Combining near-infrared tomography and magnetic resonance imaging to study in vivo breast tissue: implementation of a Laplacian-type regularization to incorporate magnetic resonance structure

Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels

Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.

Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning

Significance

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired.

Aim

We aim to use US-guided DOT to achieve an automated, fast, and accurate classification of breast lesions.

Approach

We propose a two-stage classification strategy with deep learning. In the first stage, US images and histograms created from DOT perturbation measurements are combined to predict benign lesions. Then the non-benign suspicious lesions are passed through to the second stage, which combine US image features, DOT histogram features, and 3D DOT reconstructed images for final diagnosis.

Results

The first stage alone identified 73.0% of benign cases without image reconstruction. In distinguishing between benign and malignant breast lesions in patient data, the two-stage classification approach achieved an area under the receiver operating characteristic curve of 0.946, outperforming the diagnoses of all single-modality models and of a single-stage classification model that combines all US images, DOT histogram, and imaging features.

Conclusions

The proposed two-stage classification strategy achieves better classification accuracy than single-modality-only models and a single-stage classification model that combines all features. It can potentially distinguish breast cancers from benign lesions in near real-time.

Estimation of the Differential Pathlength Factor for Human Skin Using Monte Carlo Simulations

Near-infrared technology is an emerging non-invasive technique utilized for various medical applications. Recently, there have been many attempts to utilize NIR technology for the continues monitoring of blood glucose levels through the skin. Different approaches and designs have been proposed for non-invasive blood glucose measurements. Light photons penetrating the skin can undergo multiple scattering events, and the actual optical pathlength becomes larger than the source-to-detector separation (optode spacing) in the reflection-mode configuration. Thus, the differential pathlength factor (DPF) must be incorporated into the modified Beer-Lambert law. The accurate estimation of the DPF values will lead to an accurate quantification of the physiological variations within the tissue. In this work, the aim was to systematically estimate the DPF for human skin for a range of source-to-detector separations and wavelengths. The Monte Carlo (MC) method was utilized to mimic the different layers of human skin with different optical properties and blood and water volume fractions. This work could help improve the accuracy of the near-infrared technique in the measurement of physiological variations within skin tissue.

Three-compartment-breast (3CB) prior-guided diffuse optical tomography based on dual-energy digital breast tomosynthesis (DBT)

Diffuse optical tomography (DOT) is a non-invasive functional imaging modality that uses near-infrared (NIR) light to measure the oxygenation state and the concentration of hemoglobin. By complementarily using DOT with other anatomical imaging modalities, physicians can diagnose more accurately through additional functional image information. In breast imaging, diagnosis of dense breasts is often challenging because the bulky fibrous tissues may hinder the correct tumor characterization. In this work, we proposed a three-compartment-breast (3CB) decomposition-based prior-guided optical tomography for enhancing DOT image quality. We conjectured that the 3CB prior would lead to improvement of the spatial resolution and also of the contrast of the reconstructed tumor image, particularly for the dense breasts. We conducted a Monte-Carlo simulation to acquire dual-energy X-ray projections of a realistic 3D numerical breast phantom and performed digital breast tomosynthesis (DBT) for setting up a 3CB model. The 3CB prior was then used as a structural guide in DOT image reconstruction. The proposed method resulted in the higher spatial resolution of the recovered tumor even when the tumor is surrounded by the fibroglandular tissues compared with the typical two-composition-prior method or the standard Tikhonov regularization method.

Data-specific mask-guided image reconstruction for diffuse optical tomography

Conventional approaches in diffuse optical tomography (DOT) image reconstruction often address the ill-posed inverse problem via regularization with a constant penalty parameter, which uniformly smooths out the solution. In this study, we present a data-specific mask-guided scheme that incorporates a prior mask constraint into the image reconstruction framework. The prior mask was created from the DOT data itself by exploiting the multi-measurement vector formulation. We accordingly propose two methods to integrate the prior mask into the reconstruction process. First, as a soft prior by exploiting a spatially varying regularization. Second, as a hard prior by imposing a region-of-interest-limited reconstruction. Furthermore, the latter method iterates between discrete and continuous steps to update the mask and optical parameters, respectively. The proposed methods showed enhanced optical contrast accuracy, improved spatial resolution, and reduced noise level in DOT reconstructed images compared with the conventional approaches such as the modified Levenberg-Marquardt approach and the l₁-regularization based sparse recovery approach.

Recent Developments in Instrumentation of Functional Near-Infrared Spectroscopy Systems

In the last three decades, the development and steady improvement of various optical technologies at the near-infrared region of the electromagnetic spectrum has inspired a large number of scientists around the world to design and develop functional near-infrared spectroscopy (fNIRS) systems for various medical applications. This has been driven further by the availability of new sources and detectors that support very compact and wearable system designs. In this article, we review fNIRS systems from the instrumentation point of view, discussing the associated challenges and state-of-the-art approaches. In the beginning, the fundamentals of fNIRS systems as well as light-tissue interaction at NIR are briefly introduced. After that, we present the basics of NIR systems instrumentation. Next, the recent development of continuous-wave, frequency-domain, and time-domain fNIRS systems are discussed. Finally, we provide a summary of these three modalities and an outlook into the future of fNIRS technology.

Target depth-regularized reconstruction in diffuse optical tomography using ultrasound segmentation as prior information

Ultrasound (US)-guided diffuse optical tomography (DOT) is a promising non-invasive functional imaging technique for diagnosing breast cancer and monitoring breast cancer treatment response. However, because larger lesions are highly absorbing, reconstructions of these lesions using reflection geometry may exhibit light shadowing, which leads to inaccurate quantification of their deeper portions. Here we propose a depth-regularized reconstruction algorithm combined with a semi-automated interactive neural network (CNN) for depth-dependent reconstruction of absorption distribution. CNN segments co-registered US to extract both spatial and depth priors, and the depth-regularized algorithm incorporates these parameters into the reconstruction. Through simulation and phantom data, the proposed algorithm is shown to significantly improve the depth distribution of reconstructed absorption maps of large targets. Evaluated with 26 patients with larger breast lesions, the algorithm shows 2.4 to 3 times improvement in the top-to-bottom reconstructed homogeneity of the absorption maps for these lesions.

Method for Quantitative Broadband Diffuse Optical Spectroscopy of Tumor-Like Inclusions

A hybrid reflectance-based diffuse optical imaging (DOI) technique combining discrete wavelength frequency-domain (FD) near-infrared spectroscopy (NIRS) with broadband continuous wave NIRS measurements was developed to quantify the broadband optical properties of deep tumor-like inclusions. This method was developed to more accurately measure the broadband optical properties of human tumors using a compact handheld imaging probe and without requiring a priori spectral constraints. We simulated the reconstruction of absorption and scattering spectra (650–1000 nm) of human breast tumors in a homogeneous background at depths of 0 to 10 mm. The hybrid DOI technique demonstrated enhanced performance in reconstruction of optical absorption with a mean accuracy over all 71 wavelengths of 8.39% versus 32.26% for a 10 mm deep tumor with the topographic DOI method. The new hybrid technique was also tested and validated on two heterogeneous tissue-simulating phantoms with inclusion depths of 2, 7, and 9 mm. The mean optical absorption accuracy over all wavelengths was similarly improved up to 5x for the hybrid DOI method versus topographic DOI for the deepest inclusions.

Hybrid time-domain and continuous-wave diffuse optical tomography instrument with concurrent, clinical magnetic resonance imaging for breast cancer imaging

Diffuse optical tomography has demonstrated significant potential for clinical utility in the diagnosis and prognosis of breast cancer, and its use in combination with other structural imaging modalities improves lesion localization and the quantification of functional tissue properties. Here, we introduce a hybrid diffuse optical imaging system that operates concurrently with magnetic resonance imaging (MRI) in the imaging suite, utilizing commercially available MR surface coils. The instrument acquires both continuous-wave and time-domain diffuse optical data in the parallel-plate geometry, permitting both absolute assignment of tissue optical properties and three-dimensional tomography; moreover, the instrument is designed to incorporate diffuse correlation spectroscopic measurements for probing tissue blood flow. The instrument is described in detail here. Image reconstructions of a tissue phantom are presented as an initial indicator of the system's ability to accurately reconstruct optical properties and the concrete benefits of the spatial constraints provided by concurrent MRI. Last, we briefly discuss how various data combinations that the instrument could facilitate, including tissue perfusion, can enable more comprehensive assessment of lesion physiology.

Image Restoration for Fluorescence Planar Imaging with Diffusion Model

Fluorescence planar imaging (FPI) is failure to capture high resolution images of deep fluorochromes due to photon diffusion. This paper presents an image restoration method to deal with this kind of blurring. The scheme of this method is conceived based on a reconstruction method in fluorescence molecular tomography (FMT) with diffusion model. A new unknown parameter is defined through introducing the first mean value theorem for definite integrals. System matrix converting this unknown parameter to the blurry image is constructed with the elements of depth conversion matrices related to a chosen plane named focal plane. Results of phantom and mouse experiments show that the proposed method is capable of reducing the blurring of FPI image caused by photon diffusion when the depth of focal plane is chosen within a proper interval around the true depth of fluorochrome. This method will be helpful to the estimation of the size of deep fluorochrome.

Two step imaging reconstruction using truncated pseudoinverse as a preliminary estimate in ultrasound guided diffuse optical tomography

Due to the correlated nature of diffused light, the problem of reconstructing optical properties using diffuse optical tomography (DOT) is ill-posed. US-, MRI- or x-ray-guided DOT approaches can reduce the total number of parameters to be estimated and improve optical reconstruction accuracy. However, when the target volume is large, the number of parameters to estimate can exceed the number of measurements, resulting in an underdetermined imaging model. In such cases, accurate image reconstruction is difficult and regularization methods should be employed to obtain a useful solution. In this manuscript, a simple two-step reconstruction method that can produce useful image estimates in DOT is proposed and investigated. In the first step, a truncated Moore-Penrose Pseudoinverse solution is computed to obtain a preliminary estimate of the image that can be reliably determined from the measured data; subsequently, this preliminary estimate is incorporated into the design of a penalized least squares estimator that is employed to compute the final image estimate. By use of phantom data, the proposed method was demonstrated to yield more accurate images than those produced by conventional reconstruction methods. The method was also evaluated with clinical data that included 10 benign and 10 malignant cases. The capability of reconstructing high contrast malignant lesions was demonstrated to be improved by use of the proposed method.

Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis

Diffuse optical tomography (DOT) is emerging as a noninvasive functional imaging method for breast cancer diagnosis and neoadjuvant chemotherapy monitoring. In particular, the multimodal approach of combining DOT with x-ray digital breast tomosynthesis (DBT) is especially synergistic as DBT prior information can be used to enhance the DOT reconstruction. DOT, in turn, provides a functional information overlay onto the mammographic images, increasing sensitivity and specificity to cancer pathology. We describe a dynamic DOT apparatus designed for tight integration with commercial DBT scanners and providing a fast (up to 1 Hz) image acquisition rate to enable tracking hemodynamic changes induced by the mammographic breast compression. The system integrates 96 continuous-wave and 24 frequency-domain source locations as well as 32 continuous wave and 20 frequency-domain detection locations into low-profile plastic plates that can easily mate to the DBT compression paddle and x-ray detector cover, respectively. We demonstrate system performance using static and dynamic tissue-like phantoms as well as in vivo images acquired from the pool of patients recalled for breast biopsies at the Massachusetts General Hospital Breast Imaging Division.

Diffuse optical tomography reconstruction method using ultrasound images as prior for regularization matrix

Ultrasound-guided diffuse optical tomography (DOT) is a promising imaging technique that maps hemoglobin concentrations of breast lesions to assist ultrasound (US) for cancer diagnosis and treatment monitoring. The accurate recovery of breast lesion optical properties requires an effective image reconstruction method. We introduce a reconstruction approach in which US images are encoded as prior information for regularization of the inversion matrix. The framework of this approach is based on image reconstruction package “NIRFAST.” We compare this approach to the US-guided dual-zone mesh reconstruction method, which is based on Born approximation and conjugate gradient optimization developed in our laboratory. Results were evaluated using phantoms and clinical data. This method improves classification of malignant and benign lesions by increasing malignant to benign lesion absorption contrast. The results also show improvements in reconstructed lesion shapes and the spatial distribution of absorption maps.

Diffuse optical tomography for breast cancer imaging guided by computed tomography: A feasibility study

Diffuse optical tomography (DOT) has attracted attentions in the last two decades due to its intrinsic sensitivity in imaging chromophores of tissues such as hemoglobin, water, and lipid. However, DOT has not been clinically accepted yet due to its low spatial resolution caused by strong optical scattering in tissues. Structural guidance provided by an anatomical imaging modality enhances the DOT imaging substantially. Here, we propose a computed tomography (CT) guided multispectral DOT imaging system for breast cancer imaging. To validate its feasibility, we have built a prototype DOT imaging system which consists of a laser at the wavelength of 650 nm and an electron multiplying charge coupled device (EMCCD) camera. We have validated the CT guided DOT reconstruction algorithms with numerical simulations and phantom experiments, in which different imaging setup parameters, such as projection number of measurements and width of measurement patch, have been investigated. Our results indicate that an air-cooling EMCCD camera is good enough for the transmission mode DOT imaging. We have also found that measurements at six angular projections are sufficient for DOT to reconstruct the optical targets with 2 and 4 times absorption contrast when the CT guidance is applied. Finally, we have described our future research plan on integration of a multispectral DOT imaging system into a breast CT scanner.

Combined Diffuse Optical Tomography (DOT) and MRI System for Cancer Imaging in Small Animals

Recently, there has been a great amount of interest in developing multi-modality imaging techniques for oncologic research and clinical studies with the aim of obtaining complementary information and, thus, improving the detection and characterization of tumors. In this present work, the details of a combined MR-diffuse optical imaging system for dual-modality imaging of small animals are given. As a part of this effort, a multi-spectral frequency domain diffuse optical tomography system is integrated with an MRI system. Here, a network analyzer provides the rf modulation signal for the laser diodes and measures the amplitude and the phase of the detected signals. Photomultiplier tubes are utilized to measure low-level signals. The integration of this optical imaging system with the 4T MRI system is realized by incorporating a fiber adaptive interface inside the MR magnet. Coregistration is achieved by a special probe design utilizing fiducial markers. A finite element algorithm is used to solve the diffusion equation and an inverse solver based on this forward solver is implemented to calculate the absorption and scattering maps from the acquired data. The MR a priori information is used to guide the optical reconstruction algorithm. Phantom studies show that the absorption coefficient of a 7 mm inclusion in an irregular object located in 64 mm phantom is recovered with 11% error when MR a priori information is used. ENU induced tumor model is used to test the performance of the system in vivo.

Reconstruction of in vivo fluorophore concentration variation with structural priors and smooth penalty

Reconstruction of fluorophore concentration variation in fluorescence molecular tomography is expected to reveal the metabolic processes of fluorescent biomarkers in vivo. However, the complicated and strong noise within in vivo data inhibits its applications for in vivo cases. A smooth penalty method is presented in this work to suppress the noise. The method is based on a recursive reconstruction scheme which reconstructs the fluorophore concentration variation rates (FCVRs) of two neighboring frames at the same time within an inner iteration. In addition, the performance of the Laplacian-type regularization incorporating structural priors is investigated. Results of simulations suggest that the smooth penalty method almost has no influence on the reconstructed FCVRs when the target curve is smooth, and results of in vivo experiments on mice indicate that the method is capable of suppressing the noise and achieving smooth time courses of fluorescent yield. Results of both the simulations and in vivo experiments demonstrate that the Laplacian-type regularization can improve the image quality.

Digital Breast Tomosynthesis guided Near Infrared Spectroscopy: Volumetric estimates of fibroglandular fraction and breast density from tomosynthesis reconstructions

A multimodality system combining a clinical prototype digital breast tomosynthesis with its imaging geometry modified to facilitate near-infrared spectroscopic imaging has been developed. The accuracy of parameters recovered from near-infrared spectroscopy is dependent on fibroglandular tissue content. Hence, in this study, volumetric estimates of fibroglandular tissue from tomosynthesis reconstructions were determined. A kernel-based fuzzy c-means algorithm was implemented to segment tomosynthesis reconstructed slices in order to estimate fibroglandular content and to provide anatomic priors for near-infrared spectroscopy. This algorithm was used to determine volumetric breast density (VBD), defined as the ratio of fibroglandular tissue volume to the total breast volume, expressed as percentage, from 62 tomosynthesis reconstructions of 34 study participants. For a subset of study participants who subsequently underwent mammography, VBD from mammography matched for subject, breast laterality and mammographic view was quantified using commercial software and statistically analyzed to determine if it differed from tomosynthesis. Summary statistics of the VBD from all study participants were compared with prior independent studies. The fibroglandular volume from tomosynthesis and mammography were not statistically different (p=0.211, paired t-test). After accounting for the compressed breast thickness, which were different between tomosynthesis and mammography, the VBD from tomosynthesis was correlated with (r =0.809, p<0.001), did not statistically differ from (p>0.99, paired t-test), and was linearly related to, the VBD from mammography. Summary statistics of the VBD from tomosynthesis were not statistically different from prior studies using high-resolution dedicated breast computed tomography. The observation of correlation and linear association in VBD between mammography and tomosynthesis suggests that breast density associated risk measures determined for mammography are translatable to tomosynthesis. Accounting for compressed breast thickness is important when it differs between the two modalities. The fibroglandular volume from tomosynthesis reconstructions is similar to mammography indicating suitability for use during near-infrared spectroscopy.

Direct regularization from co-registered anatomical images for MRI-guided near-infrared spectral tomographic image reconstruction

Combining anatomical information from high resolution imaging modalities to guide near-infrared spectral tomography (NIRST) is an efficient strategy for improving the quality of the reconstructed spectral images. A new approach for incorporating image information directly into the inversion matrix regularization was examined using Direct Regularization from Images (DRI), which encodes the gray-scale data into the NIRST image reconstruction problem. This process has the benefit of eliminating user intervention such as image segmentation of distinct regions. Specifically, the Dynamic Contrast Enhanced Magnetic Resonance (DCE-MR) image intensity value differences within the anatomical image were used to implement an exponentially-weighted regularization function between the image pixels. The algorithm was validated using simulated reconstructions with noise, and the results showed that spatial resolution and robustness of the reconstructed images were significantly improved by appropriate choice of the regularization weight parameters. The proposed approach was also tested on in vivo breast data acquired in a recent clinical trial combining NIRST / MRI for cancer tumor characterization. Relative to the standard "no priors" diffuse recovery, the contrast of the tumor to the normal surrounding tissue increased from 2.4 to 3.6, and the difference between the tumor size segmented from DCE-MR images and reconstructed optical images decreased from 18% to 6%, while there was an overall decrease in surface artifacts.

Noncontact diffuse correlation tomography of human breast tumor

Our first step to adapt our recently developed noncontact diffuse correlation tomography (ncDCT) system for three-dimensional (3-D) imaging of blood flow distribution in human breast tumors is reported. A commercial 3-D camera was used to obtain breast surface geometry, which was then converted to a solid volume mesh. An ncDCT probe scanned over a region of interest on the mesh surface and the measured boundary data were combined with a finite element framework for 3-D image reconstruction of blood flow distribution. This technique was tested in computer simulations and in vivo human breasts with low-grade carcinoma. Results from computer simulations suggest that relatively high accuracy can be achieved when the entire tumor is within the sensitive region of diffuse light. Image reconstruction with a priori knowledge of the tumor volume and location can significantly improve the accuracy in recovery of tumor blood flow contrasts. In vivo imaging results from two breast carcinomas show higher average blood flow contrasts (5.9- and 10.9-fold) in the tumor regions compared to the surrounding tissues, which are comparable with previous findings using diffuse correlation spectroscopy. The ncDCT system has the potential to image blood flow distributions in soft and vulnerable tissues without distorting tissue hemodynamics

Characterization of structural-prior guided optical tomography using realistic breast models derived from dual-energy x-ray mammography

Multi-spectral near-infrared diffuse optical tomography (DOT) is capable of providing functional tissue assessment that can complement structural mammographic images for more comprehensive breast cancer diagnosis. To take full advantage of the readily available sub-millimeter resolution structural information in a multi-modal imaging setting, an efficient x-ray/optical joint image reconstruction model has been proposed previously to utilize anatomical information from a mammogram as a structural prior. In this work, we develop a complex digital breast phantom (available at http://openjd.sf.net/digibreast) based on direct measurements of fibroglandular tissue volume fractions using dual-energy mammographic imaging of a human breast. We also extend our prior-guided reconstruction algorithm to facilitate the recovery of breast tumors, and perform a series of simulation-based studies to systematically evaluate the impact of lesion sizes and contrasts, tissue background, mesh resolution, inaccurate priors, and regularization parameters, on the recovery of breast tumors using multi-modal DOT/x-ray measurements. Our studies reveal that the optical property estimation error can be reduced by half by utilizing structural priors; the minimum detectable tumor size can also be reduced by half when prior knowledge regarding the tumor location is provided. Moreover, our algorithm is shown to be robust to false priors on tumor location.

Sensitivity of MRI-guided near-infrared spectroscopy clinical breast exam data and its impact on diagnostic performance

In this study, data from breast MRI-guided near infrared spectroscopy (NIRS) exams delivered to 44 patients scheduled for surgical resection (ending in 16 benign and 28 malignant diagnoses) were analyzed using a spatial sensitivity metric to quantify the adequacy of the optical measurements for interrogating the tumor region of interest, as derived from the concurrent MRI scan. Along with positional sensitivity, the incorporation of spectral priors and the selection of an appropriate regularization parameter in the image reconstruction were considered, and found to influence the diagnostic accuracy of the recovered images. Once optimized, the MRI/NIRS data was able to differentiate the malignant from benign lesions through both total hemoglobin (p = 0.0037) and tissue optical index (p = 0.00019), but required the relative spatial sensitivity of the optical measurement data to each lesion to be above 1%. Spectral constraints implemented during the reconstruction were required to obtain statistically significant diagnostic information from images of H2O, lipids, and Tissue Optical Index (TOI). These results confirm the need for optical systems that have homogenous spatial coverage of the breast while still being able to accommodate the normal range of breast sizes.

Adaptable near-infrared spectroscopy fiber array for improved coupling to different breast sizes during clinical MRI

RATIONALE AND OBJECTIVES

Near-infrared spectroscopy (NIRS) of breast can provide functional information on the vascular and structural compartments of tissues in regions identified during simultaneous magnetic resonance imaging (MRI). NIRS can be acquired during dynamic contrast-enhanced MRI (DCE-MRI) to accomplish image-guided spectroscopy of the enhancing regions, potentially increasing the diagnostic specificity of the examination and reducing the number of biopsies performed as a result of inconclusive MRI breast imaging studies.

MATERIALS AND METHODS

We combine synergistic attributes of concurrent DCE-MRI and NIRS with a new design of the clinical NIRS breast interface that couples to a standard MR breast coil and allows imaging of variable breast sizes. Spectral information from healthy volunteers and cancer patients is recovered, providing molecular information in regions defined by the segmented MR image volume.

RESULTS

The new coupling system significantly improves examination utility by allowing improved coupling of the NIR fibers to breasts of all cup sizes and lesion locations. This improvement is demonstrated over a range of breast sizes (cup size A through D) and normal tissue heterogeneity using a group of eight healthy volunteers and two cancer patients. Lesions located in the axillary region and medial-posterior breast are now accessible to NIRS optodes. Reconstructed images were found to have biologically plausible hemoglobin content, oxygen saturation, and water and lipid fractions.

CONCLUSIONS

In summary, a new NIRS/MRI breast interface was developed to accommodate the variation in breast sizes and lesion locations that can be expected in clinical practice. DCE-MRI-guided NIRS quantifies total hemoglobin, oxygenation, and scattering in MR-enhancing regions, increasing the diagnostic information acquired from MR examinations.

Subsurface fluorescence molecular tomography with prior information

Subsurface fluorescence molecular tomography (FMT) is an emerging technique determining fluorescence distribution by tomographic means in reflectance geometry. However, due to the highly diffusive nature of the photon propagation in biological tissues and the influence of nearer source-detector separations, stand-alone subsurface FMT could not accurately reflect the fluorophore distributions. To overcome this drawback, we propose a method to improve the performance of fluorescence imaging by coupling x-ray computed tomography (XCT) and subsurface FMT modalities. A Laplacian-type regularization matrix generated with tissue prior information obtained from XCT images is used to guide the reconstruction of fluorophore distribution. Reconstruction results of both simulation and phantom studies showed that significant improvements in localization and demarcation of fluorescent targets can be obtained with the proposed method compared to the reconstruction method without structural prior information.

Hybrid photomultiplier tube and photodiode parallel detection array for wideband optical spectroscopy of the breast guided by magnetic resonance imaging

A new optical parallel detection system of hybrid frequency and continuous-wave domains was developed to improve the data quality and accuracy in recovery of all breast optical properties. This new system was deployed in a previously existing system for magnetic resonance imaging (MRI)-guided spectroscopy, and allows incorporation of additional near-infrared wavelengths beyond 850 nm, with interlaced channels of photomultiplier tubes (PMTs) and silicon photodiodes (PDs). The acquisition time for obtaining frequency-domain data at six wavelengths (660, 735, 785, 808, 826, and 849 nm) and continuous-wave data at three wavelengths (903, 912, and 948 nm) is 12 min. The dynamic ranges of the detected signal are 105 and 106 for PMT and PD detectors, respectively. Compared to the previous detection system, the SNR ratio of frequency-domain detection was improved by nearly 103 through the addition of an RF amplifier and the utilization of programmable gain. The current system is being utilized in a clinical trial imaging suspected breast cancer tumors as detected by contrast MRI scans.

Small animal fluorescence and bioluminescence tomography: a review of approaches, algorithms and technology update

Emerging fluorescence and bioluminescence tomography approaches have several common, yet several distinct features from established emission tomographies of PET and SPECT. Although both nuclear and optical imaging modalities involve counting of photons, nuclear imaging techniques collect the emitted high energy (100-511 keV) photons after radioactive decay of radionuclides while optical techniques count low-energy (1.5-4.1 eV) photons that are scattered and absorbed by tissues requiring models of light transport for quantitative image reconstruction. Fluorescence imaging has been recently translated into clinic demonstrating high sensitivity, modest tissue penetration depth, and fast, millisecond image acquisition times. As a consequence, the promise of quantitative optical tomography as a complement of small animal PET and SPECT remains high. In this review, we summarize the different instrumentation, methodological approaches and schema for inverse image reconstructions for optical tomography, including luminescence and fluorescence modalities, and comment on limitations and key technological advances needed for further discovery research and translation.

Including spatial information in nonlinear inversion MR elastography using soft prior regularization

Tissue displacements required for mechanical property reconstruction in magnetic resonance elastography (MRE) are acquired in a magnetic resonance imaging (MRI) scanner, therefore, anatomical information is available from other imaging sequences. Despite its availability, few attempts to incorporate prior spatial information in the MRE reconstruction process have been reported. This paper implements and evaluates soft prior regularization (SPR), through which homogeneity in predefined spatial regions is enforced by a penalty term in a nonlinear inversion strategy. Phantom experiments and simulations show that when predefined regions are spatially accurate, recovered property values are stable for SPR weighting factors spanning several orders of magnitude, whereas inaccurate segmentation results in bias in the reconstructed properties that can be mitigated through proper choice of regularization weighting. The method was evaluated in vivo by estimating viscoelastic mechanical properties of frontal lobe gray and white matter for five repeated scans of a healthy volunteer. Segmentations of each tissue type were generated using automated software, and statistically significant differences between frontal lobe gray and white matter were found for both the storage modulus and loss modulus . Provided homogeneous property assumptions are reasonable, SPR produces accurate quantitative property estimates for tissue structures which are finer than the resolution currently achievable with fully distributed MRE.

Regularization functional semi-automated incorporation of anatomical prior information in image-guided fluorescence tomography

The use of anatomical priors in fluorescence tomography is known to improve image quality and accuracy significantly. However, the use of prior information is often implemented by incorporating user segmented structural images into the optical reconstruction algorithm, a process requiring significant time and expertise. We propose an automated implementation which encodes the gray-scale prior image directly into the regularization term, eliminating the need for direct prior image segmentation, which is extendable to any spatially defined prior data. The proposed method is supported by in vivo studies.

A digital x-ray tomosynthesis coupled near infrared spectral tomography system for dual-modality breast imaging

A Near Infrared Spectral Tomography (NIRST) system has been developed and integrated into a commercial Digital Breast Tomosynthesis (DBT) scanner to allow structural and functional imaging of breast in vivo. The NIRST instrument uses an 8-wavelength continuous wave (CW) laser-based scanning source assembly and a 75-element silicon photodiode solid-state detector panel to produce dense spectral and spatial projection data from which spectrally constrained 3D tomographic images of tissue chromophores are produced. Integration of the optical imaging system into the DBT scanner allows direct co-registration of the optical and DBT images, while also facilitating the synergistic use of x-ray contrast as anatomical priors in optical image reconstruction. Currently, the total scan time for a combined NIRST-DBT exam is ~50s with data collection from 8 wavelengths in the optical scan requiring ~42s to complete. The system was tested in breast simulating phantoms constructed using intralipid and blood in an agarose matrix with a 3 cm x 2 cm cylindrical inclusion at 1 cm depth from the surface. Diffuse image reconstruction of total hemoglobin (HbT) concentration resulted in accurate recovery of the lateral size and position of the inclusion to within 6% and 8%, respectively. Use of DBT structural priors in the NIRST reconstruction process improved the quantitative accuracy of the HbT recovery, and led to linear changes in imaged versus actual contrast, underscoring the advantages of dual-modality optical imaging approaches. The quantitative accuracy of the system can be further improved with independent measurements of scattering properties through integration of frequency or time domain data.

Review of electromagnetic techniques for breast cancer detection

Breast cancer is anticipated to be responsible for almost 40,000 deaths in the USA in 2011. The current clinical detection techniques suffer from limitations which motivated researchers to investigate alternative modalities for the early detection of breast cancer. This paper focuses on reviewing the main electromagnetic techniques for breast cancer detection. More specifically, this work reviews the cutting edge research in microwave imaging, electrical impedance tomography, diffuse optical tomography, microwave radiometry, biomagnetic detection, biopotential detection, and magnetic resonance imaging (MRI). The goal of this paper is to provide biomedical researchers with an in-depth review that includes all main electromagnetic techniques in the literature and the latest progress in each of these techniques.

Mono- and multimodal registration of optical breast images

Optical breast imaging offers the possibility of noninvasive, low cost, and high sensitivity imaging of breast cancers. Poor spatial resolution and a lack of anatomical landmarks in optical images of the breast make interpretation difficult and motivate registration and fusion of these data with subsequent optical images and other breast imaging modalities. Methods used for registration and fusion of optical breast images are reviewed. Imaging concerns relevant to the registration problem are first highlighted, followed by a focus on both monomodal and multimodal registration of optical breast imaging. Where relevant, methods pertaining to other imaging modalities or imaged anatomies are presented. The multimodal registration discussion concerns digital x-ray mammography, ultrasound, magnetic resonance imaging, and positron emission tomography.

Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model

High-density diffuse optical tomography (HD-DOT) methods have shown significant improvement in localization accuracy and image resolution compared to traditional topographic near infrared spectroscopy of the human brain. In this work we provide a comprehensive evaluation of image quality in visual cortex mapping via a simulation study with the use of an anatomical head model derived from MRI data of a human subject. A model of individual head anatomy provides the surface shape and internal structure that allow for the construction of a more realistic physical model for the forward problem, as well as the use of a structural constraint in the inverse problem. The HD-DOT model utilized here incorporates multiple source-detector separations with continuous-wave data with added noise based on experimental results. To evaluate image quality we quantify the localization error and localized volume at half maximum (LVHM) throughout a region of interest within the visual cortex and systematically analyze the use of whole-brain tissue spatial constraint within image reconstruction. Our results demonstrate that an image quality with less than 10 mm in localization error and 1000 m³ in LVHM can be obtained up to 13 mm below the scalp surface with a typical unconstrained reconstruction and up to 18 mm deep when a whole-brain spatial constraint based on the brain tissue is utilized.

Implicit and explicit prior information in near-infrared spectral imaging: accuracy, quantification and diagnostic value

Near-infrared spectroscopy (NIRS) of tissue provides quantification of absorbers, scattering and luminescent agents in bulk tissue through the use of measurement data and assumptions. Prior knowledge can be critical about things such as (i) the tissue shape and/or structure, (ii) spectral constituents, (iii) limits on parameters, (iv) demographic or biomarker data, and (v) biophysical models of the temporal signal shapes. A general framework of NIRS imaging with prior information is presented, showing that prior information datasets could be incorporated at any step in the NIRS process, with the general workflow being: (i) data acquisition, (ii) pre-processing, (iii) forward model, (iv) inversion/reconstruction, (v) post-processing, and (vi) interpretation/diagnosis. Most of the development in NIRS has used ad hoc or empirical implementations of prior information such as pre-measured absorber or fluorophore spectra, or tissue shapes as estimated by additional imaging tools. A comprehensive analysis would examine what prior information maximizes the accuracy in recovery and value for medical diagnosis, when implemented at separate stages of the NIRS sequence. Individual applications of prior information can show increases in accuracy or improved ability to estimate biochemical features of tissue, while other approaches may not. Most beneficial inclusion of prior information has been in the inversion/reconstruction process, because it solves the mathematical intractability. However, it is not clear that this is always the most beneficial stage.

Comparison of no-prior and soft-prior regularization in biomedical microwave imaging

Microwave imaging for medical applications is attractive because the range of dielectric properties of different soft tissues can be substantial. Breast cancer detection and monitoring of treatment response are areas where this technology could be important because of the contrast between normal and malignant tissue. Unfortunately, the technique is unable to achieve the high spatial resolution at depth in tissue which is available from other conventional modalities such as x-ray computed tomography (CT) or magnetic resonance imaging (MRI). We have incorporated a soft-prior regularization strategy within our microwave reconstruction algorithm and compared it with the images obtained with traditional no-prior (Levenberg-Marquardt) regularization. Initial simulation and phantom results show a significant improvement of the recovered electrical properties. Specifically, errors in the microwave property estimates were improved by as much as 95%. The effects of a false-inclusion region were also evaluated and the results show that a small residual property bias of 6% in permittivity and 15% in conductivity can occur that does not otherwise degrade the property recovery accuracy of inclusions that actually exist. The work sets the stage for integrating microwave imaging with MR for improved resolution and functional imaging of the breast in the future.

Tomographic bioluminescence imaging reconstruction via a dynamically sparse regularized global method in mouse models

Generally, the performance of tomographic bioluminescence imaging is dependent on several factors, such as regularization parameters and initial guess of source distribution. In this paper, a global-inexact-Newton based reconstruction method, which is regularized by a dynamic sparse term, is presented for tomographic reconstruction. The proposed method can enhance higher imaging reliability and efficiency. In vivo mouse experimental reconstructions were performed to validate the proposed method. Reconstruction comparisons of the proposed method with other methods demonstrate the applicability on an entire region. Moreover, the reliable performance on a wide range of regularization parameters and initial unknown values were also investigated. Based on the in vivo experiment and a mouse atlas, the tolerance for optical property mismatch was evaluated with optical overestimation and underestimation. Additionally, the reconstruction efficiency was also investigated with different sizes of mouse grids. We showed that this method was reliable for tomographic bioluminescence imaging in practical mouse experimental applications.

MR water quantitative priors improves the accuracy of optical breast imaging

Magnetic resonance (MR) guided optical breast imaging is a promising modality to improve the specificity of breast imaging, because it provides high-resolution quantitative maps of total hemoglobin, oxygen saturation, water content, and optical scattering. These properties have been shown to distinguish malignant from benign lesions. However, the optical detection hardware required for deep tissue imaging has poor spectral sensitivity which limits accurate water quantification; this reduces the accuracy of hemoglobin quantification. We present a methodology to improve optical quantification by utilizing the ability of Dixon MR imaging to quantitatively estimate water and fat; this technique effectively reduces optical crosstalk between water and oxyhemoglobin. The techniques described in this paper reduce hemoglobin quantification error by as much as 38%, as shown in a numerical phantom, and an experimental phantom. Error is reduced by as much 20% when imperfect MR water quantification is given. These techniques may also increase contrast between diseased and normal tissue, as shown in breast tissue in vivo. It is also shown that using these techniques may permit fewer wavelengths to be used with similar quantitative accuracy, enabling higher temporal resolution. In addition, it is shown that these techniques can improve the ability of MRI to quantify water in the presence of bias in the Dixon water/fat separation.

Compositional-prior-guided image reconstruction algorithm for multi-modality imaging

The development of effective multi-modality imaging methods typically requires an efficient information fusion model, particularly when combining structural images with a complementary imaging modality that provides functional information. We propose a composition-based image segmentation method for X-ray digital breast tomosynthesis (DBT) and a structural-prior-guided image reconstruction for a combined DBT and diffuse optical tomography (DOT) breast imaging system. Using the 3D DBT images from 31 clinically measured healthy breasts, we create an empirical relationship between the X-ray intensities for adipose and fibroglandular tissue. We use this relationship to then segment another 58 healthy breast DBT images from 29 subjects into compositional maps of different tissue types. For each breast, we build a weighted-graph in the compositional space and construct a regularization matrix to incorporate the structural priors into a finite-element-based DOT image reconstruction. Use of the compositional priors enables us to fuse tissue anatomy into optical images with less restriction than when using a binary segmentation. This allows us to recover the image contrast captured by DOT but not by DBT. We show that it is possible to fine-tune the strength of the structural priors by changing a single regularization parameter. By estimating the optical properties for adipose and fibroglandular tissue using the proposed algorithm, we found the results are comparable or superior to those estimated with expert-segmentations, but does not involve the time-consuming manual selection of regions-of-interest.

Image-guided optical spectroscopy in diagnosis of osteoarthritis: a clinical study

This goal of this study was to clinically evaluate the potential of a novel hybrid imaging techniques, called x-ray guided multispectral diffuse optical tomography, for identifying physiological parameters of joint tissues that can be used to distinguish between osteoarthritic and healthy joints in the hand. Between 2006 and 2009, the distal interphalangeal (DIP) finger joints from 40 subjects including 22 osteoarthritis patients and 18 healthy controls were examined clinically and scanned by the hybrid imaging platform that integrated a C-arm based x-ray tomosynthetic system with a mutlispectral diffuse optical imaging system. Based on the reconstructed results from the 40 subjects, it was observed that oxygen saturation and water content were two statistically most significant physiological discriminators for differentiation of the healthy joints from the osteoarthritic ones.

Diffuse Optics for Tissue Monitoring and Tomography

This review describes the diffusion model for light transport in tissues and the medical applications of diffuse light. Diffuse optics is particularly useful for measurement of tissue hemodynamics, wherein quantitative assessment of oxy- and deoxy-hemoglobin concentrations and blood flow are desired. The theoretical basis for near-infrared or diffuse optical spectroscopy (NIRS or DOS, respectively) is developed, and the basic elements of diffuse optical tomography (DOT) are outlined. We also discuss diffuse correlation spectroscopy (DCS), a technique whereby temporal correlation functions of diffusing light are transported through tissue and are used to measure blood flow. Essential instrumentation is described, and representative brain and breast functional imaging and monitoring results illustrate the workings of these new tissue diagnostics.

Imaging performance of a hybrid x-ray computed tomography-fluorescence molecular tomography system using priors

PURPOSE

The performance is studied of two newly introduced and previously suggested methods that incorporate priors into inversion schemes associated with data from a recently developed hybrid x-ray computed tomography and fluorescence molecular tomography system, the latter based on CCD camera photon detection. The unique data set studied attains accurately registered data of high spatially sampled photon fields propagating through tissue along 360 degrees projections.

METHODS

Approaches that incorporate structural prior information were included in the inverse problem by adding a penalty term to the minimization function utilized for image reconstructions. Results were compared as to their performance with simulated and experimental data from a lung inflammation animal model and against the inversions achieved when not using priors.

RESULTS

The importance of using priors over stand-alone inversions is also showcased with high spatial sampling simulated and experimental data. The approach of optimal performance in resolving fluorescent biodistribution in small animals is also discussed.

CONCLUSIONS

Inclusion of prior information from x-ray CT data in the reconstruction of the fluorescence biodistribution leads to improved agreement between the reconstruction and validation images for both simulated and experimental data.

Monitoring of hemodynamic changes induced in the healthy breast through inspired gas stimuli with MR-guided diffuse optical imaging

PURPOSE

The modulation of tissue hemodynamics has important clinical value in medicine for both tumor diagnosis and therapy. As an oncological tool, increasing tissue oxygenation via modulation of inspired gas has been proposed as a method to improve cancer therapy and determine radiation sensitivity. As a radiological tool, inducing changes in tissue total hemoglobin may provide a means to detect and characterize malignant tumors by providing information about tissue vascular function. The ability to change and measure tissue hemoglobin and oxygenation concentrations in the healthy breast during administration of three different types of modulated gas stimuli (oxygen/ carbogen, air/carbogen, and air/oxygen) was investigated.

METHODS

Subjects breathed combinations of gases which were modulated in time. MR-guided diffuse optical tomography measured total hemoglobin and oxygen saturation in the breast every 30 s during the 16 min breathing stimulus. Metrics of maximum correlation and phase lag were calculated by cross correlating the measured hemodynamics with the stimulus. These results were compared to an air/air control to determine the hemodynamic changes compared to the baseline physiology.

RESULTS

This study demonstrated that a gas stimulus consisting of alternating oxygen/carbogen induced the largest and most robust hemodynamic response in healthy breast parenchyma relative to the changes that occurred during the breathing of room air. This stimulus caused increases in total hemoglobin and oxygen saturation during the carbogen phase of gas inhalation, and decreases during the oxygen phase. These findings are consistent with the theory that oxygen acts as a vasoconstrictor, while carbogen acts as a vasodilator. However, difficulties in inducing a consistent change in tissue hemoglobin and oxygenation were observed because of variability in intersubject physiology, especially during the air/oxygen or air/carbogen modulated breathing protocols.

CONCLUSIONS

MR-guided diffuse optical imaging is a unique tool that can measure tissue hemodynamics in the breast during modulated breathing. This technique may have utility in determining the therapeutic potential of pretreatment tissue oxygenation or in investigating vascular function. Future gas modulation studies in the breast should use a combination of oxygen and carbogen as the functional stimulus. Additionally, control measures of subject physiology during air breathing are critical for robust measurements.

Quantitative fluorescence tomography using a combined tri-modality FT/DOT/XCT system

In this work, a first-of-its-kind fully integrated tri-modality system that combines fluorescence, diffuse optical and x-ray tomography (FT/DOT/XCT) into the same setting is presented. The purpose of this system is to perform quantitative fluorescence tomography using multi-modality imaging approach. XCT anatomical information is used as structural priori while optical background heterogeneity information obtained by DOT measurements is used as functional priori. The performance of the hybrid system is evaluated using multi-modality phantoms. In particular, we show that a 2.4 mm diameter fluorescence inclusion located in a heterogeneous medium can be localized accurately with the functional a priori information, although the fluorophore concentration is recovered with 70% error. On the other hand, the fluorophore concentration can be accurately recovered within 8% error only when both DOT optical background functional and XCT structural a priori information are utilized to guide and constrain the FT reconstruction algorithm.

Rapid convergence to the inverse solution regularized with Lorentzian distributed function for near-infrared continuous wave diffuse optical tomography

A promising method to achieve rapid convergence for image reconstruction is introduced for the continuous-wave near-infrared (NIR) diffuse optical tomography (DOT). Tomographic techniques are usually implemented off line and are time consuming to realize image reconstruction, especially for NIR DOT. Therefore, it is essential to both speed up reconstruction and achieve stable and convergent solutions. We propose an approach using a constraint based on a Lorentzian distributed function incorporated into Tikhonov regularization, thereby rapidly converging a stable solution. It is found in the study that using the proposed method with around five or six iterations leads to a stable solution. The result is compared to the primary method usually converging in approximately 25 iterations. Our algorithm rapidly converges to stable solution in the case of noisy (>20 dB) detected intensities.

Dynamic contrast-enhanced diffuse optical tomography (DCE-DOT): experimental validation with a dynamic phantom

Dynamic contrast-enhanced diffuse optical tomography (DCE-DOT) can provide spatially resolved enhancement kinetics of an optical contrast agent. We undertook a systematic phantom study to evaluate the effects of the geometrical parameters such as the depth and size of the inclusion as well as the optical parameters of the background on the recovered enhancement kinetics of the most commonly used optical contrast agent, indocyanine green (ICG). For this purpose a computer-controlled dynamic phantom was constructed. An ICG-intralipid-water mixture was circulated through the inclusions while the DCE-DOT measurements were acquired with a temporal resolution of 16 s. The same dynamic study was repeated using inclusions of different sizes located at different depths. In addition to this, the effect of non-scattering regions was investigated by placing a second inclusion filled with water in the background. The phantom studies confirmed that although the peak enhancement varied substantially for each case, the recovered injection and dilution rates obtained from the percentage enhancement maps agreed within 15% independent of not only the depth and the size of the inclusion but also the presence of a non-scattering region in the background. Although no internal structural information was used in these phantom studies, it may be necessary to use it for small objects buried deep in tissue. However, the different contrast mechanisms of optical and other imaging modalities as well as imperfect co-registration between both modalities may lead to potential errors in the structural a priori. Therefore, the effect of erroneous selection of structural priors was investigated as the final step. Again, the injection and dilution rates obtained from the percentage enhancement maps were also immune to the systematic errors introduced by erroneous selection of the structural priors, e.g. choosing the diameter of the inclusion 20% smaller increased the peak enhancement 60% but changed the injection and dilution rates only less than 10%.

Comparison of diffusion approximation and higher order diffusion equations for optical tomography of osteoarthritis

In this study, a simplified spherical harmonics approximated higher order diffusion model is employed for 3-D diffuse optical tomography of osteoarthritis in the finger joints. We find that the use of a higher-order diffusion model in a stand-alone framework provides significant improvement in reconstruction accuracy over the diffusion approximation model. However, we also find that this is not the case in the image-guided setting when spatial prior knowledge from x-rays is incorporated. The results show that the reconstruction error between these two models is about 15 and 4%, respectively, for stand-alone and image-guided frameworks.

Numerical modelling and image reconstruction in diffuse optical tomography

The development of diffuse optical tomography as a functional imaging modality has relied largely on the use of model-based image reconstruction. The recovery of optical parameters from boundary measurements of light propagation within tissue is inherently a difficult one, because the problem is nonlinear, ill-posed and ill-conditioned. Additionally, although the measured near-infrared signals of light transmission through tissue provide high imaging contrast, the reconstructed images suffer from poor spatial resolution due to the diffuse propagation of light in biological tissue. The application of model-based image reconstruction is reviewed in this paper, together with a numerical modelling approach to light propagation in tissue as well as generalized image reconstruction using boundary data. A comprehensive review and details of the basis for using spatial and structural prior information are also discussed, whereby the use of spectral and dual-modality systems can improve contrast and spatial resolution.

Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction

Diffuse optical tomography, also known as near infrared tomography, has been under investigation, for non-invasive functional imaging of tissue, specifically for the detection and characterization of breast cancer or other soft tissue lesions. Much work has been carried out for accurate modeling and image reconstruction from clinical data. NIRFAST, a modeling and image reconstruction package has been developed, which is capable of single wavelength and multi-wavelength optical or functional imaging from measured data. The theory behind the modeling techniques as well as the image reconstruction algorithms is presented here, and 2D and 3D examples are presented to demonstrate its capabilities. The results show that 3D modeling can be combined with measured data from multiple wavelengths to reconstruct chromophore concentrations within the tissue. Additionally it is possible to recover scattering spectra, resulting from the dominant Mie-type scatter present in tissue. Overall, this paper gives a comprehensive over view of the modeling techniques used in diffuse optical tomographic imaging, in the context of NIRFAST software package.