Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, Michigan, USA.
Functional MRI Laboratory, University of Michigan, Ann Arbor, Michigan, USA.
Magn Reson Med. 2020 Jun;83(6):1979-1991. doi: 10.1002/mrm.28051. Epub 2019 Nov 21.
Arterial Spin Labeling (ASL) is a quantitative, non-invasive alternative for perfusion imaging that does not use contrast agents. The magnetic resonance fingerprinting (MRF) framework can be adapted to ASL to estimate multiple physiological parameters simultaneously. In this work, we introduce an optimization scheme to increase the sensitivity of the ASL fingerprint. We also propose a regression based estimation framework for MRF-ASL.
To improve the sensitivity of MRF-ASL signals to underlying parameters, we optimized ASL labeling durations using the Cramer-Rao Lower Bound (CRLB). This paper also proposes a neural network regression based estimation framework trained using noisy synthetic signals generated from our ASL signal model. We tested our methods in silico and in vivo, and compared with multiple post labeling delay (multi-PLD) ASL and unoptimized MRF-ASL. We present comparisons of estimated maps for the six parameters of our signal model.
The scan design process facilitated precise estimates of multiple hemodynamic parameters and tissue properties from a single scan, in regions of normal gray and white matter, as well as regions with anomalous perfusion activity in the brain. In particular, there was a 86.7% correlation of perfusion estimates with the ground truth in silico, using our proposed techniques. In vivo, there was roughly a 7 fold improvement in the Coefficient of Variation (CoV) for white matter perfusion, and 2 fold improvement in gray matter perfusion CoV in comparison to a reference Multi PLD method. The regression based estimation approach provided perfusion estimates rapidly, with estimation times of around 1s per map.
Scan design optimization, coupled with regression-based estimation is a powerful tool for improving precision in MRF-ASL.
动脉自旋标记（ASL）是一种定量、非侵入性的灌注成像替代方法，它不使用造影剂。磁共振指纹（MRF）框架可以适用于 ASL，以同时估计多个生理参数。在这项工作中，我们引入了一种优化方案来提高 ASL 指纹的灵敏度。我们还提出了一种基于回归的 MRF-ASL 估计框架。
为了提高 MRF-ASL 信号对潜在参数的灵敏度，我们使用克拉美罗下界（CRLB）优化了 ASL 标记持续时间。本文还提出了一种基于神经网络回归的估计框架，该框架使用从我们的 ASL 信号模型生成的噪声合成信号进行训练。我们在体内和体内进行了方法测试，并与多后标记延迟（multi-PLD）ASL 和未优化的 MRF-ASL 进行了比较。我们比较了我们信号模型的六个参数的估计图谱。
扫描设计过程有助于从单次扫描中精确估计多个血流动力学参数和组织特性，包括正常灰质和白质区域，以及大脑中异常灌注活动的区域。特别是，在体内，使用我们提出的技术，在与真实情况的相关性方面，灌注估计有 86.7%。与参考多 PLD 方法相比，白质灌注的变异系数（CoV）提高了约 7 倍，灰质灌注的 CoV 提高了 2 倍。基于回归的估计方法可以快速提供灌注估计值，每张图谱的估计时间约为 1 秒。
扫描设计优化与基于回归的估计相结合是提高 MRF-ASL 精度的有力工具。
