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"Deep Learning Based Self-Navigated Diffusion Weighted Multi-Shot EPI with Supervised Denoising"
Yiming Dong, Kirsten Koolstra, Laurens Beljaards, Marius Staring, Matthias J.P. van Osch and Peter Börnert
Abstract
Advanced diffusion weighted self-navigated multi-shot MRI can run at high scan efficiencies resulting in good image quality. However, the model-based image reconstruction used is rather time consuming. Deep learning-based reconstruction approaches could function as a faster alternative. Tailored network architectures with appropriately set physical model constraints can help to shorten reconstruction times, resulting in good image quality with reduced noise propagation.
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Copyright © 2023 by the authors.
Published version © 2023 by .
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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BibTeX entry
| @article{Dong:2023, |
| author |
= {Dong, Yiming and Koolstra, Kirsten and Beljaards, Laurens and Staring, Marius and van Osch, Matthias J.P. and Börnert, Peter}, |
| title |
= {Deep Learning Based Self-Navigated Diffusion Weighted Multi-Shot EPI with Supervised Denoising}, |
| journal |
= {International Society for Magnetic Resonance in Medicine}, |
| month |
= {June}, |
| year |
= {2023}, |
| } |
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| last modified: 23-08-2023 |
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