Spatio-temporal regularization for deep MR Fingerprinting

Mohammad Golbabaee, Dongdong Chen, Marion I. Menzel, Mike Davies, Pedro A. Gómez

Research output: Contribution to conferencePaper

Abstract

We study a deep learning approach to address the heavy storage and computation requirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprinting (MRF) reconstruction. The MRF-Net provides a piece-wise affine approximation to the (temporal) Bloch response manifold projection. Fed with non-iterated back-projected images, the network alone is unable to fully resolve spatially-correlated artefacts which appear in highly undersampling regimes. We propose an accelerated iterative reconstruction to minimize these artefacts before feeding into the network. This is done through a convex regularization that jointly promotes spatio-temporal regularities of the MRF time-series.
Original languageEnglish
Publication statusPublished - 10 Jul 2019
EventMedical Imaging with Deep Learning - London, UK United Kingdom
Duration: 8 Jul 201910 Jul 2019
https://openreview.net/group?id=MIDL.io/2019/Conference/Abstract

Conference

ConferenceMedical Imaging with Deep Learning
Abbreviated titleMIDL 2019
CountryUK United Kingdom
CityLondon
Period8/07/1910/07/19
Internet address

Cite this

Golbabaee, M., Chen, D., Menzel, M. I., Davies, M., & Gómez, P. A. (2019). Spatio-temporal regularization for deep MR Fingerprinting. Paper presented at Medical Imaging with Deep Learning, London, UK United Kingdom. https://openreview.net/pdf?id=ryx64UL6YE