectrum

The wavelength dependence of the reduced scattering coefficient of skin has been modeled by Bashkatov 38 as a combination of Mie and Rayleigh scattering given by


in mm1 , where λ is the wavelength in nm. The first term corresponds to Mie scattering due to large collagen fibers and the second to Rayleigh scattering mainly due to small collagen fibrils.


Similarity Measures

The sum of squared differences (SSD) can be used to measure the similarity between two sensitivity profiles. For PMDFs X and Y with voxels i , the SSD is defined as


where N is the number of voxels. The SSD will be zero when the distributions are identical.


Optimal Wavelengths

Sensitivity profile maps (PMDFs) were obtained for the wavelength range of 650990nm in steps of 10nm , making a total of 35 wavelengths. Initially, the PMDFs were combined in sets of three wavelengths (C335) , making a total of 6545 wavelength sets. Thereafter, wavelength sets of four wavelengths were investigated, which results in 52,360 sets. For a medium with i absorption chromophores, at least n=i+1 wavelengths are required when the scattering parameter a is allowed to vary.16 Therefore, in order to find the solution for both absorption and scattering coefficients for a medium with two chromophores, it is necessary to employ at least three wavelengths. It is common for optical topography systems to acquire data at just two wavelengths. Therefore, all possible wavelength pairs are compared (total of 595), to provide an insight into what can be achieved using only two measurement wavelengths.

The aim of this study is to determine which wavelength set gives the best combination of: (i) good separation between absorption and scattering, (ii) good separation between [HbO2] and [HHb], and (iii) excellent overlap between sensitivity profiles. Quantitatively, we aim to find the wavelength sets that maximize the residual norm R [Eq. 7], minimize the condition number k of the matrix εi(λ) [Eq. 9], and minimize the SSD [Eq. 15]. The matrix E in Eq. 8 for three wavelengths is reduced to


where the scatter power b is considered to be 0.53.35


PMDF Comparison

The PMDFs for wavelengths from 650to990nm are compared by quantifying the differences in their sensitivity in terms of penetration depth, volume, and full width at half maximum (FWHM), in order to determine if these differences are significant. Absolute sensitivity values are used (because the absorption PMDF has negative sensitivity), and the threshold is set to 0.01. An analysis is performed of how these differences affect the measurements and image reconstructions.



The volume (V) is the number of voxels (N) with sensitivity above the threshold times the voxel volume. The relative volume compares the PMDF volume at 650nm (V650) with all the other PMDF volumes (Vλ) ,


where N650 is the number of voxels above the threshold in the PMDF at 650nm and Nλ is the number of voxels above the threshold for the remaining wavelengths.



The FWHM was calculated for the PMDF cross section at half-way between the source and detector for the depth direction. The PMDF profiles include the point of maximum sensitivity. The FWHM provides a measure of the spread of the PMDFs.


Penetration depth

The penetration depth (PD) is defined as the maximum distance into the medium at which perturbations in the optical properties can still be measured. The central region of the PMDF penetrates deeper into the tissue, which corresponds to the PMDF profile ( z direction) that passes through the midpoint between the source and detector. Hence, the distance at which the sensitivity drops below the threshold for these profiles is considered to be the PD.


Image Reconstruction

Simulations were performed to compare the cross-talk effects in images reconstructed from measurements at wavelengths selected using our method against those from measurements at wavelengths selected using Corlu ’s method.16 The simulated medium consisted of a slab with 59,202 nodes, resulting in 38,572 elements, and dimensions 85×85×30mm . An array with eight detectors and eight sources (described previously by Correia 39) is centrally placed at z=0mm . However, here only source-detector separations from 14to41mm were considered. TOAST was used to generate CW model data at each wavelength, to which 5% random Gaussian noise was added. The background had brain optical properties, which were described in Section 3.1.1. Hence, the deoxy-hemoglobin concentration [HHb] is 24μM , the oxy-hemoglobin concentration [HbO2] is 56μM , and the scattering amplitude a is 32mm1nm1 . Three perturbations with dimensions 10.6×10.6×10.6mm were placed within the medium, as shown in Fig. 2 . The center of each perturbation was at 11.7mm below the imaging surface. A single parameter was changed for each perturbation, which consisted of either an increase in [HHb] of 1μM , in [HbO2] of 4μM , or in a of 1mm1nm1 . These perturbations are sufficiently small to ensure that the concentrations and scattering amplitude can be retrieved by linear reconstruction, and are roughly consistent with the magnitude of cerebral hemodynamic changes. Images were reconstructed using the multispectral method on a 24×24×9 regular pixel basis.

Fig. 2

Cross section xy at z=11.7mm of the medium used in the simulation.





Optimal Wavelengths

For simplicity, the absorption and diffusion sensitivity profiles are dealt with separately. The first results are shown in Fig. 3 for the homogeneous model, considering absorption sensitivity profiles only and three wavelength sets. Note that the SSD values in Fig. 3 represent the average for each wavelength set. Because of the large number of points, further analysis of the wavelength sets was limited to those for which R0.2 , k100 , and (because the SSD for two similar PMDFs is 0.001 ) SSD0.002 (dark circles in Fig. 3). Each criterion has a different order of magnitude; hence, the results contained in this new range were normalized. The distance between the criteria values of a certain set of wavelengths in this range and the point (SSD,k,R)=(0,0,1) was also calculated, which is the point with the smallest SSD, smallest condition number criteria, and highest residual. The wavelength distribution of the ten sets of three wavelengths with the shortest distance are displayed in the histogram in Fig. 4 . Histograms are obtained by counting the number of times a certain wavelength occurs, which is done separately for the three wavelengths, and normalized to the most frequently occurring wavelength. For comparison, Fig. 5 shows the histogram for the ten wavelength sets within the limit R0.2 and k100 , whose distance is closest to the point (k,R)=(0,1) . The means and standard deviations σ of each wavelength distribution were calculated in order to find the optimal wavelengths and measure their spread. A 5-nm uncertainty error is combined with σ (σtotal=σ2+52) , due to the bin size. Using the three-wavelength selection criteria, the wavelengths are 680±5 , 726±7 , and 835±17nm . When only the high residual and low condition number criteria are used the optimal wavelengths are 650±5 , 724±7 , and 915±17nm . The results suggest that when the new PMDF overlap condition is included the smallest wavelength becomes larger, and the largest wavelength becomes smaller.

Fig. 3

Plot of the sum of squared differences SSD (in millimeters), condition number k and residual norm R , where each point represents a set of three wavelengths.


Fig. 4

Histogram of the optimal wavelength distributions that satisfy the three wavelength selection conditions, for the homogeneous model, three wavelengths, and two chromophores ( HbO2 and HHb).


Fig. 5

Histogram of the optimal wavelength distributions that satisfy the high residual and low condition number criteria, for the homogeneous model, three wavelengths, and two chromophores ( HbO2 and HHb).


The optimal four wavelengths that satisfy the three criteria are 680±5 , 715±14 , 733±7 , and 828±9nm . Adding a fourth wavelength broadened the central wavelength range and shifted the highest wavelength to a smaller value. The wavelength set obtained without including the third condition is 650±5 , 715±7 , 727±7 , and 919±13nm . The differences between these results and those presented by Corlu 16 are inevitably due to differences in the wavelength range and spacing, chromophore extinction coefficients, and criteria limits imposed.

The SSD for diffusion PMDFs is always slightly smaller than for absorption PMDFs. This is probably because scattering does not have a strong wavelength dependence. Thus, the optimal wavelength sets found using the absorption SSDs also return small diffusion SSDs.

The histograms for the three-layer adult head model for three and four wavelengths are shown in Figs. 6 and 7 , respectively. For the three wavelength distributions, the optimal wavelength values are 680±5 , 725±10 , and 876±12nm , and for four wavelengths these values are 685±7 , 719±9 , 731±8 , and 873±9nm . The main difference between the optimal wavelengths found for the homogeneous model and the three-layer model occurs for the largest wavelength, which becomes larger for the latter. The other wavelengths are approximately the same and, similar to the homogeneous case, adding a fourth wavelength resulted in the merger of the two central wavelengths (i.e., the mean value is approximately the same but the range is larger). Also for this model, the diffusion SSDs are smaller than the corresponding values for absorption; hence, the previous results also apply for diffusion-sensitivity profiles.

Fig. 6

Histogram of the optimal wavelength distributions that satisfy the three wavelength selection conditions, for the three-layer model, three wavelengths and two chromophores ( HbO2 and HHb).


Fig. 7

Histogram of the optimal wavelength distributions that satisfy the three-wavelength selection conditions, for the three model, four wavelengths, and two chromophores ( HbO2 and HHb).


The wavelength distributions obtained using our method were similar when the scattering power b was varied from 0 to 2.8, whereas for the two-criteria method similar wavelength distributions were obtained for a range of b from 0 to 1.2.


Two wavelengths

It is common to assume that scattering is homogeneous and constant over time. This assumption is convenient when data are acquired at only two wavelengths, enabling solutions to be obtained if only two chromophores are considered. The following analysis is aimed at identifying the two optimal wavelengths that give good separation between chromophores and good spatial overlap between sensitivity profiles.

The solution is nonunique when only two wavelengths are used. Although the SSD and the condition numbers k can be very small, the residual norm R is never larger than 1×107 , which confirms that one cannot separate absorption from scattering using only two measurement wavelengths.

Chromophore concentrations can be distinguished if the condition number k is small. The wavelengths whose distance is shortest to the point with the smallest condition number cluster at 710±16 and 973±9nm . On the other hand, the wavelengths with shortest distance to the point (SSD,k)=(0,0) cluster at 694±7 and 883±23nm . The inclusion of the PMDF overlap condition has a clear effect on the largest wavelength.

For the three-layer model, the wavelength pair with the shortest distance to the point (SSD,k)=(0,0) is 704±7 and 887±12nm , which is very similar to the optimal wavelength set found for the homogeneous case.

The influence of μa on the absorption and diffusion PMDFs is identical; high absorption coefficients lead to narrower PMDFs. A similar tendency is observed when μs has a high value for the absorption PMDFs, although the diffusion PMDFs become broader. Nevertheless, the SSD for both absorption and diffusion PMDFs is similar. The wavelength sets obtained using the diffusion PMDFs are identical to those found using the absorption PMDFs. The largest SSD occurs for wavelengths paired with 970nm , which corresponds to the absorption peak of water. As mentioned previously, the sum of squared differences SSD for diffusion is slightly smaller than for absorption.


PMDF Comparison



The relative volume was calculated for the absorption PMDFs (PMDFμa) and diffusion PMDFs (PMDFκ) , for both head models. Figure 8 shows the relative volumes for the different wavelengths and the value of N650 for each case. The volume of the PMDFμa is larger than that of the PMDFκ . For the homogeneous model, the PMDFμa volume resembles an inverted μa spectrum of brain tissue, because for large μa the PMDF volume becomes smaller and for small μa the volume becomes larger. For the PMDFκ , the volume decreases as expected, given that the volume decreases with decreasing μs . However, the volume for PMDFκ does not vary as much as for PMDFμa . The relative volumes for the layered model are similar to those obtained for the homogeneous case.

Fig. 8

Relative volume of (a) the PMDFμa and (b) PMDFκ for the homogeneous head model.




Figure 9 shows the difference between the depth FWHMs and that corresponding to the wavelength 650nm , FWHM650 . In general, the difference is <0.6mm , which indicates that the spread of regions of higher sensitivity does not vary much with wavelength. The FWHM differences are smaller for PMDFκ , in particular for the homogeneous model, which shows a relatively flat spectrum for wavelengths of <970nm .

Fig. 9

FWHM for the depth direction of the (a, c) PMDFμa and (b, d) PMDFκ profiles for the homogeneous (a, b) head model and (c, d) layered model.




PD is larger for the PMDFμa , as shown in Fig. 10 . The graphs show the differences with respect to the PMDF PD at 650nm , PD650 . For the homogeneous model, the maximum depth is 15.6mm and occurs for a wavelength of 720nm . The lowest PD is 13.2mm and occurs for a wavelength of 970nm . This means that the maximum PD difference is 2.4mm . For the layered model, the maximum penetration depth is 14.4mm for a wavelength of 720nm . For a wavelength of 970nm , the PD attains a minimum value of 12.0mm . The largest difference between PD is 2.4mm . As a consequence of the voxel size, which only allows discrete spatial steps, the relative PD spectra exhibit a steplike behavior.

Fig. 10

Penetration depth of the (a, c) PMDFμa and (b, d) PMDFκ profiles for the homogeneous (a, b) head model and (c, d) layered model.


The PMDFκ exhibits a small variation in the PD for the different wavelengths (Fig. 10). For the homogeneous model, the combination of low μa and high μs for wavelengths from 670to840nm results in a broader PMDFκ with higher PD. The maximum PD is 12.6mm , and the largest difference between PD is 0.6mm . The μs for skull decays faster than for brain tissue, and at wavelengths of > 750nm their μs values diverge significantly. When the external layers have lower μs and μa than the inner layer, the sensitivity profiles shift toward the surface. The high absorption of the brain tissue at 970nm reduces the interrogation depth to 9mm , compared to the maximum at 11.4mm .


Image Reconstruction

Images were reconstructed from simulated measurements at wavelengths selected using our new proposed method, and from measurements at wavelengths selected using the method of Corlu 16 Figure 11 shows the xy cross-sectional images at z=11.7mm [Fig. 11] and z=8.4mm [Fig. 11], using measurement wavelengths at 680, 720, and 830nm , simultaneously. Figure 12 shows the xy cross-sectional images at z=11.7mm [Fig. 12] and z=8.4mm [Fig. 12], using measurement wavelengths at 650, 720, and 910nm , simultaneously. Note that the color scales are not the same to enhance the differences between the images. For both sets of wavelengths, the images exhibited cross-talk between chromophores and also scattering. Cross-talk is particularly significant between oxy- and deoxy-hemoglobin concentrations in the deoxy-hemoglobin reconstruction and more evident in the images reconstructed using the wavelengths selected by our method. However, the deoxy-hemoglobin perturbation appears closer to the real position in the images reconstructed from wavelengths selected by our method than in the images obtained from wavelengths selected using the two-condition criteria. The [HbO2] is underestimated in all the cases. The [HHb] is overestimated at z=8.4mm and underestimated at z=11.7mm . For comparison, images were reconstructed from simulated measurements at two wavelengths that are commonly used, 780 and 850nm , and a third wavelength located between these two at 820nm . Figure 13 shows the xy cross-sectional images for this wavelengths set at z=11.7mm and Fig. 13 the reconstructed images at z=8.4mm . Cross-talk is clearly visible in the oxy-hemoglobin concentration and scattering amplitude images. The calculated concentration and scattering amplitude values are underestimated, except for [HHb] calculated at z=8.4mm . However, the [HHb] does not appear in the images in the correct position.

Fig. 11

Reconstructed images at (a–c) z=11.7mm and (d–f) z=8.4mm of (a, d) oxy-hemoglobin, (b, e) deoxy-hemoglobin, and (c, f) scattering amplitude from measurements at wavelengths 680, 720, and 830nm , for the homogeneous model.


Fig. 12

Reconstructed images at (a–c) z=11.7mm and (d–f) z=8.4mm of (a, d) oxy-hemoglobin, (b, e) deoxy-hemoglobin, and (c, f) scattering amplitude from measurements at wavelengths 650, 720, and 910nm , for the homogeneous model.


Fig. 13

Reconstructed images at (a–c) z=11.7mm and (d–f) z=8.4mm of (a, d) oxy-hemoglobin, (b, e) deoxy-hemoglobin, and (c, f) scattering amplitude from measurements at wavelengths 780, 820, and 850nm , for the homogeneous model.


The same simulation study was performed using four wavelengths, and the corresponding xy cross-sectional images at z=11.7mm and z=8.4mm obtained using the wavelengths found using our method are shown in Figs. 14 and 14 , respectively. Because of the limited sensitivity of the measurement to scattering to regions deeper than 12mm below the surface, the reconstructions were found to have less cross-talk at z=8.4mm than at the true target depth z=11.7mm . Figure 15 shows the xy cross-sectional images at z=11.7mm [Fig. 15] and z=8.4mm [Fig. 15], using the wavelengths selected by the two-criteria method. Reconstructions at z=8.4mm for both sets of wavelengths were similar and showed very small cross-talk. Nevertheless, the scattering amplitude and deoxy-hemoglobin concentration calculated at z=8.4mm were overestimated. The slightly better reconstructions at z=11.7mm obtained using our wavelength set could be due to the higher sensitivity of these wavelengths to the depth at which the targets are located. Furthermore, even though the sensitivity to scattering is quite limited in deeper regions, the PD of the PMDFκ has its maximum at these wavelengths. For comparison, images were reconstructed using the wavelengths 750, 780, 820, and 850nm . Figure 16 shows the xy cross-sectional images at z=11.7mm , and Fig. 16 shows the images at z=8.4mm , obtained using the latter measurement set. Cross-talk is visible in the images; the calculated chromophore concentrations and scattering amplitude are underestimated; and the deoxy-hemoglobin perturbation appears misplaced in the image.

Fig. 14

Reconstructed images at (a–c) z=11.7mm and (d–f) z=8.4mm of (a, d) oxy-hemoglobin, (b, e) deoxy-hemoglobin, and (c, f) scattering amplitude from measurements at wavelengths 680nm , 710, 730, and 830nm , for the homogeneous model.


Fig. 15

Reconstructed images at (a–c) z=11.7mm and (d–f) z=8.4mm of (a, d) oxy-hemoglobin, (b, e) deoxy-hemoglobin, and (c, f) scattering amplitude from measurements at wavelengths 650, 710, 730, and 910nm , for the homogeneous model.


Fig. 16

Reconstructed images at (a–c) z=11.7mm and (d–f) z=8.4mm of (a, d) oxy-hemoglobin, (b, e) deoxy-hemoglobin, and (c, f) scattering amplitude from measurements at wavelengths 750, 780, 820, and 850nm , for the homogeneous model.


The simulation results indicate that four measurement wavelengths are required, in order to reconstruct images with negligible cross-talk, from reflection measurements and using linear multispectral reconstruction.

For the three-layer adult head model presented here, the sensitivity to scattering perturbation occurring in the brain region is very low; hence, it is not possible to separate the two chromophores and scattering amplitude simultaneously.

Finally, simulations were performed using the same medium as before, but without the scattering perturbation and for two wavelengths only. Figure 17 shows the reconstructions at z=11.7mm and z=8.4mm obtained from measurements at wavelengths found using our method (690 and 880nm ). The images show a good separation between chromophores, but at z=8.4mm , the calculated chromophore concentrations are overestimated and, at z=11.7mm , are slightly underestimated. The images reconstructed from measurements at wavelengths found using the two criteria method (710 and 970nm ) exhibit a small amount of cross-talk (Fig. 18 ). The presence of cross-talk could be due to sensitivity differences between the two wavelengths. At 970nm , only the top of the target is interrogated, whereas the light at 710nm is likely to travel through the whole target volume.

Fig. 17

Reconstructed images at (a, b) z=11.7mm and (c, d) z=8.4mm of (a, c) oxy-hemoglobin, and (b, d) deoxy-hemoglobin from measurements at wavelengths 690 and 880nm , for the homogeneous model.


Fig. 18

Reconstructed images at (a, b) z=11.7mm and (c, d) z=8.4mm of (a, c) oxy-hemoglobin, and (b, d) deoxy-hemoglobin from measurements at wavelengths 710 and 970nm , for the homogeneous model.


All the simulations were repeated for up to three times larger concentration and scattering amplitude changes, and the results showed a similar tendency.

In applications of optical topography, the true optical properties of the medium are usually unknown; hence, the sensitivity matrix is usually calculated using an estimate of the average optical properties, and subsequently used to reconstruct images from measurements at different wavelengths. Given that the optical properties of the medium are wavelength dependent, the reconstructed images are affected by using sensitivity matrices generated for optical properties different from the true properties. If the estimate of the optical properties is significantly different from the true values, then the absorption perturbation is likely to appear misplaced in the image, particularly in the z direction.



A method of determining the optimum wavelengths for optical topography is presented. This method is based on the multispectral method and is an extension of Corlu’s wavelength selection method,16 where a third criterion is introduced that minimizes the differences between interrogated volumes at different wavelengths. Including the sensitivity profiles similarity criterion in the selection of the optimal wavelengths has a clear effect on the resultant wavelength sets. The insertion of two superficial layers in the adult head model had an influence on the results, in particular, for the largest wavelength mean value, which shows that selection depends on the accuracy of the head model. It is possible to separate [HbO2] and [HHb] from measurements performed at two wavelengths. However, one cannot distinguish between absorption and scattering. When the sensitivity profiles similarity condition is included in the wavelength selection method, the largest wavelength mean value changes in comparison to that obtained using the condition k alone.

The main characteristics of the optically interrogated volumes (volume, FWHM, and PD) were quantified, in order to determine how measurements at different NIR wavelengths may be combined to provide information about the volume. Particular attention is given to the wavelengths selected using our wavelength selection method and Corlu’s method.16

If we consider the thickness of the head superficial layers to be 11mm and the gray matter to be 4mm , then in theory for the homogeneous model, the lowest wavelength selected using the two-criteria method penetrates 2.8mm into the gray matter, whereas the midrange wavelengths penetrate 4.6mm , which means that some light could reach the white matter. Therefore, the information contained in the measurements at these wavelengths could be significantly different. Including our PMDF overlap condition in the selection of the optimal wavelengths reduces the difference in the PD from 1.8to0.6mm . The first and last wavelengths, with mean values 680 and 830nm , respectively, penetrate 4mm into the brain tissue. This value is closer to that obtained for the central wavelengths, which is 4.6mm . The volumes interrogated are also larger and more similar for the wavelengths selected using our method. For the PMDFκ , the changes in the PD are smaller and the maximum PD is only 12.6mm , hence confined to the gray matter. However, the differences between PD are smaller when our method is used.

For the three-layer adult head model, the discrepancies between the interrogated volumes at different wavelengths are smaller. However, the volumes are more similar for the wavelengths selected using our method than using the two-selection criteria. In terms of PD, for the PMDFμa , the variations are smaller than for the homogeneous model. For the central wavelengths of the selected wavelength sets light penetrates 3.5mm into the gray matter. For the lowest wavelengths obtained with our method, light penetrates 2.9mm into the brain. For the remaining wavelengths, a maximum depth of 2.2mm from the brain surface is probed. Although the differences between PD for the different wavelengths are identical for both selection methods, when using our method it optimizes for the wavelength set with higher PD, simultaneously with the best separation between chromophores, absorption, and scattering. The PMDF spread (i.e., the FWHM and volume) is also larger and more similar for our wavelength set. For the PMDFκ , the maximum PD is only 11.4mm ; hence, light hardly reaches the brain surface. The differences PMDFκ are much smaller than for the absorption PMDFs.

From this analysis, it was shown that for the adult human head it can be assumed that the volumes of interrogated tissue at different NIR wavelengths are identical. Hence, measurements performed at different NIR wavelengths provide information from the same brain regions.

This method could be applied to other types of tissue. For example, for measurements of muscle, breast, or neonate brain. For muscle, the highest absorption occurs at 650nm and the lowest at 850nm .40 For these wavelengths, the PD is 15 and 21.6mm , respectively. This results in a PD maximum difference of 6.6mm . For studies of muscle exercise, of the forearm for example, which has a diameter of 7cm , a difference of 7mm is quite significant. The influence of the skin and fat layer on the light propagation is likely to increase the PD, and it should depend on the thickness of these layers. For breast tissue, using the optical properties from Cubeddu, 41 the largest differences in the absorption coefficient occur at wavelengths 720 and 970nm , with a PD of 22 and 16mm , respectively. Therefore, the maximum PD difference is 6mm . The main objective of breast diffuse optical imaging is tumor detection. It is generally assumed that a tumor may be distinguished from surrounding healthy tissue on the basis of its apparent blood volume and oxygenation. Overlapping sensitivities are required to provide accurate measures of these parameters and reduce the uncertainty in the measured spatial location of the tumour.

Light propagation in the neonatal brain is different from the adult brain tissue, because the surrounding layers are thinner and the optical properties are different. The μs is lower for the neonatal brain tissue.27 Because of the high scattering of the neonatal brain, the sensitive regions of the PMDF should be confined to the gray matter. However, the cortical thickness is only 2.5mm , meaning that light is likely to penetrate into deeper regions of the white matter.42

The PMDFs of other types of measurements, either frequency or time domain, can also be analyzed using our method. For example, the region of higher sensitivity of the mean time or phase PMDF is located deeper in the tissue than that of the CW PMDFs.43

It is clear that the adult head model had a large influence on the results. A more complete model of the adult head should include the cerebrospinal fluid (CSF) surrounding the brain tissue. However, the diffusion equation-based model is only valid for high scattering tissues and cannot be applied to the low scattering CSF. Monte Carlo simulations of light transport can be used, but they require long computational times. An alternative method is the hybrid radiosity-diffusion method, where the light propagation in high scattering media is calculated using the diffusion equation and the low scattering region by radiosity theory.44, 45

The inclusion of a CSF layer in our adult head model would not significantly affect our results because the high sensitivity regions of the PMDF would still be confined to superficial regions of the brain and because this layer has very little wavelength dependence. The PD of the light in models with and without CSF is almost the same, for source-detector separations of 30 and 40mm .31

A further consideration is the error associated with the extinction coefficient for the different chromophores. The spectra found in the literature, for certain chromophores, show some variation. Based on Corlu’s method16 for wavelength optimization, Brendel and Nielsen46 added a criterion that evaluates the reconstruction errors due to uncertainty in the extinction coefficients. From simulations and breastlike phantom measurements, they found a set of optimum wavelengths almost independent on the uncertainties in the extinction coefficients spectra. Another potential source of error is the assumed values of the chromophore concentrations for each head layer. This influences the optical properties of the layers, which could possibly have an effect on the spatial sensitivity profiles.

Contrary to previous findings, our simulation results indicate that four measurement wavelengths are required in order to reconstruct images with low cross-talk from reflection measurements and using linear multispectral reconstruction. Images reconstructed from simulated measurements at wavelengths selected using our method exhibit a smaller cross-talk than those reconstructed from measurements selected using the two criteria. Therefore, the use of measurements with overlapping sensitivities slightly improves the image reconstruction.


The work has been supported by a scholarship awarded to T.C. by Fundação para a Ciência e a Tecnologia, Portugal. This work has been supported in part by funding from the ECs seventh framework programme under Grant Agreement No. FP7-HEALTH-2007-201076.


1.  A. P. Gibson, T. Austin, N. L. Everdell, M. Schweiger, S. R. Arridge, J. H. Meek, J. S. Wyatt, D. T. Delpy, and J. C. Hebden, “Three-dimensional whole-head optical tomography of passive motor evoked responses in the neonate,” Neuroimage1053-8119 30, 521–528 (2006). 10.1016/j.neuroimage.2005.08.059 Google Scholar

2.  L. C. Enfield, A. P. Gibson, N. L. Everdell, D. T. Delpy, M. Schweiger, S. R. Arridge, C. Richardson, M. Keshtgar, M. Douek, and J. C. Hebden, “Three-dimensional time-resolved optical mammography of the uncompressed breast,” Appl. Opt.0003-6935 46, 3628–3638 (2007). 10.1364/AO.46.003628 Google Scholar

3.  B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A.0027-8424 104, 12169–12174 (2007). 10.1073/pnas.0611266104 Google Scholar

4.  S. R. Hintz, D. A. Benaron, A. M. Zourabian, D. K. Stevenson, and D. A. Boas, “Bedside functional imaging of the premature infant brain during passive motor activation,” J. Perinat. Med.0300-5577 29, 335–343 (2001). 10.1515/JPM.2001.048 Google Scholar

5.  A. Custo, D. A. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. E. L. Grimson, and W. Wells, “Anatomical atlas-guided diffuse optical tomography of brain activation,” Neuroimage1053-8119 49, 561–567 (2010). 10.1016/j.neuroimage.2009.07.033 Google Scholar

6.  R. Choe, S. D. Konecky, A. Corlu, K. Lee, T. Durduran, D. R. Busch, S. Pathak, B. J. Czerniecki, J. Tchou, D. L. Fraker, A. DeMichele, B. Chance, S. R. Arridge, M. Schweiger, J. P. Culver, M. D. Schnall, M. E. Putt, M. A. Rosen, and A. G. Yodh, “Differentiation of benign and malignant breast tumors by in vivo three-dimensional parallel-plate diffuse optical tomography,” J. Biomed. Opt.1083-3668 14, 024020 (2009). 10.1117/1.3103325 Google Scholar

7.  J. C. Hebden, “Advances in optical imaging of the newborn infant brain,” Psychophysiology0048-5772 40, 501–510 (2003). 10.1111/1469-8986.00052 Google Scholar

8.  K. Uludağ, J. Steinbrink, A. Villringer, and H. Obriga, “Separability and cross talk: optimizing dual wavelength combinations for near-infrared spectroscopy of the adult head,” Neuroimage1053-8119 22, 583–589 (2004). 10.1016/j.neuroimage.2004.02.023 Google Scholar

9.  Y. Yamashita, A. Maki, and H. Koizumi, “Wavelength dependence of the precision of noninvasive optical measurement of oxy-, deoxy-, and total-hemoglobin concentration,” Med. Phys.0094-2405 28, 1108–1114 (2001). 10.1118/1.1373401 Google Scholar

10.  G. Strangman, M. A. Franceschini, and D. A. Boas, “Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters,” Neuroimage1053-8119 18, 865–879 (2003). 10.1016/S1053-8119(03)00021-1 Google Scholar

11.  N. Okui and E. Okada, “Wavelength dependence of crosstalk in dual-wavelength measurement of oxy- and deoxy-hemoglobin,” J. Biomed. Opt.1083-3668 10, 011015 (2005). 10.1117/1.1846076 Google Scholar

12.  S. Umeyama and T. Yamada, “A new crosstalk measure of near-infrared spectroscopy and its application to wavelength combination optimization,” J. Biomed. Opt.1083-3668 14, 034017 (2009). 10.1117/1.3147402 Google Scholar

13.  H. Sato, M. Kiguchi, and A. Maki, “Wavelength dependence of effective pathlength factor in noninvasive optical measurements of human brain functions,” Jpn. J. Appl. Phys.0021-4922 45, L361–L363 (2006). 10.1143/JJAP.45.L361 Google Scholar

14.  S. R. Arridge and W. R. B. Lionheart, “Nonuniqueness in diffusion-based optical tomography,” Opt. Lett.0146-9592 23, 882–884 (1998). 10.1364/OL.23.000882 Google Scholar

15.  A. Corlu, T. Durduran, R. Choe, M. Schweiger, E. M. C. Hillman, S. R. Arridge, and A. G. Yodh, “Uniqueness and wavelength optimization in continuous-wave multispectral diffuse optical tomography,” Opt. Lett.0146-9592 28, 2339–2341 (2003). 10.1364/OL.28.002339 Google Scholar

16.  A. Corlu, R. Choe, T. Durduran, K. Lee, M. Schweiger, S. R. Arridge, E. M. C. Hillman, and A. G. Yodh, “Diffuse optical tomography with spectral constrains and wavelength optimization,” Appl. Opt.0003-6935 44, 2082–2093 (2005). 10.1364/AO.44.002082 Google Scholar

17.  S. R. Arridge, “Optical tomography in medical imaging,” Inverse Probl.0266-5611 15, R41–R93 (1999). 10.1088/0266-5611/15/2/022 Google Scholar

18.  M. A. OLeary, D. A. Boas, B. Chance, and A. G. Yodh, “Experimental images of heterogeneous turbid media by frequency domain diffusing photon tomography,” Opt. Lett.0146-9592 20, 4268 (1995). Google Scholar

19.  M. Cheney, D. Isaacson, J. C. Newell, S. Simske, and J. C. Goble, “NOSER: an algorithm for solving the inverse conductivity problem,” Int. J. Imaging Syst. Technol.0899-9457 2, 66–75 (1990). 10.1002/ima.1850020203 Google Scholar

20.  A. Adler, T. Dai, and W. R. B. Lionheart, “Temporal image reconstruction in electrical impedance tomography,” Physiol. Meas0967-3334 28, S1–S11 (2007). 10.1088/0967-3334/28/7/S01 Google Scholar

21.  S. R. Arridge, “Photon measurement density functions. Part1: analytical forms,” Appl. Opt.0003-6935 34, 7395–7409 (1995). 10.1364/AO.34.007395 Google Scholar

22.  S. R. Arridge, J. C. Hebden, M. Schweiger, F. E. W. Schmidt, M. E. Fry, E. M. C. Hillman, H. Dehghani, and D. T. Delpy, “A method for three-dimensional time-resolved optical tomography,” Int. J. Imaging Syst. Technol.0899-9457 11, 2–11 (2000). 10.1002/(SICI)1098-1098(2000)11:1<2::AID-IMA2>3.0.CO;2-J Google Scholar

23.  E. Okada, M. Firbank, M. Schweiger, S. R. Arridge, M. Cope, and D. T. Delpy, “Theoretical and experimental investigation of near-infrared light propagation in a model of the adult head,” Appl. Opt.0003-6935 36, 21–31 (1997). 10.1364/AO.36.000021 Google Scholar

24.  T. S. Leung, C. E. Elwell, and S. T. Delpy, “Estimation of cerebral oxy- and deoxy-haemoglobin concentration changes in a layered adult head model using near-infrared spectroscopy and multivariate statistical analysis,” Phys. Med. Biol.0031-9155 50, 5783–5798 (2005). 10.1088/0031-9155/50/24/002 Google Scholar

25.  D. R. White, E. M. Widdowson, H. Q. Woodard, and J. W. Dickerson, “The composition of body tissues (II). fetus to young adult,” Br. J. Radiol.0007-1285 64, 149–159 (1991). 10.1259/0007-1285-64-758-149 Google Scholar

26.  M. Cope, “The application of near infrared spectroscopy to non invasive monitoring of cerebral oxygenation in the newborn infant,” PhD thesis, University College London (1991). Google Scholar

27.  P. van der Zee, “Measurement and modelling of the optical properties of human tissue in the near-infrared,” PhD thesis, University College London (1992). Google Scholar

28.  I. V. Meglinski and S. J. Matcher, “Quantitative assessment of skin layers absorption and skin reflectance spectra simulation in the visible and near-infrared spectral regions,” Physiol. Meas0967-3334 23, 741–753 (2002). 10.1088/0967-3334/23/4/312 Google Scholar

29.  George Zonios, Julie Bykowski, and Nikiforos Kollias, “Skin melanin, hemoglobin, and light scattering properties can be quantitatively assessed in vivo using diffuse reflectance spectroscopy,” J. Invest. Dermatol.0022-202X 117, 1452–1457 (2001). 10.1046/j.0022-202x.2001.01577.x Google Scholar

30.  S. L. Jacques, “Skin optics,” ⟨ http://omlc.ogi.edu/news/jan98/skinoptics.html⟩ (1998). Google Scholar

31.  E. Okada and D. T. Delpy, “Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer,” Appl. Opt.0003-6935 42, 2906–2914 (2003). 10.1364/AO.42.002906 Google Scholar

32.  S. J. Matcher, M. Cope, and D. T. Delpy, “Use of the water absorption spectrum to quantify tissue chromophore concentration changes in near-infrared spectroscopy,” Phys. Med. Biol.0031-9155 39, 177–196 (1994). 10.1088/0031-9155/39/1/011 Google Scholar

34.  R. L. P. van Veen, H. J. C. M. Sterenborg, A. Pifferi, A. Torricelli, and R. Cubeddu, “Determination of VIS- NIR absorption coefficients of mammalian fat, with time- and spatially resolved diffuse reflectance and transmission spectroscopy,” presented at OSA Annual BIOMED Topical Meeting (2004). Google Scholar

35.  S. J. Matcher, M. Cope, and D. T. Delpy, “In vivo measurements of the wavelength dependence of tissue-scattering coefficients between 760 and 900nm measured with time-resolved spectroscopy,” Appl. Opt.0003-6935 36, 386–396 (1997). 10.1364/AO.36.000386 Google Scholar

36.  M. Firbank, M. Hiraoka, M. Essenpreis, and D. T. Delpy, “Measurement of the optical properties of the skull in the wavelength range 650950nm,” Phys. Med. Biol.0031-9155 38, 503–510 (1993). 10.1088/0031-9155/38/4/002 Google Scholar

37.  C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo human skin and subcutaneous tissues measured using the monte carlo inversion technique,” Phys. Med. Biol.0031-9155 43, 2465–2478 (1998). 10.1088/0031-9155/43/9/003 Google Scholar

38.  A. N. Bashkatov, E. A. Genina, V. I. Kochubey, and V. V. Tuchin, “Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400to2000nm,” J. Phys. D0022-3727 38, 2543–2555 (2005). 10.1088/0022-3727/38/15/004 Google Scholar

39.  T. Correia, A. Banga, N. L. Everdell, A. P. Gibson, and J. C. Hebden, “A quantitative assessment of the depth sensitivity of an optical topography system using a solid dynamic tissue-phantom,” Phys. Med. Biol.0031-9155 54, 6277–6286 (2009). 10.1088/0031-9155/54/20/016 Google Scholar

40.  J. Xia, A. Weaver, D. E. Gerrard, and G. Yao, “Monitoring sarcomere structure changes in whole muscule using diffuse light reflectance,” J. Biomed. Opt.1083-3668 11, 040504 (2006). 10.1117/1.2234278 Google Scholar

41.  R. Cubeddu, C. D’Andrea, A. Pifferi, P. Taroni, A. Torricelli, and G. Valentini, “Effects of the menstrual cycle on the red and near-infrared optical properties of the human breast,” Photochem. Photobiol.0031-8655 72, 383–391 (2000). 10.1562/0031-8655(2000)072<0383:EOTMCO>2.0.CO;2 Google Scholar

42.  Y. Fukui, Y. Ajichi, and E. Okada, “Monte carlo prediction of near-infrared propagation in realistic adult and neonatal head models,” Appl. Opt.0003-6935 42, 2881–2887 (2003). 10.1364/AO.42.002881 Google Scholar

43.  S. R. Arridge and M. Schweiger, “Photon measurement density functions. Part2: finite element method calculations,” Appl. Opt.0003-6935 34, 8026–8037 (1995). 10.1364/AO.34.008026 Google Scholar

44.  S. R. Arridge, H. Dehghani, Martin Schweiger, and E. Okada, “The finite element model for the propagation of light in scattering media: a direct method for domains with nonscattering regions,” Med. Phys.0094-2405 27, 252–264 (2000). 10.1118/1.598868 Google Scholar

45.  H. Dehghani, S. R. Arridge, Martin Schweiger, and D. T. Delpy, “Optical tomography in the presence of void regions,” J. Opt. Soc. Am.0030-3941 17, 1659–1670 (2000). 10.1364/JOSAA.17.001659 Google Scholar

46.  B. Brendel and T. Nielsen, “Wavelengths optimization in multispectral diffuse optical tomography considering uncertainties in absorption spectra,” Proc. SPIE0277-786X 6629, 66290A (2007). 10.1117/12.728421 Google Scholar

Teresa M. Correia, Adam P. Gibson, Jeremy C. Hebden, "Identification of the optimal wavelengths for optical topography: a photon measurement density function analysis," Journal of Biomedical Optics 15(5), 056002 (1 September 2010). https://doi.org/10.1117/1.3484747

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