Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling
Year: 2017
Type: conference-paper
Abstract: , and spin density). Recently, MRF has been integrated with simultaneous multislice (SMS) acquisitions to enable volumetric imaging with faster scan time. In this paper, we present a new image reconstruction method based on low-rank and subspace modeling for improved SMS-MRF. Here the low-rank model exploits strong spatiotemporal correlation among contrast-weighted images, while the subspace model captures the temporal evolution of magnetization dynamics. With the proposed model, the image reconstruction problem is formulated as a convex optimization problem, for which we develop an algorithm based on variable splitting and the alternating direction method of multipliers. The performance of the proposed method has been evaluated by numerical experiments, and the results demonstrate that the proposed method leads to improved accuracy over the conventional approach. Practically, the proposed method has a potential to allow for a 3× speedup with minimal reconstruction error, resulting in less than 5 sec imaging time per slice.
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G1109774327, G1243485958, G1693937026, G3048631115, G3447810086, The Human Connectome Project (HCP), G5520316217, A Statistical Framework for Optimized Quantitative MRI with MR Fingerprinting, G787762306, G9000006430, G9000019951, G9000024958, G9000032172, G9000057962, G9000059029, G9000175730, G9000193951, G9000200654
OpenAlex ID: W2755267167