Brief review on learning-based methods for optical tomography

Abstract

Learning-based methods have been proved to perform well in a variety of areas in the biomedical field, such as biomedical image segmentation, and histopathological image analysis. Deep learning, as the most recently presented approach of learning-based methods, has attracted more and more attention. For instance, massive researches of deep learning methods for image reconstructions of computed tomography (CT) and magnetic resonance imaging (MRI) have been reported, indicating the great potential of deep learning for inverse problems. Optical technology-related medical imaging modalities including diffuse optical tomography (DOT), fluorescence molecular tomography (FMT), bioluminescence tomography (BLT), and photoacoustic tomography (PAT) are also dramatically innovated by introducing learning-based methods, in particular deep learning methods, to obtain better reconstruction results. This review depicts the latest researches on learning-based optical tomography of DOT, FMT, BLT, and PAT. According to the most recent studies, learning-based methods applied in the field of optical tomography are categorized as kernel-based methods and deep learning methods. In this review, the former are regarded as a sort of conventional learning-based methods and the latter are subdivided into model-based methods, post-processing methods, and end-to-end methods. Algorithm as well as data acquisition strategy are discussed in this review. The evaluations of these methods are summarized to illustrate the performance of deep learning-based reconstruction.

Multitask Deep Learning Reconstruction and Localization of Lesions in Limited Angle Diffuse Optical Tomography

“…The DOT image reconstruction is an ill-posed problem that has been tackled using a wide variety of reconstruction methods (see [7], [8] for a review).…”

A Learned-SVD approach for Regularization in Diffuse Optical Tomography

“…In [16] a Regularization by Denoising (RED) approach was proposed, while [17] trained a Neural Network for inverting the Lippman-Scwhinger equation, by firstly learning the pseudoinverse of the nonlinear mathematical operator modelling the DOT physics and then applying an encoder-autoencoder network to denoise. We refer to [18] for a review of other related techniques. Here we investigate and adapt the general strategy presented in [19], the so-called Learned SVD.…”

Objective Numerical Evaluation of Diffuse, Optically Reconstructed Images Using Structural Similarity Index

“…To overcome this issue, a structural similarity (SSIM) index was first introduced in 2004 to accord with the HVS by considering luminance, contrast, and structure calculation [ 12 ]. Since then, SSIM has become more popular in the field of image quality assessment (IQA), even in biomedical and clinical applications [ 15 , 16 , 17 , 18 , 19 ].…”