Structured Sparsity Regularization for online MR Image Reconstruction in Accelerated T2* Imaging

Philippe Ciuciu (CEA/NeuroSpin & Inria Saclay Île-de-France Parietal)
October 09, 2020 — 11:00 — Online

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, 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 of anatomical MR data in multiple shots to interleave the MR acquisition and image reconstruction steps. This approach is particularly appealing for 2D high-resolution T2*-weighted anatomical imaging. During the scan, acquired shots are stacked together to form mini-batches and image reconstruction may start from incomplete data. We demonstrate the interest and time savings of this online image reconstruction framework for Cartesian and non-Cartesian sampling strategies combined with a single receiver coil. Next, we further extend this formalism to address the more challenging case of multi-receiver phased array acquisition. In this setting, calibrationless image reconstruction leverages structured sparsity regularization to remain compatible with the timing constraints of online image delivery. Our results on ex-vivo 2D T2*-weighted brain images show that high-quality MR images are recovered by the end of acquisition in both acquisition setups.

References

[1] El Gueddari L, Ciuciu P, Chouzenoux E, Vignaud A, Pesquet JC. Calibrationless OSCAR-based image reconstruction in compressed sensing parallel MRI. In2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Apr 8, 2019 (pp. 1532-1536). IEEE.

[2] El Gueddari L, Chouzenoux E, Vignaud A, Pesquet JC, Ciuciu P. Online MR image reconstruction for compressed sensing acquisition in T2* imaging. InWavelets and Sparsity XVIII Sep 9, 2019 (Vol. 11138, p. 1113819). International Society for Optics and Photonics.

[3] El Gueddari L, Chouzenoux E, Vignaud A, Ciuciu P. Calibration-less parallel imaging compressed sensing reconstruction based on OSCAR regularization. https://hal.inria.fr/hal-02292372/document

Biography

Dr. Philippe Ciuciu obtained his PhD in electrical engineering from the University of Paris-Sud in 2000 and his Habilitation degree in 2008. Dr. Ciuciu is now CEA Research Director at NeuroSpin where he has led, since 2018, the Compressed Sensing group in the Inria-CEA Parietal team at NeuroSpin. Dr. Ciuciu’s work has led to more than 200 research outputs including more than 50 peer-reviewed articles in international journals such as SIAM Imaging Sciences, IEEE Trans. on Signal Processing, IEEE Trans. on Medical Imaging, Medical Image Analysis, NeuroImage, Magnetic Resonance in Medicine, etc. He also holds 2 MRI-related patents. His current research interests are in developing accelerated acquisition and image reconstruction techniques, including deep learning techniques, for magnetic resonance imaging (MRI) with applications in clinical and cognitive neuroscience at 3 and 7 Tesla. As IEEE Senior Member, he has represented the IEEE Signal Processing Society in the International Symposium on Biomedical Imaging for the 2019-2020 period. He has also been appointed to take part to the steering committee of the 2021 ESMRMB conference in Barcelona. Since 2019 he holds a position as Senior Area Editor for the IEEE open Journal of Signal Processing and that of Vice Chair for the Biomedical Image and Signal Analytics (BISA) technical committee of the EURASIP society. In 2020, he has been appointed as Associate Editor to Frontiers in Neuroscience, section Brain imaging methods.