@inproceedings{1f0d1d60837e4f219bdd4fe86fb16650,
title = "Deep Unrolling for Magnetic Resonance Fingerprinting",
abstract = "Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative MR imaging approach. Deep learning methods have been proposed for MRF and demonstrated improved performance over classical compressed sensing algorithms. However many of these end-to-end models are physics-free, while consistency of the predictions with respect to the physical forward model is crucial for reliably solving inverse problems. To address this, recently [1] proposed a proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within an unrolled learning mechanism. However, [1] only evaluated the unrolled model on synthetic data using Cartesian sampling trajectories. In this paper, as a complementary to [1], we investigate other choices of encoders to build the proximal neural network, and evaluate the deep unrolling algorithm on real accelerated MRF scans with non-Cartesian k-space sampling trajectories.",
keywords = "compressed sensing, Deep unrolling, magnetic resonance fingerprinting, quantitative MRI",
author = "Dongdong Chen and Davies, {Mike E.} and Mohammad Golbabaee",
note = "Funding Information: We thank GE Healthcare for providing the MRF dataset. DC and MD are supported by the ERC Advanced grant CSENSE, ERC-2015-AdG 694888. MD acknowledges support from his Royal Society Wolfson Research Merit Award. Funding Information: We thank GE Healthcare for providing the MRF dataset. DC and MD are supported by the ERC Advanced grant C-SENSE, ERC-2015-AdG 694888. MD acknowledges support from his Royal Society Wolfson Research Merit Award. ; 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 ; Conference date: 28-03-2022 Through 31-03-2022",
year = "2022",
month = apr,
day = "26",
doi = "10.1109/ISBI52829.2022.9761475",
language = "English",
isbn = "978-1-6654-2924-5",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE",
booktitle = "ISBI 2022 - Proceedings",
address = "USA United States",
}