Dimensionality Reduction Based Optimization Algorithm for Sparse 3-D Image Reconstruction in Diffuse Optical Tomography

Diffuse optical tomography (DOT) is a relatively low cost and portable imaging modality for reconstruction of optical properties in a highly scattering medium, such as human tissue. The inverse problem in DOT is highly ill-posed, making reconstruction of high-quality image a critical challenge. Beca...

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Bibliographic Details
Published in:Scientific reports 2016-03, Vol.6 (1), p.22242-22242, Article 22242
Main Authors: Bhowmik, Tanmoy, Liu, Hanli, Ye, Zhou, Oraintara, Soontorn
Format: Article
Language:English
Subjects:
631/114/1564
631/1647/245/2226
639/166/985
639/166/987
Algorithms
Brain Diseases - diagnostic imaging
Breast Neoplasms - diagnostic imaging
Female
Humanities and Social Sciences
Humans
Image Interpretation, Computer-Assisted
Image processing
Imaging, Three-Dimensional
Male
Medical imaging
Models, Theoretical
multidisciplinary
Optical properties
Prostatic Neoplasms - diagnostic imaging
Science
Sparsity
Tomography
Tomography, Optical - methods
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Summary:Diffuse optical tomography (DOT) is a relatively low cost and portable imaging modality for reconstruction of optical properties in a highly scattering medium, such as human tissue. The inverse problem in DOT is highly ill-posed, making reconstruction of high-quality image a critical challenge. Because of the nature of sparsity in DOT, sparsity regularization has been utilized to achieve high-quality DOT reconstruction. However, conventional approaches using sparse optimization are computationally expensive and have no selection criteria to optimize the regularization parameter. In this paper, a novel algorithm, Dimensionality Reduction based Optimization for DOT (DRO-DOT), is proposed. It reduces the dimensionality of the inverse DOT problem by reducing the number of unknowns in two steps and thereby makes the overall process fast. First, it constructs a low resolution voxel basis based on the sensing-matrix properties to find an image support. Second, it reconstructs the sparse image inside this support. To compensate for the reduced sensitivity with increasing depth, depth compensation is incorporated in DRO-DOT. An efficient method to optimally select the regularization parameter is proposed for obtaining a high-quality DOT image. DRO-DOT is also able to reconstruct high-resolution images even with a limited number of optodes in a spatially limited imaging set-up.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep22242