A Data Self-Calibration Method Based on High-Density Parallel Plate Diffuse Optical Tomography for Breast Cancer Imaging

When performing the diffuse optical tomography (DOT) of the breast, the mismatch between the forward model and the experimental conditions will significantly hinder the reconstruction accuracy. Therefore, the reference measurement is commonly used to calibrate the measured data before the reconstruction. However, it is complicated to customize corresponding reference phantoms based on the breast shape and background optical parameters of different subjects in clinical trials. Furthermore, although high-density (HD) DOT configuration has been proven to improve imaging quality, a large number of source-detector (SD) pairs also increase the difficulty of multi-channel correction. To enhance the applicability of the breast DOT, a data self-calibration method based on an HD parallel-plate DOT system is proposed in this paper to replace the conventional relative measurement on a reference phantom. The reference predicted data can be constructed directly from the measurement data with the support of the HD-DOT system, which has nearly a hundred sets of measurements at each SD distance. The proposed scheme has been validated by Monte Carlo (MC) simulation, breast-size phantom experiments, and clinical trials, exhibiting the feasibility in ensuring the quality of the DOT reconstruction while effectively reducing the complexity associated with relative measurements on reference phantoms.

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Functional imaging of the developing brain with wearable high-density diffuse optical tomography: A new benchmark for infant neuroimaging outside the scanner environment

NeuroImage ◽

10.1016/j.neuroimage.2020.117490 ◽

2021 ◽

Vol 225 ◽

pp. 117490

Author(s):

Elisabetta Maria Frijia ◽

Addison Billing ◽

Sarah Lloyd-Fox ◽

Ernesto Vidal Rosas ◽

Liam Collins-Jones ◽

...

Keyword(s):

Functional Imaging ◽

Optical Tomography ◽

Diffuse Optical Tomography ◽

High Density ◽

Developing Brain

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Acceleration of the EM-like reconstruction method for diffuse optical tomography with ordered-subsets method

Journal of Inverse and Ill-Posed Problems ◽

10.1515/jip-2012-0064 ◽

2014 ◽

Vol 22 (3) ◽

Author(s):

Caifang Wang

Keyword(s):

Numerical Experiments ◽

Imaging Modality ◽

Random Medium ◽

Optical Tomography ◽

Back Propagation ◽

Three Dimensional ◽

Diffuse Optical Tomography ◽

Optical Parameters ◽

Reconstruction Method ◽

Boundary Measurements

Abstract.Diffuse optical tomography (DOT) is an optical imaging modality, which provides the spatial distribution of the optical parameters inside a random medium. A propagation back-propagation method named EM-like reconstruction method for stationary DOT problem has been proposed yet. This method is really time consuming. Hence the ordered-subsets (OS) technique for this reconstruction method is studied in this paper. The boundary measurements of DOT are grouped into nonoverlapping and overlapping ordered sequence of subsets with random partition, sequential partition and periodic partition, respectively. The performance of OS methods is compared with the standard EM-like reconstruction method with two-dimensional and three-dimensional numerical experiments. The numerical experiments indicate that reconstruction of nonoverlapping subsets with periodic partition, overlapping subsets with periodic partition and standard EM-like method provide very similar acceptable reconstruction results. However, reconstruction of nonoverlapping subsets with periodic partition spends a minimum of time to get proper results.

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Functional imaging of the human brain using a modular, fibre-less, high-density diffuse optical tomography system

Biomedical Optics Express ◽

10.1364/boe.7.004275 ◽

2016 ◽

Vol 7 (10) ◽

pp. 4275 ◽

Cited By ~ 35

Author(s):

Danial Chitnis ◽

Robert J. Cooper ◽

Laura Dempsey ◽

Samuel Powell ◽

Simone Quaggia ◽

...

Keyword(s):

Human Brain ◽

Functional Imaging ◽

Optical Tomography ◽

Diffuse Optical Tomography ◽

High Density ◽

Tomography System

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Portable, field-based neuroimaging using high-density diffuse optical tomography

NeuroImage ◽

10.1016/j.neuroimage.2020.116541 ◽

2020 ◽

Vol 215 ◽

pp. 116541 ◽

Cited By ~ 7

Author(s):

Andrew K. Fishell ◽

Ana María Arbeláez ◽

Claudia P. Valdés ◽

Tracy M. Burns-Yocum ◽

Arefeh Sherafati ◽

...

Keyword(s):

Optical Tomography ◽

Diffuse Optical Tomography ◽

High Density

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Image Reconstruction for Diffuse Optical Tomography Based on Radiative Transfer Equation

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/286161 ◽

2015 ◽

Vol 2015 ◽

pp. 1-23 ◽

Cited By ~ 8

Author(s):

Bo Bi ◽

Bo Han ◽

Weimin Han ◽

Jinping Tang ◽

Li Li

Keyword(s):

Image Reconstruction ◽

Radiative Transfer ◽

Transfer Equation ◽

Radiative Transfer Equation ◽

Optical Tomography ◽

Measurement Data ◽

Diffuse Optical Tomography ◽

Forward Model ◽

Sparsity Regularization ◽

Reconstruction Methods

Diffuse optical tomography is a novel molecular imaging technology for small animal studies. Most known reconstruction methods use the diffusion equation (DA) as forward model, although the validation of DA breaks down in certain situations. In this work, we use the radiative transfer equation as forward model which provides an accurate description of the light propagation within biological media and investigate the potential of sparsity constraints in solving the diffuse optical tomography inverse problem. The feasibility of the sparsity reconstruction approach is evaluated by boundary angular-averaged measurement data and internal angular-averaged measurement data. Simulation results demonstrate that in most of the test cases the reconstructions with sparsity regularization are both qualitatively and quantitatively more reliable than those with standardL2regularization. Results also show the competitive performance of the split Bregman algorithm for the DOT image reconstruction with sparsity regularization compared with other existingL1algorithms.

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