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arXiv · 2103.13909

Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition

Alessandro Perelli·Martin S. Andersen
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Abstract

Spectral Computed Tomography (CT) is an emerging technology that enables to estimate the concentration of basis materials within a scanned object by exploiting different photon energy spectra. In this work, we aim at efficiently solving a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function using a randomized second order method. By approximating the Newton step using a sketching of the Hessian of the likelihood function, it is possible to reduce the complexity while retaining the complex prior structure given by the data-driven regularizer. We exploit a non-uniform block sub-sampling of the Hessian with inexact but efficient Conjugate gradient updates that require only Jacobian-vector products for denoising term. Finally, we show numerical and experimental results for spectral CT materials decomposition.

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Alessandro Perelli, Martin S. Andersen. 2021-03-25. Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition. https://doi.org/10.1098/rsta.2020.0191

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