Effective optode configuration for the image reconstruction in diffuse optical tomography

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.     

Regression-based neural network for improving image reconstruction in diffuse optical tomography

Diffuse optical tomography (DOT) is a non-invasive imaging technique utilizing multi-scattered light at visible and infrared wavelengths to detect anomalies in tissues. However, the DOT image reconstruction is based on solving the inverse problem, which requires massive calculations and time. In this article, for the first time, to the best of our knowledge, a simple, regression-based cascaded feed-forward deep learning neural network is derived to solve the inverse problem of DOT in compressed breast geometry. The predicted data is subsequently utilized to visualize the breast tissues and their anomalies. The dataset in this study is created using a Monte-Carlo algorithm, which simulates the light propagation in the compressed breast placed inside a parallel plate source-detector geometry (forward process). The simulated DL-DOT system''s performance is evaluated using the Pearson correlation coefficient (R) and the Mean squared error (MSE) metrics. Although a comparatively smaller dataset (50 nos.) is used, our simulation results show that the developed feed-forward network algorithm to solve the inverse problem delivers an increment of ∼30% over the analytical solution approach, in terms of R. Furthermore, the proposed network''s MSE outperforms that of the analytical solution''s MSE by a large margin revealing the robustness of the network and the adaptability of the system for potential applications in medical settings.     

Evaluating real-time image reconstruction in diffuse optical tomography using physiologically realistic test data

In diffuse optical tomography (DOT), real-time image reconstruction of oxy- and deoxy-haemoglobin changes occurring in the brain could give valuable information in clinical care settings. Although non-linear reconstruction techniques could provide more accurate results, their computational burden makes them unsuitable for real-time applications. Linear techniques can be employed under the assumption that the expected change in absorption is small. Several approaches exist, differing primarily in their handling of regularization and the noise statistics. In real experiments, it is impossible to compute the true noise statistics, because of the presence of physiological oscillations in the measured data. This is even more critical in real-time applications, where no off-line filtering and averaging can be performed to reduce the noise level. Therefore, many studies substitute the noise covariance matrix with the identity matrix. In this paper, we examined two questions: does using the noise model with realistic, imperfect data yield an improvement in image quality compared to using the identity matrix; and what is the difference in quality between online and offline reconstructions. Bespoke test data were created using a novel process through which simulated changes in absorption were added to real resting-state DOT data. A realistic multi-layer head model was used as the geometry for the reconstruction. Results validated our assumptions, highlighting the validity of computing the noise statistics from the measured data for online image reconstruction, which was performed at 2 Hz. Our results can be directly extended to a real application where real-time imaging is required.OCIS codes: (100.0100) Image processing, (100.3010) Image reconstruction techniques, (100.3190) Inverse problems     

Parametric level set reconstruction methods for hyperspectral diffuse optical tomography

A parametric level set method (PaLS) is implemented for image reconstruction for hyperspectral diffuse optical tomography (DOT). Chromophore concentrations and diffusion amplitude are recovered using a linearized Born approximation model and employing data from over 100 wavelengths. The images to be recovered are taken to be piecewise constant and a newly introduced, shape-based model is used as the foundation for reconstruction. The PaLS method significantly reduces the number of unknowns relative to more traditional level-set reconstruction methods and has been show to be particularly well suited for ill-posed inverse problems such as the one of interest here. We report on reconstructions for multiple chromophores from simulated and experimental data where the PaLS method provides a more accurate estimation of chromophore concentrations compared to a pixel-based method.     

Fast and efficient image reconstruction for high density diffuse optical imaging of the human brain

Real-time imaging of human brain has become an important technique within neuroimaging. In this study, a fast and efficient sensitivity map generation based on Finite Element Models (FEM) is developed which utilises a reduced sensitivitys matrix taking advantage of sparsity and parallelisation processes. Time and memory efficiency of these processes are evaluated and compared with conventional method showing that for a range of mesh densities from 50000 to 320000 nodes, the required memory is reduced over tenfold and computational time fourfold allowing for near real-time image recovery.OCIS codes: (110.6960) Tomography, (170.2655) Functional monitoring and imaging, (170.3010) Image reconstruction techniques, (170.3660) Light propagation in tissues     

Dynamic physiological modeling for functional diffuse optical tomography

Diffuse optical tomography (DOT) is a noninvasive imaging technology that is sensitive to local concentration changes in oxy- and deoxyhemoglobin. When applied to functional neuroimaging, DOT measures hemodynamics in the scalp and brain that reflect competing metabolic demands and cardiovascular dynamics. The diffuse nature of near-infrared photon migration in tissue and the multitude of physiological systems that affect hemodynamics motivate the use of anatomical and physiological models to improve estimates of the functional hemodynamic response. In this paper, we present a linear state-space model for DOT analysis that models the physiological fluctuations present in the data with either static or dynamic estimation. We demonstrate the approach by using auxiliary measurements of blood pressure variability and heart rate variability as inputs to model the background physiology in DOT data. We evaluate the improvements accorded by modeling this physiology on ten human subjects with simulated functional hemodynamic responses added to the baseline physiology. Adding physiological modeling with a static estimator significantly improved estimates of the simulated functional response, and further significant improvements were achieved with a dynamic Kalman filter estimator (paired t tests, n=10, P<0.05). These results suggest that physiological modeling can improve DOT analysis. The further improvement with the Kalman filter encourages continued research into dynamic linear modeling of the physiology present in DOT. Cardiovascular dynamics also affect the blood-oxygen-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI). This state-space approach to DOT analysis could be extended to BOLD fMRI analysis, multimodal studies and real-time analysis.     

Improvement of image quality of time-domain diffuse optical tomography with l sparsity regularization

An l(p) (0 < p ≤ 1) sparsity regularization is applied to time-domain diffuse optical tomography with a gradient-based nonlinear optimization scheme to improve the spatial resolution and the robustness to noise. The expression of the l(p) sparsity regularization is reformulated as a differentiable function of a parameter to avoid the difficulty in calculating its gradient in the optimization process. The regularization parameter is selected by the L-curve method. Numerical experiments show that the l(p) sparsity regularization improves the spatial resolution and recovers the difference in the absorption coefficients between two targets, although a target with a small absorption coefficient may disappear due to the strong effect of the l(p) sparsity regularization when the value of p is too small. The l(p) sparsity regularization with small p values strongly localizes the target, and the reconstructed region of the target becomes smaller as the value of p decreases. A phantom experiment validates the numerical simulations.     

Integrated diffuse optical tomography and photoacoustic tomography: phantom validations

We designed, fabricated and tested a novel imaging system that fuses diffuse optical tomography (DOT) and photoacoustic tomography (PAT) in a single platform. This platform takes advantages of both DOT and PAT, and can potentially provide dual-modality two dimensional functional and cellular images of the breast quantitatively. Here we describe this integrated platform along with initial tissue phantom validations.     

Shape-parameterized diffuse optical tomography holds promise for sensitivity enhancement of fluorescence molecular tomography

A fundamental approach to enhancing the sensitivity of the fluorescence molecular tomography (FMT) is to incorporate diffuse optical tomography (DOT) to modify the light propagation modeling. However, the traditional voxel-based DOT has been involving a severely ill-posed inverse problem and cannot retrieve the optical property distributions with the acceptable quantitative accuracy and spatial resolution. Although, with the aid of an anatomical imaging modality, the structural-prior-based DOT method with either the hard- or soft-prior scheme holds promise for in vivo acquiring the optical background of tissues, the low robustness of the hard-prior scheme to the segmentation error and inferior performance of the soft-prior one in the quantitative accuracy limit its further application. We propose in this paper a shape-parameterized DOT method for not only effectively determining the regional optical properties but potentially achieving reasonable structural amelioration, lending itself to FMT for comparably improved recovery of fluorescence distribution.OCIS codes: (170.3880) Medical and biological imaging, (170.6960) Tomography, (170.3010) Image reconstruction techniques     

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.     

Extended hierarchical Bayesian diffuse optical tomography for removing scalp artifact

Functional near-infrared spectroscopy (fNIRS) can non-invasively measure hemodynamic responses in the cerebral cortex with a portable apparatus. However, the observation signal in fNIRS measurements is contaminated by the artifact signal from the hemodynamic response in the scalp. In this paper, we propose a method to separate the signals from the cortex and the scalp by estimating both hemodynamic changes by diffuse optical tomography (DOT). In the inverse problem of DOT, we introduce smooth regularization to the hemodynamic change in the scalp and sparse regularization to that in the cortex based on the nature of the hemodynamic responses. These appropriate regularization models, with the spatial information of optical paths of many measurement channels, allow three-dimensional reconstruction of both hemodynamic changes. We validate our proposed method through two-layer phantom experiments and MRI-based head-model simulations. In both experiments, the proposed method simultaneously estimates the superficial smooth activity in the scalp area and the deep localized activity in the cortical area.OCIS codes: (100.3010) Image reconstruction techniques, (100.3190) Inverse problems, (170.3880) Medical and biological imaging     

A systematic investigation of reflectance diffuse optical tomography using nonlinear reconstruction methods and continuous wave measurements

We conducted a systematic investigation of the reflectance diffuse optical tomography using continuous wave (CW) measurements and nonlinear reconstruction algorithms. We illustrated and suggested how to fine-tune the nonlinear reconstruction methods in order to optimize target localization with depth-adaptive regularizations, reduce boundary noises in the reconstructed images using a logarithm based objective function, improve reconstruction quantification using transport models, and resolve crosstalk problems between absorption and scattering contrasts with the CW reflectance measurements. The upgraded nonlinear reconstruction algorithms were evaluated with a series of numerical and experimental tests, which show the potentials of the proposed approaches for imaging both absorption and scattering contrasts in the deep targets with enhanced image quality.OCIS codes: (170.5280) Photon migration, (170.3010) Image reconstruction techniques, (100.3190) Inverse problems, (170.6960) Tomography     

Anatomical atlas-guided diffuse optical tomography of brain activation

We describe a neuroimaging protocol that utilizes an anatomical atlas of the human head to guide diffuse optical tomography of human brain activation. The protocol is demonstrated by imaging the hemodynamic response to median-nerve stimulation in three healthy subjects, and comparing the images obtained using a head atlas with the images obtained using the subject-specific head anatomy. The results indicate that using the head atlas anatomy it is possible to reconstruct the location of the brain activation to the expected gyrus of the brain, in agreement with the results obtained with the subject-specific head anatomy. The benefits of this novel method derive from eliminating the need for subject-specific head anatomy and thus obviating the need for a subject-specific MRI to improve the anatomical interpretation of diffuse optical tomography images of brain activation.     

Multi-modulated frequency domain high density diffuse optical tomography

Frequency domain (FD) high density diffuse optical tomography (HD-DOT) utilising varying or combined modulation frequencies (mFD) has shown to theoretically improve the imaging accuracy as compared to conventional continuous wave (CW) measurements. Using intensity and phase data from a solid inhomogeneous phantom (NEUROPT) with three insertable rods containing different contrast anomalies, at modulation frequencies of 78 MHz, 141 MHz and 203 MHz, HD-DOT is applied and quantitatively evaluated, showing that mFD outperforms FD and CW for both absolute (iterative) and temporal (linear) tomographic imaging. The localization error (LOCA), full width half maximum (FWHM) and effective resolution (ERES) were evaluated. Across all rods, the LOCA of mFD was 61.3% better than FD and 106.1% better than CW. For FWHM, CW was 6.0% better than FD and mFD and for ERES, mFD was 1.20% better than FD and 9.83% better than CW. Using mFD data is shown to minimize the effect of inherently noisier FD phase data whilst maximising its strengths through improved contrast.     

Parametric estimation of 3D tubular structures for diffuse optical tomography

We explore the use of diffuse optical tomography (DOT) for the recovery of 3D tubular shapes representing vascular structures in breast tissue. Using a parametric level set method (PaLS) our method incorporates the connectedness of vascular structures in breast tissue to reconstruct shape and absorption values from severely limited data sets. The approach is based on a decomposition of the unknown structure into a series of two dimensional slices. Using a simplified physical model that ignores 3D effects of the complete structure, we develop a novel inter-slice regularization strategy to obtain global regularity. We report on simulated and experimental reconstructions using realistic optical contrasts where our method provides a more accurate estimate compared to an unregularized approach and a pixel based reconstruction.OCIS codes: (170.3010) Image reconstruction techniques, (170.6960) Tomography, (100.3190) Inverse problems, (170.3830) Mammography, (170.3880) Medical and biological imaging, (170.3660) Light propagation in tissues, (170.5280) Photon migration, (290.1990) Diffusion, (290.7050) Turbid media     

Lock-in-photon-counting-based highly-sensitive and large-dynamic imaging system for continuous-wave diffuse optical tomography

We implemented a novel lock-in photon-counting detection architecture that combines the ultra-high sensitivity of the photon-counting detection and the measurement parallelism of the lock-in technique. Based on this technique, a dual-wavelength simultaneous measurement continuous wave diffuse optical tomography system was developed with a configuration of 16 sources and 16 detectors that works in a tandem serial-to-parallel fashion. Methodology validation and performance assessment of the system were conducted using phantom experiments that demonstrate excellent measurement linearity, moderate-term system stability, robustness to noise and negligible inter-wavelength crosstalk. 2-D imaging experiments further validate high sensitivity of the lock-in photon-counting methodology as well as high reliability of the proposed system. The advanced detection principle can be adapted to achieving a fully parallelized instrumentation for the extended applications.OCIS codes: (120.0120) Instrumentation, measurement, and metrology; (170.2655) Functional monitoring and imaging; (170.3890) Medical optics instrumentation; (170.4090) Modulation techniques; (170.6960) Tomography     

Temporal image reconstruction in electrical impedance tomography

Electrical impedance tomography (EIT) calculates images of the body from body impedance measurements. While the spatial resolution of these images is relatively low, the temporal resolution of EIT data can be high. Most EIT reconstruction algorithms solve each data frame independently, although Kalman filter algorithms track the image changes across frames. This paper proposes a new approach which directly accounts for correlations between images in successive data frames. Image reconstruction is posed in terms of an augmented image x and measurement vector y, which concatenate the values from the d previous and future frames. Image reconstruction is then based on an augmented regularization matrix R, which accounts for a model of both the spatial and temporal correlations between image elements. Results are compared for reconstruction algorithms based on independent frames, Kalman filters and the proposed approach. For low values of the regularization hyperparameter, the proposed approach performs similarly to independent frames, but for higher hyperparameter values, it uses adjacent frame data to reduce reconstructed image noise.     

Self-calibrated algorithms for diffuse optical tomography and bioluminescence tomography using relative transmission images

Reconstruction algorithms for diffuse optical tomography (DOT) and bioluminescence tomography (BLT) have been developed based on diffusion theory. The algorithms numerically solve the diffusion equation using the finite element method. The direct measurements of the uncalibrated light fluence rates by a camera are used for the reconstructions. The DOT is self-calibrated by using all possible pairs of transmission images obtained with external sources along with the relative values of the simulated data and the calculated Jacobian. The reconstruction is done in the relative domain with the cancelation of any geometrical or optical factors. The transmission measurements for the DOT are used for calibrating the bioluminescence measurements at each wavelength and then a normalized system of equations is built up which is self-calibrated for the BLT. The algorithms have been applied to a three dimensional model of the mouse (MOBY) segmented into tissue regions which are assumed to have uniform optical properties. The DOT uses the direct method for calculating the Jacobian. The BLT uses a reduced space of eigenvectors of the Green''s function with iterative shrinking of the permissible source region. The reconstruction results of the DOT and BLT algorithms show good agreement with the actual values when using either absolute or relative data. Even a small calibration error causes significant degradation of the reconstructions based on absolute data.OCIS codes: (170.6960) Tomography, (170.3880) Medical and biological imaging     

Quantification in time-domain diffuse optical tomography using Mellin-Laplace transforms

Simulations and phantom measurements are used to evaluate the ability of time-domain diffuse optical tomography using Mellin-Laplace transforms to quantify the absorption perturbation of centimetric objects immersed at depth 1-2 cm in turbid media. We find that the estimated absorption coefficient varies almost linearly with the absorption change in the range of 0-0.15 cm⁻¹ but is underestimated by a factor that depends on the inclusion depth (~2, 3 and 6 for depths of 1.0, 1.5 and 2.0 cm respectively). For larger absorption changes, the variation is sublinear with ~20% decrease for δμ_a = 0.37 cm⁻¹. By contrast, constraining the absorption change to the actual volume of the inclusion may considerably improve the accuracy and linearity of the reconstructed absorption.OCIS codes: (170.6920) Time-resolved imaging, (110.6960) Tomography, (100.3010) Image reconstruction techniques, (110.0113) Imaging through turbid media, (030.5260) Photon counting, (230.5160) Photodetectors