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L1 -norm Based Nonlinear Reconstruction Improves Quantitative Accuracy of Spectral Diffuse Optical Tomography

Lu, Wenqi and Styles, iain (2017) L1 -norm Based Nonlinear Reconstruction Improves Quantitative Accuracy of Spectral Diffuse Optical Tomography. [Dataset] (Submitted)

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Abstract

Spectrally constrained diffuse optical tomography (SCDOT) is known to improve reconstruction in diffuse optical imaging: constraining the reconstruction by coupling the optical
properties across multiple wavelengths and suppressing artefacts in the resulting reconstructed images. In other work, L1-norm regularization has been shown to be able to improve certain types of image reconstruction problem as its sparsity-promoting properties render it robust against
noise and enable preservation of edges in images, but because the L1-norm is non-differentiable, it is not always simple to implement. In this work, we show how to incorporate L1 regularization into SCDOT. Three popular algorithms for L1 regularization are assessed for application in SCDOT: iteratively reweighted least square algorithm (IRLS), alternating directional method of
multipliers (ADMM) and fast iterative shrinkage-thresholding algorithm (FISTA). We introduce an objective procedure for determining the regularization parameter in these algorithms and compare their performance in two-dimensional and three-dimensional simulated experiments. Our results show that L1 regularization consistently outperforms Tikhonov regularization in this application, particularly in the presence of noise.

Type of Work:Dataset
School/Faculty:Colleges (2008 onwards) > College of Engineering & Physical Sciences
Department:School of Computer Science
Date:2017
Projects:BITMAP Marie Sklodowska- Curie Innovative Training Network,grant agreement no 675332
Subjects:Q Science > Q Science (General)
Q Science > QA Mathematics > QA76 Computer software
Funders:European Commission
ID Code:3029

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