Spatially regularized parametric map reconstruction for fast magnetic resonance fingerprinting.

Balsiger, Fabian; Jungo, Alain; Scheidegger, Olivier; Carlier, Pierre G; Reyes, Mauricio; Marty, Benjamin (2020). Spatially regularized parametric map reconstruction for fast magnetic resonance fingerprinting. Medical image analysis, 64(101741), p. 101741. Elsevier 10.1016/j.media.2020.101741

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Magnetic resonance fingerprinting (MRF) provides a unique concept for simultaneous and fast acquisition of multiple quantitative MR parameters. Despite acquisition efficiency, adoption of MRF into the clinics is hindered by its dictionary matching-based reconstruction, which is computationally demanding and lacks scalability. Here, we propose a convolutional neural network-based reconstruction, which enables both accurate and fast reconstruction of parametric maps, and is adaptable based on the needs of spatial regularization and the capacity for the reconstruction. We evaluated the method using MRF T1-FF, an MRF sequence for T1 relaxation time of water (T1H2O) and fat fraction (FF) mapping. We demonstrate the method's performance on a highly heterogeneous dataset consisting of 164 patients with various neuromuscular diseases imaged at thighs and legs. We empirically show the benefit of incorporating spatial regularization during the reconstruction and demonstrate that the method learns meaningful features from MR physics perspective. Further, we investigate the ability of the method to handle highly heterogeneous morphometric variations and its generalization to anatomical regions unseen during training. The obtained results outperform the state-of-the-art in deep learning-based MRF reconstruction. The method achieved normalized root mean squared errors of 0.048  ±  0.011 for T1H2O maps and 0.027  ±  0.004 for FF maps when compared to the dictionary matching in a test set of 50 patients. Coupled with fast MRF sequences, the proposed method has the potential of enabling multiparametric MR imaging in clinically feasible time.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Balsiger, Fabian; Jungo, Alain; Scheidegger, Olivier and Reyes, Mauricio

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

1361-8415

Publisher:

Elsevier

Language:

English

Submitter:

Chantal Kottler

Date Deposited:

09 Nov 2020 15:18

Last Modified:

15 Nov 2020 02:44

Publisher DOI:

10.1016/j.media.2020.101741

PubMed ID:

32544842

Uncontrolled Keywords:

Convolutional neural network Image reconstruction Magnetic resonance fingerprinting Quantitative magnetic resonance imaging

BORIS DOI:

10.7892/boris.147357

URI:

https://boris.unibe.ch/id/eprint/147357

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