Diffuse optical tomography using multi-directional sources and detectors

High-density diffuse optical tomography (HD-DOT) is an emerging technique for visualizing the internal state of biological tissues. The large number of overlapping measurement channels due to the use of high-density probe arrays permits the reconstruction of the internal optical properties, even with a reflectance-only measurement. However, accurate three-dimensional reconstruction is still a challenging problem. First, the exponentially decaying sensitivity causes a systematic depth-localization error. Second, the nature of diffusive light makes the image blurred. In this paper, we propose a three-dimensional reconstruction method that overcomes these two problems by introducing sensitivity-normalized regularization and sparsity into the hierarchical Bayesian method. Phantom experiments were performed to validate the proposed method under three conditions of probe interval: 26 mm, 18.4 mm, and 13 mm. We found that two absorbers with distances shorter than the probe interval could be discriminated under the high-density conditions of 18.4-mm and 13-mm intervals. This discrimination ability was possible even if the depths of the two absorbers were different from each other. These results show the high spatial resolution of the proposed method in both depth and horizontal directions.

Three-dimensional diffuse optical imaging of hand joints: System description and phantom studies

Diffuse optical tomography (DOT) is a non-invasive imaging modality that uses near-infrared light to probe the optical properties of tissue. In conventionally used deterministic methods for DOT inversion, the measurement errors were not taken into account, resulting in unsatisfactory noise robustness and, consequently, affecting the DOT image reconstruction quality. In order to overcome this defect, an extended Kalman filter (EKF)-based DOT reconstruction algorithm was introduced first, which improved the reconstruction results by incorporating a priori information and measurement errors to the model. Further, to mitigate the instability caused by the ill-condition of the observation matrix in the tomographic imaging problem, a new, to the best of our knowledge, estimation algorithm was derived by incorporating Tikhonov regularization to the EKF method. To verify the effectiveness of the EKF algorithm and Tikhonov regularization-based EKF algorithm for DOT imaging, a series of numerical simulations and phantom experiments were conducted, and the experimental results were quantitatively evaluated and compared with two conventionally used deterministic methods involving the algebraic reconstruction technique and Levenberg-Marquardt algorithm. The results show that the two EKF-based algorithms can accurately estimate the location and size of the target, and the imaging accuracy and noise robustness are obviously improved. Furthermore, the Tikhonov regularization-based EKF obtained optimal parameter estimations, especially under the circumstance of low absorption contrast (1.2) and high noise level (10%).

The accuracy and clinical utility of diffuse optical imaging systems rely on continuous advancements in instrumentation, theoretical modeling, and computational methods, alongside strategies to overcome persistent barriers to clinical translation. A key challenge in diffuse optical imaging research is the development of computationally efficient numerical models that accurately describe light-tissue interactions in complex anatomical domains, along with the validation of these models and novel instrumentation using realistic tissue-mimicking phantoms. Although diffuse optical imaging remains a promising area of research for applications such as cancer detection and functional brain activity assessment, progress has been limited by the lack of anatomically complex optical phantoms and the high computational cost of Monte Carlo simulations required to model light propagation. This dissertation addresses these challenges through two key innovations. First, we designed a multi-filament-mixing 3-D printing platform which enables spatially varying absorption, scattering, and luminescent optical properties by dynamically blending multiple commercially available filaments. This platform facilitates the design and fabrication of anatomically accurate phantoms with precise control over optical absorbance and scattering parameters, while also incorporating tunable luminescence intensity through the embedding of phosphorescent materials. We printed several benchmark phantoms showcasing our method's capability of generating complex anatomical shapes with continuously varying optical properties. This method should therefore allow for the design and easy fabrication of highly complex, anatomically accurate phantoms for benchmarking, calibration, and training across a wide range of diffuse optical imaging applications. Second, we introduce a memory-efficient parametric modeling approach to address the computational challenges in capturing detected photon partial path length (PPL) distributions in Monte Carlo simulations. This method utilizes numerical stochastic estimation techniques, such as the Box-Cox transformation, skew-normal distribution modeling, and rejection sampling, to efficiently approximate the PPL distribution with a small set of statistical parameters. This approach significantly reduces the memory footprint of large-scale Monte Carlo simulations, while remaining generalizable to anatomically accurate domains. Furthermore, the ability to regenerate an arbitrary number of detected photon PPLs using the recorded parametric PPL models demonstrates significant computational savings while retaining high levels of accuracy. Together, these findings enable more accessible and accurate diffuse optical imaging modeling by substantially reducing the computational costs typically associated with simulating light transport in complex anatomical domains. In summary, this dissertation presents experimental and computational advancements that are broadly applicable to diffuse optical imaging research. Both innovations are generalizable to complex tissue-like domains, enhancing their relevance across a wide range of applications. These contributions support the continued development of diffuse optical imaging technologies and promote their clinical translation, helping to enable more accessible, cost-effective, and practical imaging systems.--Author's abstract

Imaging of tissue with Ultrasound-guided diffuse optical tomography (DOT) is a rising imaging technique to map hemoglobin concentrations within tissue for breast cancer detection and diagnosis. Near-infrared optical imaging received a lot of attention in research as a possible technique to be used for such purpose especially for breast tumors. Since DOT images contrast is closely related to oxygenation and deoxygenating of the hemoglobin, which is an important factor in differentiating malignant and benign tumors. One of the optical imaging modalities used is the diffused optical tomography (DOT); which probes deep scattering tissue (1-5cm) by NIR optical source-detector probe and detects NIR photons in the diffusive regime. The photons in the diffusive regime usually reach the detector without significant information about their source direction and the propagation path. Because of that, the optical reconstruction problem of the medium characteristics is ill-posed even with the tomography and Back-projection techniques. The accurate recovery of images requires an effective image reconstruction method. Here, we illustrate a method in which ultrasound images are encoded as prior for regularization of the inversion matrix. Results were evaluated using phantom experiments of low and high absorption contrasts. This method improves differentiation between the low and the high contrasts targets. Ultimately, this method could improve malignant and benign cases by increasing reconstructed absorption ratio of malignant to benign. Besides that, the phantom results show improvements in target shape as well as the spatial resolution of the DOT reconstructed images.

The underdeterminedness of the inverse problems encountered in diffuse optical tomography (DOT) becomes especially severe when detecting breast cancers, because much more variables are needed to be reconstructed due to the big-size. With the addition of ill-condition caused by the diffusive nature of light propagation, the ill-posedness makes it very difficult to improve the image reconstruction. Fortunately, from the anatomy viewpoint, we have known that the cancer is distributed locally and only amounts to a small percentage of the whole breast. This makes it possible to employ the compressive sensing theory to mitigate the ill-posedness, based on the prior knowledge about the sparsity of the signal to be reconstructed. Specifically speaking, sparsity regularizations can be used in DOT to improve the image reconstruction under the premise that un-increase the number of measurements required in the reconstruction. In this paper, we primarily focus on comparing the performances of different kinds of L_p-norm-based regularizations in terms of theory and real effects, respectively. The numerical and phantom experiments have proven that the sparsity regularizations can dramatically improve the image reconstruction. Furthermore, as the p in the L_p-norm decreasing to zero, the solutions become sparser and the corresponding image quality gets higher, with smooth L₀-norm-based regularization providing the highest image quality.

Effective optode configuration for the image reconstruction in diffuse optical tomography

There are different studies that allow detecting the presence of cancer cells in a patient; however, the time it takes to obtain a correct diagnosis is critical for these cases. This work presents the design and construction of a prototype as first approach for a microtomograph based on Diffuse Optical Tomography (DOT). Diffused Optical Tomography is a technique that uses light from the Near Infrared Region (NIR) to measure tissue’s optical properties that has become relevant for the medical field due to it being a non-invasive technique. The main goal of this device is to be able to detect and analyze cancerous tumors by measuring diffuse photon densities in turbid media. As a first phase of the development, this project integrates an image acquisition mechanism, an image processing interface at software level developed with Blender to exploit a GPU architecture to optimize the execution time.

Reconstruction algorithms are presented for two-step solutions of the bioluminescence tomography (BLT) and the fluorescence tomography (FT) problems. In the first step, a continuous wave (cw) diffuse optical tomography (DOT) algorithm is used to reconstruct the tissue optical properties assuming known anatomical information provided by x-ray computed tomography or other methods. Minimization problems are formed based on L1 norm objective functions, where normalized values for the light fluence rates and the corresponding Green’s functions are used. Then an iterative minimization solution shrinks the permissible regions where the sources are allowed by selecting points with higher probability to contribute to the source distribution. Throughout this process the permissible region shrinks from the entire object to just a few points. The optimum reconstructed bioluminescence and fluorescence distributions are chosen to be the results of the iteration corresponding to the permissible region where the objective function has its global minimum This provides efficient BLT and FT reconstruction algorithms without the need for a priori information about the bioluminescence sources or the fluorophore concentration. Multiple small sources and large distributed sources can be reconstructed with good accuracy for the location and the total source power for BLT and the total number of fluorophore molecules for the FT. For non-uniform distributed sources, the size and magnitude become degenerate due to the degrees of freedom available for possible solutions. However, increasing the number of data points by increasing the number of excitation sources can improve the accuracy of reconstruction for non-uniform fluorophore distributions.

Diffuse optical tomography (DOT) is to reconstruct the images of internal optical parameters distribution from boundary measurements. Due to the amount of available boundary measurements is less than the number of unknown optical parameters to be recovered, this inverse problem usually shows the ill-posed characteristics. This will result in the problem of low reconstruction image quality. In this paper, an adaptive regularization method based on the objective function values is proposed, which reduces the ill-posed characteristics in the inverse problem by selecting an appropriate regularization value at teach iteration. Results from computer simulations indicated that using this regularization technique, DOT imaging quality is improved effectively. Furthermore, using the regularization technique, the sensitivity to noise of the reconstructed images can be decreased greatly.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Diffuse optical topography has excellent features as a noninvasive method that provides 2D location information of cortical activity. However, it cannot distinguish the activation depth. We propose an image reconstruction algorithm that suppresses undesirable effects of skin circulation. It comprises a filtering algorithm that extracts target signals from observation data contaminated by disturbing signals and a 2D visualizing process. Computer simulations revealed its excellent performance. We developed a depth selective diffuse optical topography system prototype and performed phantom experiments. Our algorithm significantly suppressed the influence of the disturbing body in the shallow plane with minimal degradation of the target signal.

Diffuse optical topography has excellent features as a noninvasive method that provides 2D location information of cortical activity. However, it cannot distinguish the activation depth. We propose an image reconstruction algorithm that suppresses undesirable effects of skin circulation. It comprises a filtering algorithm that extracts target signals from observation data contaminated by disturbing signals and a 2D visualizing process. Computer simulations revealed its excellent performance. We developed a depth selective diffuse optical topography system prototype and performed phantom experiments. Our algorithm significantly suppressed the influence of the disturbing body in the shallow plane with minimal degradation of the target signal.

Developing a computationally efficient automated method for the optimal choice of regularization parameter in diffuse optical tomography. The least-squares QR (LSQR)-type method that uses Lanczos bidiagonalization is known to be computationally efficient in performing the reconstruction procedure in diffuse optical tomography. The same is effectively deployed via an optimization procedure that uses the simplex method to find the optimal regularization parameter. The proposed LSQR-type method is compared with the traditional methods such as L-curve, generalized cross-validation (GCV), and recently proposed minimal residual method (MRM)-based choice of regularization parameter using numerical and experimental phantom data. The results indicate that the proposed LSQR-type and MRM-based methods performance in terms of reconstructed image quality is similar and superior compared to L-curve and GCV-based methods. The proposed method computational complexity is at least five times lower compared to MRM-based method, making it an optimal technique. The LSQR-type method was able to overcome the inherent limitation of computationally expensive nature of MRM-based automated way finding the optimal regularization parameter in diffuse optical tomographic imaging, making this method more suitable to be deployed in real-time.

Near Infrared diffuse optical tomography was used to measure the molar absorption spectra for de-oxyhemoglobin, oxyhemoglobin and water through phantom experiments. The measured spectra compensate for any systemic errors and errors arising from model-based estimation of concentrations of these chromophores, and show reasonable agreement with existing literature values. The spectra were validated by phantom experiments to obtain the oxygen dissociation curve. Variation of the partial pressure of oxygen in solution yielded results consistent with theory of mean error of 7.6% in estimation of oxygen saturation, and accurate within 5% in estimation of hemoglobin.

Time-domain (TD) spatial frequency domain (SFD) diffuse optical tomography (DOT) potentially enables laminar tomography of both the absorption and scattering coefficients. Its full time-resolved-data scheme is expected to enhance performances of the image reconstruction but poses heavy computational costs and also susceptible signal-to-noise ratio (SNR) limits, as compared to the featured-data one. We herein propose a computationally-efficient linear scheme of TD-SFD-DOT, where an analytical solution to the TD phasor diffusion equation for semi-infinite geometry is derived and used to formulate the Jacobian matrices with regard to overlap time-gating data of the time-resolved measurement for improved SNR and reduced redundancy. For better contrasting the absorption and scattering and widely adapted to practically-available resources, we develop an algebraic-reconstruction-technique-based two-step linear inversion procedure with support of a balanced memory-speed strategy and multi-core parallel computation. Both simulations and phantom experiments are performed to validate the effectiveness of the proposed TD-SFD-DOT method and show an achieved tomographic reconstruction at a relative depth resolution of ∼4 mm.