Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.
IMPRS for Cognitive and Systems Neuroscience, Eberhard-Karls University of Tuebingen, Germany.
Magn Reson Med. 2018 Dec;80(6):2311-2325. doi: 10.1002/mrm.27225. Epub 2018 Apr 29.
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 H FID MRSI sequence at 9.4T.
X-t sparse compressed sensing reconstruction was optimized for nonlipid suppressed 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.
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.
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.
本研究旨在应用压缩感知技术，使用非脂质抑制的超短 TR 和 TE 高频磁共振波谱成像（ H FID MRSI）序列在 9.4T 下加速获取高分辨率人脑代谢物图谱。
针对非脂质抑制的 H FID MRSI 数据，优化了 X-t 稀疏压缩感知重建。比较了线圈间 X-t 稀疏重建与 SENSE x-t 稀疏和低秩重建。研究了矩阵大小和空间分辨率对可实现加速因子的影响。最后，采集并比较了不同加速因子（2、4、5 和 10）的体内代谢物图谱。
线圈间 X-t 稀疏压缩感知重建无法可靠地恢复非脂质抑制数据，而是需要并行和稀疏重建的组合（SENSE x-t 稀疏）。对于高达 5 的加速因子，低秩和压缩感知方法都能够很好地重建数据（Cre 的均方根误差 [RMSE]≤10.5%）。然而，低秩算法的重建时间要长得多。使用优化的压缩感知重建，对于 64×64 矩阵大小的 MRSI 数据，可以达到 4 或 5 的加速因子。对于较低的空间分辨率，可以成功实现高达 R∼4 的加速因子。
通过针对非脂质抑制数据进行参数优化和性能评估来定制重建方案，我们在 9.4T 下获得了高分辨率（97µL 体素大小）的人脑代谢物图谱，扫描时间为 3 到 3.75 分钟。
