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

• URL: http://arxiv.org/abs/2103.13909v1
• Date: Thu, 25 Mar 2021 15:20:10 GMT
• Title: Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition
• Authors: Alessandro Perelli, Martin S. Andersen
• Abstract summary: We propose to solve 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. We show numerical and experimental results for spectral CT materials decomposition.
• Score: 78.37855832568569
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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