Real-time diffuse optical tomography using reduced-order light propagation models based on a priori anatomical and functional information

This paper proposes a new fast 3D image reconstruction algorithm for Diffuse Optical Tomography using reduced order polynomial mappings from the space of optical tissue parameters into the space of flux measurements at the detector locations. The polynomial mappings are constructed through an iterative estimation process involving structure detection, parameter estimation and cross-validation using data generated by simulating a diffusion approximation of the radiative transfer equation incorporating a priori anatomical and functional information provided by MR scans and prior psychological evidence. Numerical simulation studies demonstrate that reconstructed images are remarkably similar in quality as those obtained using the standard approach, but obtained at a fraction of the time

EIT Reconstruction Algorithms: Pitfalls, Challenges and Recent Developments

We review developments, issues and challenges in Electrical Impedance Tomography (EIT), for the 4th Workshop on Biomedical Applications of EIT, Manchester 2003. We focus on the necessity for three dimensional data collection and reconstruction, efficient solution of the forward problem and present and future reconstruction algorithms. We also suggest common pitfalls or ``inverse crimes'' to avoid.Comment: A review paper for the 4th Workshop on Biomedical Applications of EIT, Manchester, UK, 200

Characterizing Accuracy of Total Hemoglobin Recovery Using Contrast-Detail Analysis in 3D Image-Guided Near Infrared Spectroscopy with the Boundary Element Method

The quantification of total hemoglobin concentration (HbT) obtained from multi-modality image-guided near infrared spectroscopy (IG-NIRS) was characterized using the boundary element method (BEM) for 3D image reconstruction. Multi-modality IG-NIRS systems use a priori information to guide the reconstruction process. While this has been shown to improve resolution, the effect on quantitative accuracy is unclear. Here, through systematic contrast-detail analysis, the fidelity of IG-NIRS in quantifying HbT was examined using 3D simulations. These simulations show that HbT could be recovered for medium sized (20mm in 100mm total diameter) spherical inclusions with an average error of 15%, for the physiologically relevant situation of 2:1 or higher contrast between background and inclusion. Using partial 3D volume meshes to reduce the ill-posed nature of the image reconstruction, inclusions as small as 14mm could be accurately quantified with less than 15% error, for contrasts of 1.5 or higher. This suggests that 3D IG-NIRS provides quantitatively accurate results for sizes seen early in treatment cycle of patients undergoing neoadjuvant chemotherapy when the tumors are larger than 30mm

Uniqueness, Born Approximation, and Numerical Methods for Diffuse Optical Tomography

Diffuse optical tomogrpahy (DOT) is to find optical coefficients of tissue using near infrared light. DOT as an inverse problem is described and the studies about unique determination of optical coefficients are summarized. If a priori information of the optical coefficient is known, DOT is reformulated to find a perturbation of the optical coefficients inverting the Born expansion which is an infinite series expansion with respect to the perturbation and the a priori information. Numerical methods for DOT are explained as methods inverting first- or second-order Born approximation or the Born expansion itself

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

Transformer Meets Boundary Value Inverse Problems

A Transformer-based deep direct sampling method is proposed for a class of boundary value inverse problems. A real-time reconstruction is achieved by evaluating the learned inverse operator between carefully designed data and the reconstructed images. An effort is made to give a specific example to a fundamental question: whether and how one can benefit from the theoretical structure of a mathematical problem to develop task-oriented and structure-conforming deep neural networks? Specifically, inspired by direct sampling methods for inverse problems, the 1D boundary data in different frequencies are preprocessed by a partial differential equation-based feature map to yield 2D harmonic extensions as different input channels. Then, by introducing learnable non-local kernels, the direct sampling is recast to a modified attention mechanism. The proposed method is then applied to electrical impedance tomography, a well-known severely ill-posed nonlinear inverse problem. The new method achieves superior accuracy over its predecessors and contemporary operator learners, as well as shows robustness with respect to noise. This research shall strengthen the insights that the attention mechanism, despite being invented for natural language processing tasks, offers great flexibility to be modified in conformity with the a priori mathematical knowledge, which ultimately leads to the design of more physics-compatible neural architectures