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Compressed sensing for high‐resolution nonlipid suppressed 1H FID MRSI of the human brain at 9.4T

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Nassirpour,  S
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group MR Spectroscopy and Ultra-High Field Methodology, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons192839

Chang,  P
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group MR Spectroscopy and Ultra-High Field Methodology, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons133464

Avdievitch,  N
Research Group MR Spectroscopy and Ultra-High Field Methodology, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84402

Henning,  A
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group MR Spectroscopy and Ultra-High Field Methodology, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Nassirpour, S., Chang, P., Avdievitch, N., & Henning, A. (2018). Compressed sensing for high‐resolution nonlipid suppressed 1H FID MRSI of the human brain at 9.4T. Magnetic Resonance in Medicine, 80(6), 2311-2325. doi:10.1002/mrm.27225.


Cite as: http://hdl.handle.net/21.11116/0000-0001-7CD0-9
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 1H FID MRSI sequence at 9.4T. Methods X‐t sparse compressed sensing reconstruction was optimized for nonlipid suppressed 1H 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.