Diffuse Optical Tomography Imaging Research Articles

Fast multispectral diffuse optical tomography system for in vivo three-dimensional imaging of seizure dynamics

We describe a multispectral continuous-wave diffuse optical tomography (DOT) system that can be used for in vivo three-dimensional (3-D) imaging of seizure dynamics. Fast 3-D data acquisition is realized through a time multiplexing approach based on a parallel lighting configuration--our system can achieve 0.12 ms per source per wavelength and up to a 14 Hz sampling rate for a full set of data for 3-D DOT image reconstruction. The system is validated using both static and dynamic tissue-like phantoms. An initial in vivo experiment using a rat model of seizure is also demonstrated.

Design and evaluation of a hybrid photoacoustic tomography and diffuse optical tomography system for breast cancer detection

The authors present the design and evaluation of a second-generation (G2) hybrid photoacoustic tomography (PAT) and diffuse optical tomography (DOT) imaging system for detection of breast cancer. The combined PAT/DOT system takes full advantages of two different modalities to provide complementary information and most accurate recovery of tissue optical properties. This system was designed to produce cross-section images of breast tissue with a ring-shaped home-made array of 64 wideband acoustic transducers combined with an array of 16/16 source/detector optical fiber bundles. A scanning light delivery system was built to illuminate a large area of breast tissue for optimized tissue penetration. Finite element reconstruction methods for quantitative PAT and DOT were utilized to recover absorption and scattering coefficients of tissue-mimicking phantoms and ex vivo tumor tissue. Performances of the acoustic transducer array and the PAT/DOT system were investigated in detail. Frequency response of the transducers is from 380 kHz to 1.48 MHz and maximum frequency response is up to 2 MHz. Directivity of a single element in the array is ±30°. The PAT/DOT system offers a spatial resolution of ∼0.5 mm for PAT and of ∼4.0 mm for DOT. Quantitatively accurate absorption and scattering coefficients were obtained from both the phantom and ex vivo experiments. The PAT/DOT system provided better PAT images when the targets were smaller in size and located near the center of the background phantom, while better DOT images were obtained when the targets were larger in size and located away from the center of the background. The successful results obtained from both the phantom and ex vivo experiments will allow us to test the hybrid system in humans with breast cancer in the near future.

Sparsity enhanced spatial resolution and depth localization in diffuse optical tomography

: In diffuse optical tomography (DOT), researchers often face challenges to accurately recover the depth and size of the reconstructed objects. Recent development of the Depth Compensation Algorithm (DCA) solves the depth localization problem, but the reconstructed images commonly exhibit over-smoothed boundaries, leading to fuzzy images with low spatial resolution. While conventional DOT solves a linear inverse model by minimizing least squares errors using L2 norm regularization, L1 regularization promotes sparse solutions. The latter may be used to reduce the over-smoothing effect on reconstructed images. In this study, we combined DCA with L1 regularization, and also with L2 regularization, to examine which combined approach provided us with an improved spatial resolution and depth localization for DOT. Laboratory tissue phantoms were utilized for the measurement with a fiber-based and a camera-based DOT imaging system. The results from both systems showed that L1 regularization clearly outperformed L2 regularization in both spatial resolution and depth localization of DOT. An example of functional brain imaging taken from human in vivo measurements was further obtained to support the conclusion of the study.

Model-resolution based regularization improves near infrared diffuse optical tomography

Diffuse optical tomographic imaging is known to be an ill-posed problem, and a penalty/regularization term is used in image reconstruction (inverse problem) to overcome this limitation. Two schemes that are prevalent are spatially varying (exponential) and constant (standard) regularizations/penalties. A scheme that is also spatially varying but uses the model information is introduced based on the model-resolution matrix. This scheme, along with exponential and standard regularization schemes, is evaluated objectively based on model-resolution and data-resolution matrices. This objective analysis showed that resolution characteristics are better for spatially varying penalties compared to standard regularization; and among spatially varying regularization schemes, the model-resolution based regularization fares well in providing improved data-resolution and model-resolution characteristics. The verification of the same is achieved by performing numerical experiments in reconstructing 1% noisy data involving simple two- and three-dimensional imaging domains.

Multifunctionality of indocyanine green-loaded biodegradable nanoparticles for enhanced optical imaging and hyperthermia intervention of cancer

The aim of this study was to develop and characterize multifunctional biodegradable and biocompatible poly lactic-co-glycolic acid (PLGA) nanoparticles loaded with indocyanine green (ICG) as an optical-imaging contrast agent for cancer imaging and as a photothermal therapy agent for cancer treatment. PLGA-ICG nanoparticles (PIN) were synthesized with a particle diameter of 246±11 nm, a polydispersity index of 0.10±0.03, and ICG loading efficiency of 48.75±5.48%. PIN were optically characterized with peak excitation and emission at 765 and 810±5 nm, a fluorescence lifetime of 0.30±0.01 ns, and peak absorbance at 780 nm. The cytocompatibility study of PIN showed 85% cell viability till 1-mg/ml concentration of PIN. Successful cellular uptake of ligand conjugated PIN by prostate cancer cells (PC3) was also obtained. Both phantom-based and in vitro cell culture results demonstrated that PIN (1) have the great potential to induce local hyperthermia (i.e., temperature increase of 8 to 10°C) in tissue within 5 mm both in radius and in depth; (2) result in improved optical stability, excellent biocompatibility with healthy cells, and a great targeting capability; (3) have the ability to serve as an image contrast agent for deep-tissue imaging in diffuse optical tomography.

Monitoring early tumor response to drug therapy with diffuse optical tomography

Although anti-angiogenic agents have shown promise as cancer therapeutics, their efficacy varies between tumor types and individual patients. Providing patient-specific metrics through rapid noninvasive imaging can help tailor drug treatment by optimizing dosages, timing of drug cycles, and duration of therapy-thereby reducing toxicity and cost and improving patient outcome. Diffuse optical tomography (DOT) is a noninvasive three-dimensional imaging modality that has been shown to capture physiologic changes in tumors through visualization of oxygenated, deoxygenated, and total hemoglobin concentrations, using non-ionizing radiation with near-infrared light. We employed a small animal model to ascertain if tumor response to bevacizumab (BV), an anti-angiogenic agent that targets vascular endothelial growth factor (VEGF), could be detected at early time points using DOT. We detected a significant decrease in total hemoglobin levels as soon as one day after BV treatment in responder xenograft tumors (SK-NEP-1), but not in SK-NEP-1 control tumors or in non-responder control or BV-treated NGP tumors. These results are confirmed by magnetic resonance imaging T2 relaxometry and lectin perfusion studies. Noninvasive DOT imaging may allow for earlier and more effective control of anti-angiogenic therapy.

Convergence rate calculation of simultaneous iterative reconstruction technique algorithm for diffuse optical tomography image reconstruction: A feasibility study

Diffuse optical tomography (DOT) is an emerging technique for functional biological imaging. The imaging quality of DOT depends on the imaging reconstruction algorithm. The SIRT has been widely used for DOT image reconstruction but there is no criterion to truncate based on any kind of residual parameter. The iteration loops will always be decided by experimental rule. This work presents the CR calculation that can be great help for SIRT optimization. In this paper, four inhomogeneities with various shapes of absorption distributions are simulated as imaging targets. The images are reconstructed and analyzed based on the simultaneous iterative reconstruction technique (SIRT) method. For optimization between time consumption and imaging accuracy in reconstruction process, the numbers of iteration loop needed to be optimized with a criterion in algorithm, that is, the root mean square error (RMSE) should be minimized in limited iterations. For clinical applications of DOT, the RMSE cannot be obtained because the measured targets are unknown. Thus, the correlations between the RMSE and the convergence rate (CR) in SIRT algorithm are analyzed in this paper. From the simulation results, the parameter CR reveals the related RMSE value of reconstructed images. The CR calculation offers an optimized criterion of iteration process in SIRT algorithm for DOT imaging. Based on the result, the SIRT can be modified with CR calculation for self-optimization. CR reveals an indicator of SIRT image reconstruction in clinical DOT measurement. Based on the comparison result between RMSE and CR, a threshold value of CR (CRT) can offer an optimized number of iteration steps for DOT image reconstruction. This paper shows the feasibility study by utilizing CR criterion for SIRT in simulation and the clinical application of DOT measurement relies on further investigation.

P2-10-09: Detecting Breast Cancer with Dynamic Diffuse Optical Tomographic Imaging.

Abstract Background: Over the last decade diffuse optical tomography (DOT) has emerged as a novel medical imaging modality. Near-infrared light is used to non-invasively probe biological tissue, and three-dimensional (3D) maps of blood-dependent parameters can be obtained. Several studies are underway to show the clinical utility of DOT for imaging brain disease, joint disease, and breast cancer. Here we present the largest clinical study to date that uses hemodynamic effects caused by a simple breath hold to identify breast tumors using DOT. Methods: We have designed and built a DOT breast imaging system that can acquire full 3D data sets within a fraction of a second. The system affords the ability to study fast hemodynamic effects in both breasts simultaneously. Using non-compressive imaging heads we measured the oxygenated and deoxygenated hemoglobin levels in the breasts of 15 patients whose mammogram showed a mass >1 cm (4 benign, 11 malignant) and 3 healthy controls. Data was acquired over the course of a 30-second breath hold and 30 seconds thereafter. In addition, a baseline measurement of 30 seconds prior to the breath hold was obtained. A multi-wavelength image reconstruction algorithm was used to create 3D maps of hemoglobin-dependent parameters (Δ[HbO2] and Δ[Hb]) in the breast every 0.58 seconds, over the course of the 90-second experiment. An image analysis algorithm identified regions of peak percentage change in [Hb] and [HbO2] in the breast and then computed the average hemoglobin levels in those regions. Results: We observed an increase in the hemoglobin levels in all breasts during the breath hold. Upon resuming breathing, these hemoglobin levels returned to baseline. Tumor bearing breasts showed a statistically significant slower return to baseline than healthy breasts. In particular, we found that tumors can be detected by a substantially larger Δ[Hb] value as compared to normal tissue in images acquired 15 seconds following the end of the breath hold. In 10 of 11 patients the malignant tumor was identified using this technique, suggesting sensitivity over 90%. No regions of increased Δ[Hb] were seen in the healthy breasts, or in the breasts with benign masses. The peak percentage change in [Hb] at the 15 second post-breath hold time point was 10.0 ± 6.0% (n=11) in the malignant tumors compared to 1.4 ± 0.5% (n=3) (p=0.001) in healthy patients and 4.8 ± 1.9% in benign masses (n=4)(p=0.03). Discussion: A breath hold impedes venous return to the heart, which causes pooling of blood in the breast. This is observed as an increase in [Hb] and [HbO2] using DOT. Tumor vasculature is known to be more disorganized, tortuous, and leakier than normal vasculature. Therefore, once the breath hold is released and blood is allowed to drain from the breast, blood accumulated in the tumor during the breath hold will drain more slowly than blood in healthy tissue. This study has shown that DOT allows us to visualize these hemodynamic effects and use them to detect tumors with a simple breath hold and compression-free imaging head. Future studies need to explore the detection limits and general clinical utility of this technique for screening, differentiating malignant from benign masses, and treatment monitoring. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P2-10-09.

Somatosensory activation of two fingers can be discriminated with ultrahigh-density diffuse optical tomography

Topographic non-invasive near infrared spectroscopy (NIRS) has become a well-established tool for functional brain imaging. Applying up to 100 optodes over the head of a subject, allows achieving a spatial resolution in the centimeter range. This resolution is poor compared to other functional imaging tools. However, recently it was shown that diffuse optical tomography (DOT) as an extension of NIRS based on high-density (HD) probe arrays and supplemented by an advanced image reconstruction procedure allows describing activation patterns with a spatial resolution in the millimeter range. Building on these findings, we hypothesize that HD-DOT may render very focal activations accessible which would be missed by the traditionally used sparse arrays. We examined activation patterns in the primary somatosensory cortex, since its somatotopic organization is very fine-grained. We performed a vibrotactile stimulation study of the first and fifth finger in eight human subjects, using a 900-channel continuous-wave DOT imaging system for achieving a higher resolution than conventional topographic NIRS. To compare the results to a well-established high-resolution imaging technique, the same paradigm was investigated in the same subjects by means of functional magnetic resonance imaging (fMRI). In this work, we tested the advantage of ultrahigh-density probe arrays and show that highly focal activations would be missed by classical next-nearest neighbor NIRS approach, but also by DOT, when using a sparse probe array. Distinct activation patterns for both fingers correlated well with the expected neuroanatomy in five of eight subjects. Additionally we show that activation for different fingers is projected to different tissue depths in the DOT image. Comparison to the fMRI data yielded similar activation foci in seven out of ten finger representations in these five subjects when comparing the lateral localization of DOT and fMRI results.

Tumor‐Targeted Polydiacetylene Micelles for In Vivo Imaging and Drug Delivery

In vivo tumor targeting and drug delivery properties of small polymerized polydiacetylene (PDA) micelles (∼10 nm) is investigated in a murine MDA-MB-231 xenograft model of breast cancer. Three micelles with different surface coatings are synthesized and tested for their ability to passively target tumor through the enhanced permeability and retention effect. After injection (24 h), fluorescence diffuse optical tomographic imaging indicates a tumor uptake of nearly 3% of the injected dose for the micelles with a 2 kDa poly(ethylene glycol) (PEG)-coating (PDA-PEG2000). The uptake of PDA micelles in tumors is confirmed by co-localization with [(18) F]-fluorodeoxyglucose (FDG) positron emission tomography. Although FDG has a higher diffusion rate in tumors, 40 ± 19% of the retained micelles is co-registered with the tumor volume visualized by FDG. Finally, PDA-PEG2000 micelles are loaded with the hydrophobic anticancer drug paclitaxel and used in vivo to inhibit tumor growth. These findings demonstrate the potential of PDA-PEG2000 micelles for both in vivo tumor imaging and drug delivery applications.

Improvement of absorption and scattering discrimination by selection of sensitive points on temporal profile in diffuse optical tomography

We present a new method allowing the reconstruction of 3D time-domain diffuse optical tomography images, based on the time-dependent diffusion equation and an iterative algorithm (ART) using specific points on the temporal profiles. The first advantage of our method versus the full time-resolved scheme consists in considerably reducing the inverse problem resolution time. Secondly, in the presence of scattering heterogeneities, our method provides images of better quality comparatively to classical methods using full-time data or the first moments of the profiles.

Reduction of Poisson noise in measured time-resolved data for time-domain diffuse optical tomography

A method to reduce noise for time-domain diffuse optical tomography (DOT) is proposed. Poisson noise which contaminates time-resolved photon counting data is reduced by use of maximum a posteriori estimation. The noise-free data are modeled as a Markov random process, and the measured time-resolved data are assumed as Poisson distributed random variables. The posterior probability of the occurrence of the noise-free data is formulated. By maximizing the probability, the noise-free data are estimated, and the Poisson noise is reduced as a result. The performances of the Poisson noise reduction are demonstrated in some experiments of the image reconstruction of time-domain DOT. In simulations, the proposed method reduces the relative error between the noise-free and noisy data to about one thirtieth, and the reconstructed DOT image was smoothed by the proposed noise reduction. The variance of the reconstructed absorption coefficients decreased by 22% in a phantom experiment. The quality of DOT, which can be applied to breast cancer screening etc., is improved by the proposed noise reduction.

Optical mammography combined with fluorescence imaging: lesion detection using scatterplots

Using scatterplots of 2 or 3 parameters, diffuse optical tomography and fluorescence imaging are combined to improve detectability of breast lesions. Small or low contrast phantom-lesions that were missed in the optical and fluorescence images were detected in the scatterplots. In patient measurements, all tumors were visible and easily differentiated from artifacts and areolas in the scatterplots. The different rate of intake and wash out of the fluorescent contrast agent in the healthy versus malignant tissues was also observed in the scatterplot: this information can be used to discriminate malignant lesion from normal structures.

Light propagation from fluorescent probes in biological tissues by coupled time-dependent parabolic simplified spherical harmonics equations

We introduce a system of coupled time-dependent parabolic simplified spherical harmonic equations to model the propagation of both excitation and fluorescence light in biological tissues. We resort to a finite element approach to obtain the time-dependent profile of the excitation and the fluorescence light fields in the medium. We present results for cases involving two geometries in three-dimensions: a homogeneous cylinder with an embedded fluorescent inclusion and a realistically-shaped rodent with an embedded inclusion alike an organ filled with a fluorescent probe. For the cylindrical geometry, we show the differences in the time-dependent fluorescence response for a point-like, a spherical, and a spherically Gaussian distributed fluorescent inclusion. From our results, we conclude that the model is able to describe the time-dependent excitation and fluorescent light transfer in small geometries with high absorption coefficients and in nondiffusive domains, as may be found in small animal diffuse optical tomography (DOT) and fluorescence DOT imaging.

A Regularized Gauss–Newton Trust Region Approach to Imaging in Diffuse Optical Tomography

We present a new algorithm for the solution of nonlinear least squares problems arising from parameterized imaging problems with diffuse optical tomographic data [D. Boas et al., IEEE Signal Process. Mag., 18 (2001), pp. 57–75]. The parameterization arises from the use of parametric level sets for regularization [M. E. Kilmer et al., Proc. SPIE, 5559 (2004), pp. 381–391], [A. Aghasi, M. E. Kilmer, and E. L. Miller, SIAM J. Imaging Sci., 4 (2011), pp. 618–650]. Such problems lead to Jacobians that have relatively few columns, a relatively modest number of rows, and are ill-conditioned. Moreover, such problems have function and Jacobian evaluations that are computationally expensive. Our optimization algorithm is appropriate for any inverse or imaging problem with those characteristics. In fact, we expect our algorithm to be effective for more general problems with ill-conditioned Jacobians. The algorithm aims to minimize the total number of function and Jacobian evaluations by analyzing which spectral components of the Gauss–Newton direction should be discarded or damped. The analysis considers for each component the reduction of the objective function and the contribution to the search direction, restricting the computed search direction to be within a trust region. The result is a truncated SVD-like approach to choosing the search direction. However, we do not necessarily discard components in order of decreasing singular value, and some components may be scaled to maintain fidelity to the trust region model. Our algorithm uses the Basic Trust Region Algorithm from [A. R. Conn, N. I. M. Gould, and Ph. L. Toint, Trust-Region Methods, SIAM, Philadelphia, 2000]. We prove that our algorithm is globally convergent to a critical point. Our numerical results show that the new algorithm generally outperforms competing methods applied to the DOT imaging problem with parametric level sets, and regularly does so by a significant factor.

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.

Accelerated gradient based diffuse optical tomographic image reconstruction

Fast reconstruction of interior optical parameter distribution using a new approach called Broyden-based model iterative image reconstruction (BMOBIIR) and adjoint Broyden-based MOBIIR (ABMOBIIR) of a tissue and a tissue mimicking phantom from boundary measurement data in diffuse optical tomography (DOT). DOT is a nonlinear and ill-posed inverse problem. Newton-based MOBIIR algorithm, which is generally used, requires repeated evaluation of the Jacobian which consumes bulk of the computation time for reconstruction. In this study, we propose a Broyden approach-based accelerated scheme for Jacobian computation and it is combined with conjugate gradient scheme (CGS) for fast reconstruction. The method makes explicit use of secant and adjoint information that can be obtained from forward solution of the diffusion equation. This approach reduces the computational time many fold by approximating the system Jacobian successively through low-rank updates. Simulation studies have been carried out with single as well as multiple inhomogeneities. Algorithms are validated using an experimental study carried out on a pork tissue with fat acting as an inhomogeneity. The results obtained through the proposed BMOBIIR and ABMOBIIR approaches are compared with those of Newton-based MOBIIR algorithm. The mean squared error and execution time are used as metrics for comparing the results of reconstruction. We have shown through experimental and simulation studies that Broyden-based MOBIIR and adjoint Broyden-based methods are capable of reconstructing single as well as multiple inhomogeneities in tissue and a tissue-mimicking phantom. Broyden MOBIIR and adjoint Broyden MOBIIR methods are computationally simple and they result in much faster implementations because they avoid direct evaluation of Jacobian. The image reconstructions have been carried out with different initial values using Newton, Broyden, and adjoint Broyden approaches. These algorithms work well when the initial guess is close to the true solution. However, when initial guess is far away from true solution, Newton-based MOBIIR gives better reconstructed images. The proposed methods are found to be stable with noisy measurement data.

Discretization Error Analysis and Adaptive Meshing Algorithms for Fluorescence Diffuse Optical Tomography in the Presence of Measurement Noise

Quantitatively accurate fluorescence diffuse optical tomographic (FDOT) image reconstruction is a computationally demanding problem that requires repeated numerical solutions of two coupled partial differential equations and an associated inverse problem. Recently, adaptive finite element methods have been explored to reduce the computation requirements of the FDOT image reconstruction. However, existing approaches ignore the ubiquitous presence of noise in boundary measurements. In this paper, we analyze the effect of finite element discretization on the FDOT forward and inverse problems in the presence of measurement noise and develop novel adaptive meshing algorithms for FDOT that take into account noise statistics. We formulate the FDOT inverse problem as an optimization problem in the maximum a posteriori framework to estimate the fluorophore concentration in a bounded domain. We use the mean-square-error (MSE) between the exact solution and the discretized solution as a figure of merit to evaluate the image reconstruction accuracy, and derive an upper bound on the MSE which depends upon the forward and inverse problem discretization parameters, noise statistics, a priori information of fluorophore concentration, source and detector geometry, as well as background optical properties. Next, we use this error bound to develop adaptive meshing algorithms for the FDOT forward and inverse problems to reduce the MSE due to discretization in the reconstructed images. Finally, we present a set of numerical simulations to illustrate the practical advantages of our adaptive meshing algorithms for FDOT image reconstruction.

Effective optode configuration for the image reconstruction in diffuse optical tomography

Abstract The influence of the configuration of the optodes on the images of diffuse optical tomography (DOT) was investigated using 3D numerical simulations. 3D distributions of the absorption coefficients in a spherical object were reconstructed from the numerically simulated measurement data for various configurations of the optodes. When the optodes were placed in a plane containing a target which strongly absorbs light, the target could be reconstructed with good localization. For good reconstruction of the target, it was found to be very important that the optode configuration was optimized in order to detect light propagating through the target effectively. The simulations also showed that the optode configuration affects the quality of the reconstructed images and that some prior information about the measured object improved the DOT images. Finally the simulation results were verified by a phantom experiment.

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.