Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
School of Biomedical Engineering and Imaging Sciences, King's College London and King's College London & Guy's and St. Thomas' PET Centre, St. Thomas' Hospital, London, United Kingdom.
Magn Reson Med. 2020 Sep;84(3):1306-1320. doi: 10.1002/mrm.28205. Epub 2020 Mar 3.
A model-based reconstruction framework is proposed for motion-corrected and high-resolution anatomically assisted (MOCHA) reconstruction of arterial spin labeling (ASL) data. In this framework, all low-resolution ASL control-label pairs are used to reconstruct a single high-resolution cerebral blood flow (CBF) map, corrected for rigid-motion, point-spread-function blurring and partial volume effect.
Six volunteers were recruited for CBF imaging using pseudo-continuous ASL labeling, two-shot 3D gradient and spin-echo sequences and high-resolution T -weighted MRI. For 2 volunteers, high-resolution scans with double and triple resolution in the partition direction were additionally collected. Simulations were designed for evaluations against a high-resolution ground-truth CBF map, including a simulated hyperperfused lesion and hyperperfusion/hypoperfusion abnormalities. The MOCHA technique was compared with standard reconstruction and a 3D linear regression partial-volume effect correction method and was further evaluated for acquisitions with reduced control-label pairs and k-space undersampling.
The MOCHA reconstructions of low-resolution ASL data showed enhanced image quality, particularly in the partition direction. In simulations, both MOCHA and 3D linear regression provided more accurate CBF maps than the standard reconstruction; however, MOCHA resulted in the lowest errors and well delineated the abnormalities. The MOCHA reconstruction of standard-resolution in vivo data showed good agreement with higher-resolution scans requiring 4-times and 9-times longer acquisitions. The MOCHA reconstruction was found to be robust for 4-times-accelerated ASL acquisitions, achieved by reduced control-label pairs or k-space undersampling.
The MOCHA reconstruction reduces partial-volume effect by direct reconstruction of CBF maps in the high-resolution space of the corresponding anatomical image, incorporating motion correction and point spread function modeling. Following further evaluation, MOCHA should promote the clinical application of ASL.
提出了一种基于模型的重建框架，用于运动校正和高分辨率解剖辅助（MOCHA）动脉自旋标记（ASL）数据的重建。在这个框架中，所有低分辨率 ASL 控制-标记对都用于重建单个高分辨率脑血流（CBF）图，校正刚性运动、点扩散函数模糊和部分容积效应。
招募了 6 名志愿者进行使用伪连续 ASL 标记、双-shot 3D 梯度和自旋回波序列和高分辨率 T1 加权 MRI 的 CBF 成像。对于 2 名志愿者，还额外采集了分区方向上具有双分辨率和三分辨率的高分辨率扫描。设计了模拟实验，以评估与高分辨率真实 CBF 图的对比，包括模拟的高灌注病变和高灌注/低灌注异常。将 MOCHA 技术与标准重建和 3D 线性回归部分容积效应校正方法进行了比较，并进一步评估了减少控制-标记对和 k 空间欠采样的采集。
低分辨率 ASL 数据的 MOCHA 重建显示出增强的图像质量，特别是在分区方向上。在模拟中，MOCHA 和 3D 线性回归都比标准重建提供了更准确的 CBF 图，但 MOCHA 导致的误差最小，并且很好地描绘了异常。标准分辨率的体内数据的 MOCHA 重建与需要 4 倍和 9 倍更长采集时间的更高分辨率扫描显示出良好的一致性。发现 MOCHA 重建对 4 倍加速的 ASL 采集具有鲁棒性，通过减少控制-标记对或 k 空间欠采样实现。
MOCHA 重建通过在相应解剖图像的高分辨率空间中直接重建 CBF 图来减少部分容积效应，同时包括运动校正和点扩散函数建模。在进一步评估后，MOCHA 应该会促进 ASL 的临床应用。
