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Fast Compressed Sensing of 3D Radial T1 Mapping with Different Sparse and Low-Rank Models

Authors :
Antti Paajanen
Matti Hanhela
Nina Hänninen
Olli Nykänen
Ville Kolehmainen
Mikko J. Nissi
Source :
Journal of Imaging, Vol 9, Iss 8, p 151 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

Knowledge of the relative performance of the well-known sparse and low-rank compressed sensing models with 3D radial quantitative magnetic resonance imaging acquisitions is limited. We use 3D radial T1 relaxation time mapping data to compare the total variation, low-rank, and Huber penalty function approaches to regularization to provide insights into the relative performance of these image reconstruction models. Simulation and ex vivo specimen data were used to determine the best compressed sensing model as measured by normalized root mean squared error and structural similarity index. The large-scale compressed sensing models were solved by combining a GPU implementation of a preconditioned primal-dual proximal splitting algorithm to provide high-quality T1 maps within a feasible computation time. The model combining spatial total variation and locally low-rank regularization yielded the best performance, followed closely by the model combining spatial and contrast dimension total variation. Computation times ranged from 2 to 113 min, with the low-rank approaches taking the most time. The differences between the compressed sensing models are not necessarily large, but the overall performance is heavily dependent on the imaged object.

Details

Language :
English
ISSN :
2313433X
Volume :
9
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Journal of Imaging
Publication Type :
Academic Journal
Accession number :
edsdoj.6c06fe664f04efcb1dcb14fbeb52346
Document Type :
article
Full Text :
https://doi.org/10.3390/jimaging9080151