Article ; Online: Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction
Bioengineering, Vol 10, Iss 364, p
2023 Volume 364
Abstract: 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 ... ...
Abstract | 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. |
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Keywords | MRI reconstruction ; federated learning ; unrolled optimization ; accelerated MRI ; Technology ; T ; Biology (General) ; QH301-705.5 |
Subject code | 006 |
Language | English |
Publishing date | 2023-03-01T00:00:00Z |
Publisher | MDPI AG |
Document type | Article ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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