Iterative regularization methods for a discrete inverse problem in MRI
A. Leitao, J. Zubelli
TL;DR
This paper develops and analyzes iterative regularization algorithms for solving inverse problems in MRI, extending convergence results to improve the reliability of MRI image reconstruction.
Contribution
It introduces a convergent iterative regularization method for MRI inverse problems, building on and extending previous convergence results for the Landweber-Kaczmarz method.
Findings
Proposes an efficient numerical method for MRI inverse problems.
Extends convergence theory for iterative regularization methods.
Demonstrates improved stability and convergence in MRI reconstructions.
Abstract
We propose and investigate efficient numerical methods for inverse problems related to Magnetic Resonance Imaging (MRI). Our goal is to extend the recent convergence results for the Landweber-Kaczmarz method obtained in [Haltmeier, Leitao, Scherzer 2007], in order to derive a convergent iterative regularization method for an inverse problem in MRI.
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Taxonomy
TopicsNumerical methods in inverse problems · Microwave Imaging and Scattering Analysis · Electrical and Bioimpedance Tomography