Sahar Nassirpour1,2, Paul Chang1,2, Nikolai Avdievitch1,3, Anke Henning1,3. 1. Max Planck Institute for Biological Cybernetics, Tuebingen, Germany. 2. IMPRS for Cognitive and Systems Neuroscience, Eberhard-Karls University of Tuebingen, Germany. 3. Institute of Physics, Ernst-Moritz-Arndt University Greifswald, Greifswald, Germany.
Abstract
PURPOSE: The aim of this study was to apply compressed sensing to accelerate the acquisition of high resolution metabolite maps of the human brain using a nonlipid suppressed ultra-short TR and TE 1 H FID MRSI sequence at 9.4T. METHODS: X-t sparse compressed sensing reconstruction was optimized for nonlipid suppressed 1 H FID MRSI data. Coil-by-coil x-t sparse reconstruction was compared with SENSE x-t sparse and low rank reconstruction. The effect of matrix size and spatial resolution on the achievable acceleration factor was studied. Finally, in vivo metabolite maps with different acceleration factors of 2, 4, 5, and 10 were acquired and compared. RESULTS: Coil-by-coil x-t sparse compressed sensing reconstruction was not able to reliably recover the nonlipid suppressed data, rather a combination of parallel and sparse reconstruction was necessary (SENSE x-t sparse). For acceleration factors of up to 5, both the low-rank and the compressed sensing methods were able to reconstruct the data comparably well (root mean squared errors [RMSEs] ≤ 10.5% for Cre). However, the reconstruction time of the low rank algorithm was drastically longer than compressed sensing. Using the optimized compressed sensing reconstruction, acceleration factors of 4 or 5 could be reached for the MRSI data with a matrix size of 64 × 64. For lower spatial resolutions, an acceleration factor of up to R∼4 was successfully achieved. CONCLUSION: By tailoring the reconstruction scheme to the nonlipid suppressed data through parameter optimization and performance evaluation, we present high resolution (97 µL voxel size) accelerated in vivo metabolite maps of the human brain acquired at 9.4T within scan times of 3 to 3.75 min.
PURPOSE: The aim of this study was to apply compressed sensing to accelerate the acquisition of high resolution metabolite maps of the human brain using a nonlipid suppressed ultra-short TR and TE 1 H FID MRSI sequence at 9.4T. METHODS: X-t sparse compressed sensing reconstruction was optimized for nonlipid suppressed 1 H FID MRSI data. Coil-by-coil x-t sparse reconstruction was compared with SENSE x-t sparse and low rank reconstruction. The effect of matrix size and spatial resolution on the achievable acceleration factor was studied. Finally, in vivo metabolite maps with different acceleration factors of 2, 4, 5, and 10 were acquired and compared. RESULTS: Coil-by-coil x-t sparse compressed sensing reconstruction was not able to reliably recover the nonlipid suppressed data, rather a combination of parallel and sparse reconstruction was necessary (SENSE x-t sparse). For acceleration factors of up to 5, both the low-rank and the compressed sensing methods were able to reconstruct the data comparably well (root mean squared errors [RMSEs] ≤ 10.5% for Cre). However, the reconstruction time of the low rank algorithm was drastically longer than compressed sensing. Using the optimized compressed sensing reconstruction, acceleration factors of 4 or 5 could be reached for the MRSI data with a matrix size of 64 × 64. For lower spatial resolutions, an acceleration factor of up to R∼4 was successfully achieved. CONCLUSION: By tailoring the reconstruction scheme to the nonlipid suppressed data through parameter optimization and performance evaluation, we present high resolution (97 µL voxel size) accelerated in vivo metabolite maps of the human brain acquired at 9.4T within scan times of 3 to 3.75 min.
Authors: Andrew A Maudsley; Ovidiu C Andronesi; Peter B Barker; Alberto Bizzi; Wolfgang Bogner; Anke Henning; Sarah J Nelson; Stefan Posse; Dikoma C Shungu; Brian J Soher Journal: NMR Biomed Date: 2020-04-29 Impact factor: 4.044
Authors: Roland Kreis; Vincent Boer; In-Young Choi; Cristina Cudalbu; Robin A de Graaf; Charles Gasparovic; Arend Heerschap; Martin Krššák; Bernard Lanz; Andrew A Maudsley; Martin Meyerspeer; Jamie Near; Gülin Öz; Stefan Posse; Johannes Slotboom; Melissa Terpstra; Ivan Tkáč; Martin Wilson; Wolfgang Bogner Journal: NMR Biomed Date: 2020-08-17 Impact factor: 4.044
Authors: Alex A Bhogal; Tommy A A Broeders; Lisan Morsinkhof; Mirte Edens; Sahar Nassirpour; Paul Chang; Dennis W J Klomp; Christiaan H Vinkers; Jannie P Wijnen Journal: Brain Behav Date: 2020-11-20 Impact factor: 2.708