SUPER：一种用于加速 MR 参数映射的分块曲线拟合方法，结合快速重建。
SUPER: A blockwise curve-fitting method for accelerating MR parametric mapping with fast reconstruction.
机构信息
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut.
出版信息
Magn Reson Med. 2019 Jun;81(6):3515-3529. doi: 10.1002/mrm.27662. Epub 2019 Jan 17.
PURPOSE
To investigate Shift Undersampling improves Parametric mapping Efficiency and Resolution (SUPER), a novel blockwise curve-fitting method for accelerating parametric mapping with very fast reconstruction.
METHODS
SUPER uses interleaved k-space undersampling, which enables a blockwise decomposition of the otherwise large-scale cost function to improve the reconstruction efficiency. SUPER can be readily combined with SENSE to achieve at least 4-fold acceleration. D-factor, a parametric-mapping counterpart of g-factor, was proposed and formulated to compare spatially heterogeneous noise amplification because of different acceleration methods. As a proof-of-concept, SUPER/SUPER-SENSE was validated using T mapping, by comparing them to alternative model-based methods, including MARTINI and GRAPPATINI, via simulations, phantom imaging, and in vivo brain imaging (N = 5), over criteria of normalized root-mean-squares error (NRMSE), average d-factor, and computational time per voxel (TPV). A novel SUPER-SENSE MOLLI cardiac T -mapping sequence with improved resolution (1.4 mm × 1.4 mm) was compared to standard MOLLI (1.9 mm × 2.5 mm) in 8 healthy subjects.
RESULTS
In brain imaging, 2-fold SUPER achieved lower NRMSE (0.04 ± 0.02 vs. 0.11 ± 0.02, P < 0.01), lower average d-factor (1.01 ± 0.002 vs. 1.12 ± 0.004, P < 0.001), and lower TPV (4.6 ms ± 0.2 ms vs. 79 ms ± 3 ms, P < 0.001) than 2-fold MARTINI. Similarly, 4-fold SUPER-SENSE achieved lower NRMSE (0.07 ± 0.01 vs. 0.13 ± 0.03, P = 0.02), lower average d-factor (1.15 ± 0.01 vs. 1.20 ± 0.01, P < 0.001), and lower TPV (4.0 ms ± 0.1 ms vs. 72 ms ± 3 ms, P < 0.001) than 4-fold GRAPPATINI. In cardiac T mapping, SUPER-SENSE MOLLI yielded similar myocardial T (1151 ms ± 63 ms vs. 1159 ms ± 32 ms, P = 0.6), slightly lower blood T (1643 ms ± 86 ms vs. 1680 ms ± 79 ms, P = 0.004), but improved spatial resolution compared with standard MOLLI in the same imaging time.
CONCLUSION
SUPER and SUPER-SENSE provide fast model-based reconstruction methods for accelerating parametric mapping and improving its clinical appeal.
目的
研究 Shift Undersampling improves Parametric mapping Efficiency and Resolution（SUPER），这是一种新颖的分块曲线拟合方法，可用于加速具有非常快速重建的参数映射。
方法
SUPER 使用交错的欠采样，这使得能够对否则大规模的成本函数进行分块分解，从而提高重建效率。SUPER 可以很容易地与 SENSE 结合使用，以实现至少 4 倍的加速。D-因子，参数映射的 g-因子的对应物，被提出并制定，以比较由于不同加速方法而导致的空间异质噪声放大。作为概念验证，通过模拟、体模成像和体内脑成像（N = 5），通过比较替代基于模型的方法，包括 MARTINI 和 GRAPPATINI，来验证 SUPER/SUPER-SENSE 在归一化均方根误差（NRMSE）、平均 d-因子和每个体素的计算时间（TPV）标准的参数。在 8 名健康受试者中，使用改进分辨率（1.4mm×1.4mm）的新型 SUPER-SENSE MOLLI 心脏 T-映射序列与标准 MOLLI（1.9mm×2.5mm）进行了比较。
结果
在脑成像中，2 倍 SUPER 实现了更低的 NRMSE（0.04±0.02 与 0.11±0.02，P<0.01）、更低的平均 d-因子（1.01±0.002 与 1.12±0.004，P<0.001）和更低的 TPV（4.6ms±0.2ms 与 79ms±3ms，P<0.001），低于 2 倍的 MARTINI。同样，4 倍 SUPER-SENSE 实现了更低的 NRMSE（0.07±0.01 与 0.13±0.03，P=0.02）、更低的平均 d-因子（1.15±0.01 与 1.20±0.01，P<0.001）和更低的 TPV（4.0ms±0.1ms 与 72ms±3ms，P<0.001），低于 4 倍的 GRAPPATINI。在心脏 T 映射中，SUPER-SENSE MOLLI 产生了相似的心肌 T（1151ms±63ms 与 1159ms±32ms，P=0.6），稍低的血液 T（1643ms±86ms 与 1680ms±79ms，P=0.004），但与标准 MOLLI 相比，在相同的成像时间内提高了空间分辨率。
结论
SUPER 和 SUPER-SENSE 提供了基于模型的快速重建方法，用于加速参数映射并提高其临床吸引力。
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