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

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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 …
Original languageEnglish
Title of host publicationMachine Learning for Medical Image Reconstruction
Subtitle of host publicationSecond International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings
EditorsF. Knoll, A. Maier, D. Rueckert, J. Ye
Place of PublicationCham
PublisherSpringer
Pages71-79
ISBN (Print)978-3-030-33842-8
DOIs
Publication statusPublished - 2019
Event2019 International Workshop on Machine Learning for Medical Image Reconstruction - Shenzen, China
Duration: 17 Oct 2019 → …

Publication series

NameLecture Notes in Computer Science
Volume11905

Conference

Conference2019 International Workshop on Machine Learning for Medical Image Reconstruction
Abbreviated titleMLMIR 2019
CountryChina
CityShenzen
Period17/10/19 → …

Fingerprint Dive into the research topics of 'Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions'. Together they form a unique fingerprint.

  • Cite this

    Johnson, P. M., Muckley, M. J., Bruno, M., Kobler, E., Hammernik, K., Pock, T., & Knoll, F. (2019). Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. In F. Knoll, A. Maier, D. Rueckert, & J. Ye (Eds.), Machine Learning for Medical Image Reconstruction: Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings (pp. 71-79). (Lecture Notes in Computer Science; Vol. 11905). Cham: Springer. https://doi.org/10.1007/978-3-030-33843-5_7