Fast deep learning based reconstruction for limited angle tomography

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Bibliographic Details
Main Authors:	Salomonsson, Knut, Oldgren, Eric, Ström, Emanuel, Öktem, Ozan
Format:	Preprint
Published:	2024
Subjects:	Numerical Analysis
Online Access:	Acceder al recurso
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author	Salomonsson, Knut Oldgren, Eric Ström, Emanuel Öktem, Ozan
author_facet	Salomonsson, Knut Oldgren, Eric Ström, Emanuel Öktem, Ozan
contents	A major challenge in computed tomography is reconstructing objects from incomplete data. An increasingly popular solution for these problems is to incorporate deep learning models into reconstruction algorithms. This study introduces a novel approach by integrating a Fourier neural operator (FNO) into the Filtered Backprojection (FBP) reconstruction method, yielding the FNO back projection (FNO-BP) network. We employ moment conditions for sinogram extrapolation to assist the model in mitigating artefacts from limited data. Notably, our deep learning architecture maintains a runtime comparable to classical filtered back projection (FBP) reconstructions, ensuring swift performance during both inference and training. We assess our reconstruction method in the context of the Helsinki Tomography Challenge 2022 and also compare it against regular FBP methods.
format	Preprint
id	arxiv_https___arxiv_org_abs_2402_12141
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Fast deep learning based reconstruction for limited angle tomography Salomonsson, Knut Oldgren, Eric Ström, Emanuel Öktem, Ozan Numerical Analysis A major challenge in computed tomography is reconstructing objects from incomplete data. An increasingly popular solution for these problems is to incorporate deep learning models into reconstruction algorithms. This study introduces a novel approach by integrating a Fourier neural operator (FNO) into the Filtered Backprojection (FBP) reconstruction method, yielding the FNO back projection (FNO-BP) network. We employ moment conditions for sinogram extrapolation to assist the model in mitigating artefacts from limited data. Notably, our deep learning architecture maintains a runtime comparable to classical filtered back projection (FBP) reconstructions, ensuring swift performance during both inference and training. We assess our reconstruction method in the context of the Helsinki Tomography Challenge 2022 and also compare it against regular FBP methods.
title	Fast deep learning based reconstruction for limited angle tomography
topic	Numerical Analysis
url	https://arxiv.org/abs/2402.12141

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