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

36 38 57 5 108 document 57 SpectralL1.zip application/zip 4feab96bbbb7393c80facced75126813 MD5 28309491 2017-08-09 12:19:02 http://edata.bham.ac.uk/36/1/SpectralL1.zip 36 1 1 application/zip application/zip en public cc_by

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data http://eprints.org/relation/isVersionOf http://edata.bham.ac.uk/id/document/229732 http://eprints.org/relation/isVolatileVersionOf http://edata.bham.ac.uk/id/document/229732 http://eprints.org/relation/ispreviewThumbnailVersionOf http://edata.bham.ac.uk/id/document/229732 archive 4 disk0/00/00/00/36 2017-07-28 11:25:10 2021-07-21 15:51:58 2018-05-24 15:45:48 dataset show 0 Lu Wenqi Styles Iain I.B.Styles@bham.ac.uk L1 -norm Based Nonlinear Reconstruction Improves Quantitative Accuracy of Spectral Diffuse Optical Tomography submitted Q1 QA76 10col_ephy 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. 2017 submitted University of Birmingham 10.25500/eData.bham.00000036 Dataset research-data@contacts.bham.ac.uk School of Computer Science ec BITMAP Marie Sklodowska- Curie Innovative Training Network,grant agreement no 675332 na na na TRUE