Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions

Patricia M Johnson, Matthew J Muckley, Mary Bruno, Erich Kobler, Kerstin Hammernik, Thomas Pock, Florian Knoll

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

Magnetic resonance imaging is a leading image modality for many clinical applications; however, a significant drawback is the lengthy data acquisition. This motivates the development of methods for reconstruction of sparsely sampled image data. One such technique is the Variational Network (VN), a machine learning method that generalizes traditional iterative reconstruction techniques, learning the regularization term from large amounts of image data. Previously, with the VN technique, reconstruction of 4-fold accelerated knee images was shown to be highly successful. In this work we extend the VN approach to applications beyond knee imaging and evaluate the classic VN and a newly developed Unet-VN in 5 different anatomical regions. We evaluate the networks trained individually for each anatomical area as well as jointly trained with data from all anatomical areas. The VN and Unet-VN were …
Originalspracheenglisch
TitelMachine Learning for Medical Image Reconstruction
UntertitelSecond International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings
Redakteure/-innenF. Knoll, A. Maier, D. Rueckert, J. Ye
ErscheinungsortCham
Herausgeber (Verlag)Springer
Seiten71-79
ISBN (Print)978-3-030-33842-8
DOIs
PublikationsstatusVeröffentlicht - 2019
Veranstaltung2019 International Workshop on Machine Learning for Medical Image Reconstruction - Shenzen, China
Dauer: 17 Okt 2019 → …

Publikationsreihe

NameLecture Notes in Computer Science
Band11905

Konferenz

Konferenz2019 International Workshop on Machine Learning for Medical Image Reconstruction
KurztitelMLMIR 2019
Land/GebietChina
OrtShenzen
Zeitraum17/10/19 → …

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