Journal Article

PUBDB-2023-06365

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Convergent Data-driven Regularizations for CT Reconstruction

Kabri, S. (Corresponding author)DESY* ; Auras, A. ; Riccio, D. ; Bauermeister, H. ; Benning, M. ; Moeller, M.Extern* ; Burger, M.DESY*

2024
Springer Singapore Singapore

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Please use a persistent id in citations: doi:10.1007/s42967-023-00333-2  doi:10.3204/PUBDB-2023-06365

Abstract: The reconstruction of images from their corresponding noisy Radontransform is a typical example of an ill-posed linear inverse problemas arising in the application of computerized tomography (CT). Asthe (naive) solution does not depend on the measured data continu-ously, regularization is needed to re-establish a continuous dependence.In this work, we investigate simple, but yet still provably convergentapproaches to learning linear regularization methods from data. Morespecifically, we analyze two approaches: One generic linear regularizationthat learns how to manipulate the singular values of the linear oper-ator in an extension of our previous work, and one tailored approachin the Fourier domain that is specific to CT-reconstruction. We provethat such approaches become convergent regularization methods as wellas the fact that the reconstructions they provide are typically muchsmoother than the training data they were trained on. Finally, wecompare the spectral as well as the Fourier-based approaches for CT-reconstruction numerically, discuss their advantages and disadvantagesand investigate the effect of discretization errors at different resolutions

Note: Funding should be my DFG project BU 2327/19/-1 Grundlagen vollüberwachter Deep Learning Verfahren für Inverse ProblemePaper accepted, not yet published (written while SK and MB were still with FAU)

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Contributing Institute(s):

1. Computational Imaging (FS-CI)

Research Program(s):

1. 623 - Data Management and Analysis (POF4-623) (POF4-623)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2024

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; DEAL Springer ; Emerging Sources Citation Index ; IF < 5 ; JCR ; SCOPUS ; Web of Science Core Collection

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