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Proceedings Paper

Online MR image reconstruction for compressed sensing acquisition in T2* imaging
Author(s): Loubna El Gueddari; Emilie Chouzenoux; Alexandre Vignaud; Jean-Christophe Pesquet; Philippe Ciuciu
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Paper Abstract

Reducing acquisition time is a major challenge in high-resolution MRI that has been successfully addressed by Compressed Sensing (CS) theory. While the scan time has been massively accelerated by a factor up to 20 in 2D imaging, the complexity of image recovery algorithms has strongly increased, resulting in slower reconstruction processes. In this work we propose an online approach to shorten image reconstruction times in the CS setting. We leverage the segmented acquisition in multiple shots of k-space data to interleave the MR acquisition and image reconstruction steps. This approach is particularly appealing for 2D high-resolution T2 -weighted anatomical imaging as the largest timing interval (i.e. Time of Repetition) between consecutive shots arises for this kind of imaging. During the scan, acquired shots are stacked together to form mini-batches and image reconstruction may start from incomplete data. For each newly available mini-batch, the previous partial solution is used as warm restart for the next sub-problem to be solved in a timing window compatible with the given TR and the number of shots stacked in a mini-batch. We demonstrate the interest and time savings of using online MR image reconstruction for Cartesian and non-Cartesian sampling strategies combined with a single receiver coil. Next, we extend the online formalism to address the more general multi-receiver phased array acquisition scenario. In this setting, calibrationless image reconstruction is adopted to remain compatible with the timing constraints of online delivery. Our retrospective and prospective results on ex-vivo 2D T2 -weighted brain imaging show that high-quality MR images are recovered by the end of acquisition for single receiver acquisition and that additional iterations are required when parallel imaging is adopted. Overall, our approach implemented through the Gadgetron framework may be compatible with the data workflow on the scanner to provide the physician with reliable MR images for diagnostic purposes.

Paper Details

Date Published: 9 September 2019
PDF: 15 pages
Proc. SPIE 11138, Wavelets and Sparsity XVIII, 1113819 (9 September 2019); doi: 10.1117/12.2527881
Show Author Affiliations
Loubna El Gueddari, CEA, NeuroSpin (France)
INRIA-CEA Saclay Ile-de-France, Univ. Paris-Saclay (France)
Emilie Chouzenoux, CVN, INRIA Saclay, Univ. Paris-Saclay (France)
Alexandre Vignaud, CEA, NeuroSpin (France)
Jean-Christophe Pesquet, CVN, INRIA Saclay, Univ. Paris-Saclay (France)
Philippe Ciuciu, CEA, NeuroSpin (France)
INRIA-CEA Saclay Ile-de-France, Univ. Paris-Saclay (France)


Published in SPIE Proceedings Vol. 11138:
Wavelets and Sparsity XVIII
Dimitri Van De Ville; Manos Papadakis; Yue M. Lu, Editor(s)

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