1. Optimizing full 3D SPARKLING trajectories for high-resolution T2*-weighted Magnetic Resonance Imaging
- Author
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R, Chaithya G, Weiss, Pierre, Daval-Fr��rot, Guillaume, Massire, Aur��lien, Vignaud, Alexandre, Ciuciu, Philippe, Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modèles et inférence pour les données de Neuroimagerie (MIND), IFR49 - Neurospin - CEA, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de Mathématiques de Toulouse UMR5219 (IMT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), PRIMO (ITAV), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut des Technologies Avancées en sciences du Vivant (ITAV), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Siemens Healthineers [Saint-Denis], Building large instruments for neuroimaging: from population imaging to ultra-high magnetic fields (BAOBAB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Chaithya Giliyar Radhakrishna was supported by the CEA NUMERICS PhD program, which received European funding from Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No800945.We acknowledge the financial support of the CrossDisciplinary Program on Numerical Simulation of CEA (SILICOSMIC project, PI: P. Ciuciu), the French Alternative Energies and Atomic Energy Commission. This work was granted access to the HPC resources of TGCC in France under the allocation 2019-GCH0424 made by GENCI. Pierre Weiss was supported by the ANR JCJC Optimization on Measures Spaces ANR-17-CE23-0013-01 and the ANR-3IA Artificial and Natural Intelligence Toulouse Institute., ANR-17-CE23-0013,OMS,Optimisation sur des espaces de mesures(2017), ANR-19-P3IA-0004,ANITI,Artificial and Natural Intelligence Toulouse Institute(2019), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut des Technologies Avancées en sciences du Vivant (ITAV), Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Unité Baobab (BAOBAB), and We acknowledge the financial support of the CrossDisciplinary Program on Numerical Simulation of CEA (SILICOSMIC project, PI: P. Ciuciu), the French Alternative Energies and Atomic Energy Commission. This work was granted access to the HPC resources of TGCC in France under the allocation 2019-GCH0424 made by GENCI. Pierre Weiss was supported by the ANR JCJC Optimization on Measures Spaces ANR-17-CE23-0013-01 and the ANR-3IA Artificial and Natural Intelligence Toulouse Institute.
- Subjects
FOS: Computer and information sciences ,3D MRI ,Computer Science - Distributed, Parallel, and Cluster Computing ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,non-Cartesian ,[INFO.INFO-IM]Computer Science [cs]/Medical Imaging ,Distributed, Parallel, and Cluster Computing (cs.DC) ,acceleration ,[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] ,[INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC] ,optimization ,compressed sensing - Abstract
International audience; The Spreading Projection Algorithm for Rapid K-space samplING, or SPARKLING, is an optimization-driven method that has been recently introduced for accelerated 2D T2*-w MRI using compressed sensing. It has then been extended to address 3D imaging using either stacks of 2D sampling patterns or a local 3D strategy that optimizes a single sampling trajectory at a time. 2D SPARKLING actually performs variable density sampling (VDS) along a prescribed target density while maximizing sampling efficiency and meeting the gradient-based hardware constraints. However, 3D SPARKLING has remained limited in terms of acceleration factors along the third dimension if one wants to preserve a peaky point spread function (PSF) and thus good image quality.In this paper, in order to achieve higher acceleration factors in 3D imaging while preserving image quality, we propose a new efficient algorithm that performs optimization on full 3D SPARKLING. The proposed implementation based on fast multipole methods (FMM) allows us to design sampling patterns with up to 10^7 k-space samples, thus opening the door to 3D VDS. We compare multi-CPU and GPU implementations and demonstrate that the latter is optimal for 3D imaging in the high-resolution acquisition regime (600µm isotropic). Finally, we show that this novel optimization for full 3D SPARKLING outperforms stacking strategies or 3D twisted projection imaging through retrospective and prospective studies on NIST phantom and in vivo brain scans at 3 Tesla. Overall the proposed method allows for 2.5-3.75x shorter scan times compared to GRAPPA-4 parallel imaging acquisition at 3 Tesla without compromising image quality.
- Published
- 2022