Dual-mesh optical tomography reconstruction method with a depth correction that uses a priori ultrasound information

Fusion deep learning approach combining diffuse optical tomography and ultrasound for improving breast cancer classification

Diffuse optical tomography (DOT) is a promising technique that provides functional information related to tumor angiogenesis. However, reconstructing the DOT function map of a breast lesion is an ill-posed and underdetermined inverse process. A co-registered ultrasound (US) system that provides structural information about the breast lesion can improve the localization and accuracy of DOT reconstruction. Additionally, the well-known US characteristics of benign and malignant breast lesions can further improve cancer diagnosis based on DOT alone. Inspired by a fusion model deep learning approach, we combined US features extracted by a modified VGG-11 network with images reconstructed from a DOT deep learning auto-encoder-based model to form a new neural network for breast cancer diagnosis. The combined neural network model was trained with simulation data and fine-tuned with clinical data: it achieved an AUC of 0.931 (95% CI: 0.919-0.943), superior to those achieved using US images alone (0.860) or DOT images alone (0.842).

Difference imaging from single measurements in diffuse optical tomography: a deep learning approach

SIGNIFICANCE

"Difference imaging," which reconstructs target optical properties using measurements with and without target information, is often used in diffuse optical tomography (DOT) in vivo imaging. However, taking additional reference measurements is time consuming, and mismatches between the target medium and the reference medium can cause inaccurate reconstruction.

AIM

We aim to streamline the data acquisition and mitigate the mismatch problems in DOT difference imaging using a deep learning-based approach to generate data from target measurements only.

APPROACH

We train an artificial neural network to output data for difference imaging from target measurements only. The model is trained and validated on simulation data and tested with simulations, phantom experiments, and clinical data from 56 patients with breast lesions.

RESULTS

The proposed method has comparable performance to the traditional approach using measurements without mismatch between the target side and the reference side, and it outperforms the traditional approach using measurements when there is a mismatch. It also improves the target-to-artifact ratio and lesion localization in patient data.

CONCLUSIONS

The proposed method can simplify the data acquisition procedure, mitigate mismatch problems, and improve reconstructed image quality in DOT difference imaging.

Evaluation of a pipeline for simulation, reconstruction, and classification in ultrasound-aided diffuse optical tomography of breast tumors

SIGNIFICANCE

Diffuse optical tomography is an ill-posed problem. Combination with ultrasound can improve the results of diffuse optical tomography applied to the diagnosis of breast cancer and allow for classification of lesions.

AIM

To provide a simulation pipeline for the assessment of reconstruction and classification methods for diffuse optical tomography with concurrent ultrasound information.

APPROACH

A set of breast digital phantoms with benign and malignant lesions was simulated building on the software VICTRE. Acoustic and optical properties were assigned to the phantoms for the generation of B-mode images and optical data. A reconstruction algorithm based on a two-region nonlinear fitting and incorporating the ultrasound information was tested. Machine learning classification methods were applied to the reconstructed values to discriminate lesions into benign and malignant after reconstruction.

RESULTS

The approach allowed us to generate realistic US and optical data and to test a two-region reconstruction method for a large number of realistic simulations. When information is extracted from ultrasound images, at least 75% of lesions are correctly classified. With ideal two-region separation, the accuracy is higher than 80%.

CONCLUSIONS

A pipeline for the generation of realistic ultrasound and diffuse optics data was implemented. Machine learning methods applied to a optical reconstruction with a nonlinear optical model and morphological information permit to discriminate malignant lesions from benign ones.

Machine learning model with physical constraints for diffuse optical tomography

A machine learning model with physical constraints (ML-PC) is introduced to perform diffuse optical tomography (DOT) reconstruction. DOT reconstruction is an ill-posed and under-determined problem, and its quality suffers by model mismatches, complex boundary conditions, tissue-probe contact, noise etc. Here, for the first time, we combine ultrasound-guided DOT with ML to facilitate DOT reconstruction. Our method has two key components: (i) a neural network based on auto-encoder is adopted for DOT reconstruction, and (ii) physical constraints are implemented to achieve accurate reconstruction. Both qualitative and quantitative results demonstrate that the accuracy of the proposed method surpasses that of existing models. In a phantom study, compared with the Born conjugate gradient descent (Born-CGD) reconstruction method, the ML-PC method decreases the mean percentage error of the reconstructed maximum absorption coefficient from 16.41% to 13.4% for high contrast phantoms and from 23.42% to 9.06% for low contrast phantoms, with improved depth distribution of the target absorption maps. In a clinical study, better contrast was obtained between malignant and benign breast lesions, with the ratio of the medians of the maximum absorption coefficient improved from 1.63 to 2.22.

Enhanced diffuse optical tomographic reconstruction using concurrent ultrasound information

Multimodal imaging is an active branch of research as it has the potential to improve common medical imaging techniques. Diffuse optical tomography (DOT) is an example of a low resolution, functional imaging modality that typically has very low resolution due to the ill-posedness of its underlying inverse problem. Combining the functional information of DOT with a high resolution structural imaging modality has been studied widely. In particular, the combination of DOT with ultrasound (US) could serve as a useful tool for clinicians for the formulation of accurate diagnosis of breast lesions. In this paper, we propose a novel method for US-guided DOT reconstruction using a portable time-domain measurement system. B-mode US imaging is used to retrieve morphological information on the probed tissues by means of a semi-automatical segmentation procedure based on active contour fitting. A two-dimensional to three-dimensional extrapolation procedure, based on the concept of distance transform, is then applied to generate a three-dimensional edge-weighting prior for the regularization of DOT. The reconstruction procedure has been tested on experimental data obtained on specifically designed dual-modality silicon phantoms. Results show a substantial quantification improvement upon the application of the implemented technique. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging

Near-infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response in patients with locally advanced breast cancers. The path toward commercialization of DOT techniques depends upon the improvement of robustness and user-friendliness of this technique in hardware and software. In this study, we introduce our recently developed ultrasound-guided DOT system, which has been improved in system compactness, robustness, and user-friendliness by custom-designed electronics, automated data preprocessing, and implementation of a new two-step reconstruction algorithm. The system performance has been tested with several sets of solid and blood phantoms and the results show accuracy in reconstructed absorption coefficients as well as blood oxygen saturation. A clinical example of a breast cancer patient, who was undergoing neoadjuvant chemotherapy, is given to demonstrate the system performance.

Diffuse optical tomography using semiautomated coregistered ultrasound measurements

Diffuse optical tomography (DOT) has demonstrated huge potential in breast cancer diagnosis and treatment monitoring. DOT image reconstruction guided by ultrasound (US) improves the diffused light localization and lesion reconstruction accuracy. However, DOT reconstruction depends on tumor geometry provided by coregistered US. Experienced operators can manually measure these lesion parameters; however, training and measurement time are needed. The wide clinical use of this technique depends on its robustness and faster imaging reconstruction capability. This article introduces a semiautomated procedure that automatically extracts lesion information from US images and incorporates it into the optical reconstruction. An adaptive threshold-based image segmentation is used to obtain tumor boundaries. For some US images, posterior shadow can extend to the chest wall and make the detection of deeper lesion boundary difficult. This problem can be solved using a Hough transform. The proposed procedure was validated from data of 20 patients. Optical reconstruction results using the proposed procedure were compared with those reconstructed using extracted tumor information from an experienced user. Mean optical absorption obtained from manual measurement was 0.21±0.06  cm-1 for malignant and 0.12±0.06  cm-1 for benign cases, whereas for the proposed method it was 0.24±0.08  cm-1 and 0.12±0.05  cm-1, respectively.

Ultrasound-Guided Diffuse Optical Tomography for Differentiation of Benign and Malignant Breast Lesions: A Meta-analysis

OBJECTIVES

The purpose of this study was to assess the diagnostic performance of ultrasound-guided diffuse optical tomography for differentiation of benign and malignant breast lesions.

METHODS

The Cochrane Library, PubMed, and Embase databases were searched from inception to February 14, 2016. Sensitivity, specificity, and other information were extracted from the included studies. Sensitivity and specificity were pooled by a bivariate mixed-effects binary regression model. A summary receiver operating characteristic curve was constructed. Heterogeneity and publication bias were explored by Higgins and Deeks tests, respectively.

RESULTS

Seven studies including 768 women with 886 lesions were analyzed. The summary sensitivity, specificity, and diagnostic odds ratio were 95% (95% confidence interval [CI], 85%-98%), 77% (95% CI, 66%-85%), and 57 (95% CI, 12-267), respectively. The area under the summary receiver operating characteristic curve was 91% (95% CI, 89%-94%). No significant heterogeneity or publication bias existed.

CONCLUSIONS

Ultrasound-guided diffuse optical tomography is useful for differentiating breast lesions. Especially, its sensitivity is excellent.

Automated data selection method to improve robustness of diffuse optical tomography for breast cancer imaging

Imaging-guided near infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response of breast cancers. However, diffused light measurements are sensitive to artifacts caused by outliers and errors in measurements due to probe-tissue coupling, patient and probe motions, and tissue heterogeneity. In general, pre-processing of the measurements is needed by experienced users to manually remove these outliers and therefore reduce imaging artifacts. An automated method of outlier removal, data selection, and filtering for diffuse optical tomography is introduced in this manuscript. This method consists of multiple steps to first combine several data sets collected from the same patient at contralateral normal breast and form a single robust reference data set using statistical tests and linear fitting of the measurements. The second step improves the perturbation measurements by filtering out outliers from the lesion site measurements using model based analysis. The results of 20 malignant and benign cases show similar performance between manual data processing and automated processing and improvement in tissue characterization of malignant to benign ratio by about 27%.

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

Light-emitting diode-based multiwavelength diffuse optical tomography system guided by ultrasound

Laser diodes are widely used in diffuse optical tomography (DOT) systems but are typically expensive and fragile, while light-emitting diodes (LEDs) are cheaper and are also available in the near-infrared (NIR) range with adequate output power for imaging deeply seated targets. In this study, we introduce a new low-cost DOT system using LEDs of four wavelengths in the NIR spectrum as light sources. The LEDs were modulated at 20 kHz to avoid ambient light. The LEDs were distributed on a hand-held probe and a printed circuit board was mounted at the back of the probe to separately provide switching and driving current to each LED. Ten optical fibers were used to couple the reflected light to 10 parallel photomultiplier tube detectors. A commercial ultrasound system provided simultaneous images of target location and size to guide the image reconstruction. A frequency-domain (FD) laser-diode-based system with ultrasound guidance was also used to compare the results obtained from those of the LED-based system. Results of absorbers embedded in intralipid and inhomogeneous tissue phantoms have demonstrated that the LED-based system provides a comparable quantification accuracy of targets to the FD system and has the potential to image deep targets such as breast lesions.

Effect of vascular haemoglobin concentrations on ultrasound-guided diffuse optical tomography in differentiating benign from malignant breast lesions

OBJECTIVES

Ultrasound-guided diffuse optical tomography (US-DOT) can potentially detect breast carcinomas by measuring total tumour haemoglobin concentrations (TTHC). The purpose of this study was to evaluate whether vascular haemoglobin concentrations (VHC) affect the ability of US-DOT to distinguish breast carcinomas from benign.

MATERIALS AND METHODS

In 85 women (97 palpable lesions) referred for core breast biopsy, we measured VHC with a complete blood count and calculated TTHCs for each lesion with US-DOT. Anaemia was defined as a VHC less than 120.0 g/L.

RESULTS

Mean TTHCs were significantly higher in malignant lesions (n = 53) than in benign lesions (n = 44), regardless of whether the lesions were from women with anaemia (TTHC, 248.5 vs. 123.3 μmol/L; P = 0.001) or from those without (TTHC, 229.7 vs. 173.9 μmol/L; P = 0.016). A cut-off TTHC of 155.1 μmol/L provided 81.3 % sensitivity, 81.8 % specificity and 81.5 % accuracy for detecting malignant tumours in women with anaemia and 78.4 % sensitivity, 54.5 % specificity and 67.1 % accuracy for women without. There was no significant difference in sensitivity (P = 0.813), specificity (P = 0.108) and accuracy (P = 0.162) between the anaemic group and the non-anaemic group.

CONCLUSIONS

Vascular haemoglobin concentrations did not affect the ability of US-DOT to differentiate breast carcinomas from benign lesions.

KEY POINTS

• US-DOT can differentiate benign from malignant breast lesions by measuring TTHC. • No difference in TTHC between the anaemia and non-anaemia group. • Vascular haemoglobin concentrations do not affect the diagnostic ability of US-DOT.

Reconstruction of localized fluorescent target from multi-view continuous-wave surface images of small animal with lp sparsity regularization

Fluorescence diffuse optical tomography using a multi-view continuous-wave and non-contact measurement system and an algorithm incorporating the lp (0 < p ≤ 1) sparsity regularization reconstructs a localized fluorescent target in a small animal. The measurement system provides a total of 25 fluorescence surface 2D-images of an object, which are acquired by a CCD camera from five different angles of view with excitation from five different angles. Fluorescence surface emissions from five different angles of view are simultaneously imaged on the CCD sensor, thus leading to fast acquisition of the 25 images within three minutes. The distributions of the fluorophore are reconstructed by solving the inverse problem based on the photon diffusion equations. In the reconstruction process incorporating the lp sparsity regularization, the regularization term is reformulated as a differentiable function for gradient-based non-linear optimization. Numerical simulations and phantom experiments show that the use of the lp sparsity regularization improves the localization of the target and quantitativeness of the fluorophore concentration. A mouse experiment demonstrates that a localized fluorescent target in a mouse is successfully reconstructed.

Localization of an absorber in a turbid semi-infinite medium by spatially resolved continuous-wave diffuse reflectance measurements

A method to locate an absorber embedded in a semi-infinite turbid medium by spatially-resolved continuous-wave (SRCW) diffuse reflectance measurements is introduced. The depth of the absorber is assessed by single wavelength SRCW diffuse reflectance measurements by two detectors in a radial row. The ratio of perturbations introduced by the defect at two detectors is used to be matched with the ratio-versus-depth curve, which are generated by approximate formulas of continuous wave diffuse reflectance. The error due to approximation and the error in depth assessment are studied for different cases revealing favorable source-detector placements with respect to planar position of the defect. The effect of lateral displacement of the source with respect to defect is studied. A strategy to overcome errors introduced by erroneous prediction of background medium optical properties is suggested. Theoretical results indicate that the depth of the absorber can be obtained with 0.1 mm precision independent of its absorption coefficient and its size for the values chosen in the study. The approach is tested experimentally and it is observed that theoretical results fit with experimental data.

Depth-correction algorithm that improves optical quantification of large breast lesions imaged by diffuse optical tomography

Optical quantification of large lesions imaged with diffuse optical tomography in reflection geometry is depth dependence due to the exponential decay of photon density waves. We introduce a depth-correction method that incorporates the target depth information provided by coregistered ultrasound. It is based on balancing the weight matrix, using the maximum singular values of the target layers in depth without changing the forward model. The performance of the method is evaluated using phantom targets and 10 clinical cases of larger malignant and benign lesions. The results for the homogenous targets demonstrate that the location error of the reconstructed maximum absorption coefficient is reduced to the range of the reconstruction mesh size for phantom targets. Furthermore, the uniformity of absorption distribution inside the lesions improve about two times and the median of the absorption increases from 60 to 85% of its maximum compared to no depth correction. In addition, nonhomogenous phantoms are characterized more accurately. Clinical examples show a similar trend as the phantom results and demonstrate the utility of the correction method for improving lesion quantification.

Direct-current-based image reconstruction versus direct-current included or excluded frequency-domain reconstruction in diffuse optical tomography

We study the level of image artifacts in optical tomography associated with measurement uncertainty under three reconstruction configurations, namely, by using only direct-current (DC), DC-excluded frequency-domain, and DC-included frequency-domain data. Analytic and synthetic studies demonstrate that, at the same level of measurement uncertainty typical to optical tomography, the ratio of the standard deviation of mu(a) over mu(a) reconstructed by DC only is at least 1.4 times lower than that by frequency-domain methods. The ratio of standard deviations of D (or mu(s)') over D (or mu(s)') reconstructed by DC only are slightly lower than those by frequency-domain methods. Frequency-domain reconstruction including DC generally outperforms that excluding DC, but as the amount of measurements increases, the difference between the two diminishes. Under the condition of a priori structural information, the performances of three reconstruction configurations are seemingly equivalent.

Light shadowing effect of large breast lesions imaged by optical tomography in reflection geometry

When a large, highly absorbing breast lesion is imaged by optical tomography in reflection geometry, most of the photons are absorbed by the top portion of the lesion. As a result, the lower portion of the lesion is not quantified correctly. This posterior light shadowing effect is similar to the sound shadowing effect frequently seen in pulse-echo ultrasound images. The presence of significant posterior shadowing of a lesion in ultrasound images suggests malignance. The light shadowing effect due to optical contrast is characterized using a simple measure and validated by the Monte Carlo photon-tracking method and phantom experiments. Clinical examples of large malignant and benign lesions are presented to demonstrate the shadowing effect and the utility of the measure. Understanding and quantifying the shadowing effect due to optical contrast is important for characterizing larger malignant cancers from benign lesions.

Discretization error analysis and adaptive meshing algorithms for fluorescence diffuse optical tomography: part II

In the first part of this work, we analyze the effect of discretization on the accuracy of fluorescence diffuse optical tomography (FDOT). Our error analysis provides two new error estimates which present a direct relationship between the error in the reconstructed fluorophore concentration and the discretization of the forward and inverse problems. In this paper, based on these error estimates, we develop two new adaptive mesh generation algorithms for the numerical solutions of the forward and inverse problems in FDOT, with the objective of error reduction in the reconstructed optical images due to discretization while keeping the size of the discretized forward and inverse problems within the allowable limits. We present three-dimensional numerical simulations to demonstrate the improvements in accuracy, resolution and detectability of small heterogeneities in reconstructed images provided by the use of the new adaptive mesh generation algorithms. Finally, we compare our algorithms both analytically and numerically with the existing conventional adaptive mesh generation algorithms.

Multistage inversion algorithm for biological tissue imaging

A new inversion method for diffuse optical tomography is proposed. This is a multistage algorithm, that uses a signal subspace-based method to simplify the inverse problem and proposes a guided iterative inversion process to improve the imaging. First, subspace-based analysis is used to determine the voxels that definitely belong to the background and exclude them from further consideration. Then, the pseudo-inverse technique is applied for reconstruction. In the final stage, the reconstruction is improved iteratively by finding and excluding more voxels belonging to background. The method reduces the ill-posedness of the image reconstruction problem iteratively such that good imaging results are obtained for multiple heterogeneities having complicated geometries even in the presence of 3% additive white noise.

Parametric image reconstruction using the discrete cosine transform for optical tomography

It is well known that the inverse problem in optical tomography is highly ill-posed. The image reconstruction process is often unstable and nonunique, because the number of the boundary measurements data is far fewer than the number of the unknown parameters to be reconstructed. To overcome this problem, one can either increase the number of measurement data (e.g., multispectral or multifrequency methods), or reduce the number of unknowns (e.g., using prior structural information from other imaging modalities). We introduce a novel approach for reducing the unknown parameters in the reconstruction process. The discrete cosine transform (DCT), which has long been used in image compression, is here employed to parameterize the reconstructed image. In general, only a few DCT coefficients are needed to describe the main features in an optical tomographic image. Thus, the number of unknowns in the image reconstruction process can be drastically reduced. We show numerical and experimental examples that illustrate the performance of the new algorithm as compared to a standard model-based iterative image reconstructions scheme. We especially focus on the influence of initial guesses and noise levels on the reconstruction results.

US-guided diffused optical tomography: a promising functional imaging technique in breast lesions

OBJECTIVE

To investigate the feasibility and clinical efficacy of ultrasound (US)-guided diffused optical tomography (DOT) in differentiating malignant breast lesions from benign lesions.

MATERIAL AND METHODS

From October 2007 to August 2008, 198 women with 214 lesions scheduled for open biopsy were included in this study. Conventional US was used to locate the breast lesion, and DOT to measure the total haemoglobin concentration (THC) for each breast lesion. Sensitivity, specificity and overall accuracy were determined with surgical pathology as the gold standard.

RESULTS

There were 96 benign lesions and 118 malignant lesions. The total haemoglobin concentration (THC) of all 214 lesions was calculated from DOT. The average THC in benign lesions was 125.5 +/- 83.4 micromol/L, and in malignant lesions 222.2 +/- 87.2 micromol/L. The THC of malignant lesions was significantly higher than that of benign lesions (p < 0.05). When a THC of 140 micromol/L was used as the cutoff value for differentiating breast cancer from benign lesions, the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of DOT were 83.9%, 66.7%, 76.2%, 75.6% and 77.1%, respectively. Our study demonstrated that THC was significantly higher in malignant lesions than in benign lesions.

CONCLUSION

US-guided diffused optical tomography, a noninvasive functional imaging technique, has potential utility in differentiating breast cancer from benign lesions.

Fast intersections on nested tetrahedrons (FINT): An algorithm for adaptive finite element based distributed parameter estimation

A variety of biomedical imaging techniques such as optical and fluorescence tomography, electrical impedance tomography, and ultrasound imaging can be cast as inverse problems, wherein image reconstruction involves the estimation of spatially distributed parameter(s) of the PDE system describing the physics of the imaging process. Finite element discretization of imaged domain with tetrahedral elements is a popular way of solving the forward and inverse imaging problems on complicated geometries. A dual-adaptive mesh-based approach wherein, one mesh is used for solving the forward imaging problem and the other mesh used for iteratively estimating the unknown distributed parameter, can result in high resolution image reconstruction at minimum computation effort, if both the meshes are allowed to adapt independently. Till date, no efficient method has been reported to identify and resolve intersection between tetrahedrons in independently refined or coarsened dual meshes. Herein, we report a fast and robust algorithm to identify and resolve intersection of tetrahedrons within nested dual meshes generated by 8-similar subtetrahedron subdivision scheme. The algorithm exploits finite element weight functions and gives rise to a set of weight functions on each vertex of disjoint tetrahedron pieces that completely cover up the intersection region of two tetrahedrons. The procedure enables fully adaptive tetrahedral finite elements by supporting independent refinement and coarsening of each individual mesh while preserving fast identification and resolution of intersection. The computational efficiency of the algorithm is demonstrated by diffuse photon density wave solutions obtained from a single- and a dual-mesh, and by reconstructing a fluorescent inclusion in simulated phantom from boundary frequency domain fluorescence measurements.

Optical tomography with ultrasound localization for breast cancer diagnosis and treatment monitoring

Optical tomography with ultrasound (US) localization uses coregistered ultrasound images to guide optical imaging reconstruction. To simultaneously acquire US images and optical measurements, the authors used a hand-held probe consisting of a commercial US transducer and near-infrared optical imaging sensors of multiple wavelengths. A novel image scheme was used to map the ultrasound-visible lesions for optical imaging reconstruction. As a result, the problem of intense light scattering caused by breast tissue was overcome and reliable tumor hemoglobin concentration and blood oxygen saturation distributions from a group of patients were obtained. These functional parameters are valuable for aiding US diagnosis and for assessing chemotherapy response.

Fully adaptive finite element based tomography using tetrahedral dual-meshing for fluorescence enhanced optical imaging in tissue

We have developed fluorescence enhanced optical tomography based upon fully adaptive finite element method (FEM) using tetrahedral dual-meshing wherein one of the two meshes discretizes the forward variables and the other discretizes the unknown parameters to be estimated. We used the 8-subtetrahedron subdivision scheme to create the nested dualmesh in which each are independently refined. However, two tetrahedrons from the two different meshes pose an intersection problem that needs to be resolved in order to find the common regions that the forward variables (the fluorescent diffuse photon fluence fields) and the parameter estimates (the fluorescent absorption coefficients) can be mutually assigned. Using an efficient intersection algorithm in the nested tetrahedral environments previously developed by the authors, we demonstrate fully adaptive tomography using a posteriori error estimates. Performing the iterative reconstructions using the simulated boundary measurement data, we demonstrate that small fluorescent targets embedded in the breast simulating phantom in point illumination/detection geometry can be resolved at reasonable computational cost.

Image correction scheme applied to functional diffuse optical tomography scattering images

We have extended our investigation on the use of a linear algorithm for enhancing the accuracy of diffuse optical tomography (DOT) images, to include spatial maps of the diffusion coefficient. The results show that the corrected images are markedly improved in terms of estimated size, spatial resolution, two-object resolving power, and quantitative accuracy. These image-enhancing effects are significant at expected levels of diffusion-coefficient contrast in tissue and noise levels typical of experimental DOT data. Overall, the types and magnitudes of image-enhancing effects obtained here are qualitatively similar to those seen in previous studies on mu(a) perturbations. The implications for practical implementations of DOT time-series imaging are discussed.

Fully adaptive FEM based fluorescence optical tomography from time-dependent measurements with area illumination and detection

Using an area-illumination and area-detection scheme, we acquire fluorescence frequency domain measurements from a tissue phantom with an embedded fluorescent target and obtain tomographic reconstructions of the interior fluorescence absorption map with an adaptive finite element based scheme. The tissue phantom consisted of a clear acrylic cubic box (512 ml) filled with 1% Liposyn solution, while the fluorescent targets were 5 mm diameter glass bulbs filled with 1 microM Indocyanine Green dye solution in 1% Liposyn. Frequency domain area illumination and detection employed a planar excitation source using an expanded intensity modulated (100 MHz) 785 nm diode laser light and a gain modulated image intensified charge coupled device camera, respectively. The excitation pattern was characterized by isolating the singly scattered component with cross polarizers and was input into a dual adaptive finite element-based scheme for three dimensional reconstructions of fluorescent targets embedded beneath the phantom surface. Adaptive mesh refinement techniques allowed efficient simulation of the incident excitation light and the reconstruction of fluorescent targets buried at the depths of 1 and 2 cm. The results demonstrate the first clinically relevant noncontact fluorescence tomography with adaptive finite element methods.

Parametric reconstruction method in optical tomography

Optical tomography consists of reconstructing the spatial of a medium's optical properties from measurements of transmitted light on the boundary of the medium. Mathematically this problem amounts to parameter identification for the radiative transport equation (ERT) or diffusion approximation (DA). However, this type of boundary-value problem is highly ill-posed and the image reconstruction process is often unstable and non-unique. To overcome this problem, we present a parametric inverse method that considerably reduces the number of variables being reconstructed. In this way the amount of measured data is equal or larger than the number of unknowns. Using synthetic data, we show examples that demonstrate how this approach leads to improvements in imaging quality.

Benign versus malignant breast masses: optical differentiation with US-guided optical imaging reconstruction

PURPOSE

To investigate prospectively the feasibility of using optical tomography with ultrasonographic (US) localization to differentiate malignant from benign breast masses and to compare optical tomography with color Doppler US.

MATERIALS AND METHODS

The study was approved by the local internal review board committee and by the Human Subjects Research Review Board of Army Medical Research and Materiel Command. Signed informed consent was obtained, and the study was HIPAA compliant. Between May 2003 and March 2004, 65 consecutive women (mean age, 51 years; age range, 24-80 years) with 81 breast lesions underwent US-guided biopsy and were scanned with a combined imager. The hand-held probe, which consisted of a centrally located US transducer surrounded by near-infrared sensors, was used to simultaneously acquire coregistered US images and optical data. The lesion location obtained at US was used to guide optical imaging reconstruction. Light absorption was measured at two wavelengths. From these measurements, tumor angiogenesis was assessed on the basis of calculated total hemoglobin concentration. A Student t distribution was used to calculate the statistical significance of mean maximum and mean average hemoglobin concentrations obtained in malignant and benign lesion groups, and P < .001 was considered to indicate a statistically significant difference.

RESULTS

Biopsy results revealed eight early stage invasive carcinomas (malignant group) and 73 benign lesions (benign group). The mean maximum and mean average hemoglobin concentrations in the malignant group were 122 micromol/L +/- 26.8 (+/- standard deviation) and 88 micromol/L +/- 24.5, respectively. The mean maximum and mean average hemoglobin concentrations in the benign group were 55 micromol/L +/- 24.8 and 38 micromol/L +/- 17.4, respectively. Both the maximum and average total hemoglobin concentrations were significantly higher in the malignant group compared with the benign group (P < .001). When a maximum hemoglobin concentration of 95 micromol/L was used as the threshold value, the sensitivity, specificity, positive predictive value, and negative predictive value of optical tomography were 100%, 96%, 73%, and 100%, respectively, and the sensitivity, specificity, positive predictive value, and negative predictive value of color Doppler US were 63%, 69%, 19%, and 94%, respectively.

CONCLUSION

Findings indicate that optical tomography with US localization is feasible for differentiating benign and early stage malignant breast lesions.

Optical tomography with ultrasound localization: initial clinical results and technical challenges

Optical tomography with ultrasound localization utilizes co-registered ultrasound lesion structure information to guide optical imaging reconstruction. A hand-held probe consisting of a commercial ultrasound transducer and near infrared optical imaging sensors was used to simultaneously acquire ultrasound images and optical measurements. A dual-mesh scheme was used to map the ultrasound-visible lesions to finer-grid lesion regions and coarser-grid background regions for optical imaging reconstruction. As a result, optical imaging reconstruction was well-conditioned for inverse mapping of lesion hemoglobin concentration and blood oxygen saturation. Initial clinical results have shown that early stage invasive cancers may be distinguished by a two-fold greater total hemoglobin concentration compared with fibroadenomas and other benign lesions. Initial results of advanced cancers have shown that the hemoglobin distribution is highly distorted and heterogeneous and the distorted distributions correlate with histological microvessel density counts and could be used to assess chemotherapy response.