Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Magn Reson Imaging. 2021 Jan;75:72-88. doi: 10.1016/j.mri.2020.09.019. Epub 2020 Sep 24.
To compare three anisotropic acquisition schemes and three compressed sensing (CS) approaches for accelerated tissue sodium concentration (TSC) quantification using Na MRI at 7 T.
Three anisotropic 3D-radial acquisition sequences were evaluated using simulations, phantom- and in vivo TSC measurements: An anisotropic density-adapted 3D-radial sequence (3DPR-C), a 3D acquisition-weighted density-adapted stack-of-stars sampling scheme (SOS) and a SOS approach with golden-ratio rotation (SOS-GR). Eight healthy volunteers were examined at a 7 Tesla MRI system. TSC measurements of the calf were conducted with a nominal spatial resolution of Δx = (3.0 × 3.0 × 15.0) mm and a field of view of (156.0 × 156.0 × 240.0) mm for multiple undersampling factors (USF). Three CS reconstructions were evaluated: Total variation CS (TV-CS), 3D dictionary-learning compressed sensing (3D-DLCS) and TV-CS with a block matching prior (TV-BL-CS). Results of the simulations and measurements were compared to a simulated ground truth (GT) or a fully sampled reference measurement (FS), respectively. The deviation of the mean TSC evaluated in multiple ROI (mE) and the normalized root-mean-squared error (NRMSE) for simulations were evaluated for CS and NUFFT reconstructions.
In simulations, the SOS-GR yielded the lowest NRMSE and mE (< 4%) with NUFFT for an acquisition time (TA) of less than 2 min. CS further improved the results. In simulations and measurements, the best TSC quantification results were obtained with 3D-DLCS and SOS-GR (lowest NRMSE, mE < 2.6% in simulations, mE < 10.7% for phantom measurements and mE < 6% in vivo) with an USF = 4.1 (TA < 2 min). TV-CS showed no or only slight improvements to NUFFT. The results of TV-BL-CS were similar to 3D-DLCS.
The TA for TSC measurements could be reduced to less than 2 min by using adapted sequences such as SOS-GR and CS reconstruction approaches such as 3D-DLCS or TV-BL-CS, while the quantitative accuracy stays comparable to a fully sampled NUFFT reconstruction (approx. 8 min TA). In future, the lower TA could improve clinical applicability of TSC measurements.
在 7T 磁共振上比较三种各向异性采集方案和三种压缩感知（CS）方法在组织钠浓度（TSC）定量中的应用。
使用仿真、体模和活体 TSC 测量对三种各向异性 3D 径向采集序列进行评估：一种各向异性密度自适应 3D 径向序列（3DPR-C）、一种 3D 采集加权密度自适应星堆采样方案（SOS）和一种具有黄金比例旋转的 SOS 方法（SOS-GR）。八名健康志愿者在 7T MRI 系统上进行检查。采用名义空间分辨率 Δx = (3.0×3.0×15.0)mm 和视场 (156.0×156.0×240.0)mm 的小腿 TSC 测量，用于多个欠采样因子（USF）。评估了三种 CS 重建方法：全变差 CS（TV-CS）、3D 字典学习压缩感知（3D-DLCS）和具有块匹配先验的 TV-CS（TV-BL-CS）。仿真和测量结果分别与模拟真实值（GT）或完全采样参考测量（FS）进行比较。对于 CS 和 NUFFT 重建，评估了仿真中多个 ROI（mE）的平均 TSC 偏差和归一化均方根误差（NRMSE）。
在仿真中，对于小于 2 分钟的采集时间（TA），SOS-GR 结合 NUFFT 可获得最低的 NRMSE 和 mE（<4%）。CS 进一步改善了结果。在仿真和测量中，使用 3D-DLCS 和 SOS-GR 获得了最佳的 TSC 定量结果（最低 NRMSE，仿真中<2.6%，体模测量中<10.7%，体内测量中<6%），USF 为 4.1（TA<2 分钟）。TV-CS 对 NUFFT 没有或只有轻微的改进。TV-BL-CS 的结果与 3D-DLCS 相似。
通过使用适应序列（如 SOS-GR）和 CS 重建方法（如 3D-DLCS 或 TV-BL-CS），TSC 测量的 TA 可减少到 2 分钟以内，而定量准确性与完全采样的 NUFFT 重建相当（大约 8 分钟 TA）。在未来，更低的 TA 可以提高 TSC 测量的临床适用性。
