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Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction
Image reconstruction is the process of recovering an image from raw, under-sampled signal measurements, and is a critical step in diagnostic medical imaging, such as magnetic resonance imaging (MRI). Recently, data-driven methods have led to improved image quality in MRI reconstruction using a limit...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045102/ https://www.ncbi.nlm.nih.gov/pubmed/36978755 http://dx.doi.org/10.3390/bioengineering10030364 |
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author | Levac, Brett R. Arvinte, Marius Tamir, Jonathan I. |
author_facet | Levac, Brett R. Arvinte, Marius Tamir, Jonathan I. |
author_sort | Levac, Brett R. |
collection | PubMed |
description | Image reconstruction is the process of recovering an image from raw, under-sampled signal measurements, and is a critical step in diagnostic medical imaging, such as magnetic resonance imaging (MRI). Recently, data-driven methods have led to improved image quality in MRI reconstruction using a limited number of measurements, but these methods typically rely on the existence of a large, centralized database of fully sampled scans for training. In this work, we investigate federated learning for MRI reconstruction using end-to-end unrolled deep learning models as a means of training global models across multiple clients (data sites), while keeping individual scans local. We empirically identify a low-data regime across a large number of heterogeneous scans, where a small number of training samples per client are available and non-collaborative models lead to performance drops. In this regime, we investigate the performance of adaptive federated optimization algorithms as a function of client data distribution and communication budget. Experimental results show that adaptive optimization algorithms are well suited for the federated learning of unrolled models, even in a limited-data regime (50 slices per data site), and that client-sided personalization can improve reconstruction quality for clients that did not participate in training. |
format | Online Article Text |
id | pubmed-10045102 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100451022023-03-29 Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction Levac, Brett R. Arvinte, Marius Tamir, Jonathan I. Bioengineering (Basel) Article Image reconstruction is the process of recovering an image from raw, under-sampled signal measurements, and is a critical step in diagnostic medical imaging, such as magnetic resonance imaging (MRI). Recently, data-driven methods have led to improved image quality in MRI reconstruction using a limited number of measurements, but these methods typically rely on the existence of a large, centralized database of fully sampled scans for training. In this work, we investigate federated learning for MRI reconstruction using end-to-end unrolled deep learning models as a means of training global models across multiple clients (data sites), while keeping individual scans local. We empirically identify a low-data regime across a large number of heterogeneous scans, where a small number of training samples per client are available and non-collaborative models lead to performance drops. In this regime, we investigate the performance of adaptive federated optimization algorithms as a function of client data distribution and communication budget. Experimental results show that adaptive optimization algorithms are well suited for the federated learning of unrolled models, even in a limited-data regime (50 slices per data site), and that client-sided personalization can improve reconstruction quality for clients that did not participate in training. MDPI 2023-03-16 /pmc/articles/PMC10045102/ /pubmed/36978755 http://dx.doi.org/10.3390/bioengineering10030364 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Levac, Brett R. Arvinte, Marius Tamir, Jonathan I. Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction |
title | Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction |
title_full | Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction |
title_fullStr | Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction |
title_full_unstemmed | Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction |
title_short | Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction |
title_sort | federated end-to-end unrolled models for magnetic resonance image reconstruction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10045102/ https://www.ncbi.nlm.nih.gov/pubmed/36978755 http://dx.doi.org/10.3390/bioengineering10030364 |
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