Department of Radiology, Stony Brook University, Stony Brook, New York, USA.
Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA.
Magn Reson Med. 2018 Aug;80(2):736-747. doi: 10.1002/mrm.27058. Epub 2018 Jan 7.
To evaluate the role of true diffusion and flow-related pseudodiffusion in cerebral blood flow (CBF) quantification using arterial spin labeling (ASL) with single-shot or segmented 3D gradient and spin echo (GRASE) readouts.
The extended phase graph (EPG) algorithm, originally designed to model the effects of T /T relaxation and true diffusion in MRI acquisitions utilizing multiple refocusing RF pulses, was augmented (aEPG). This augmentation accounted for flow-related pseudodiffusion attenuation of intravascular MRI signal in the k-space domain during 3D-GRASE acquisition, which leads to blur along the partition direction in the image domain. Blurring of ASL signal into neighboring voxels can lead to underestimation of CBF in small, high-flow structures such as cortical gray matter (GM).
The diffusion sensitivity of 3D-GRASE was evaluated through aEPG simulations and in vivo experiments in 13 healthy subjects. The CBF estimation bias for different postlabeling delays, crusher gradient strengths, and segmentation factors along the partition (PAR) and phase-encoding (PE) directions was numerically assessed by simulations and experimentally validated.
In vivo experiments demonstrated systematic underestimation of mean GM CBF measured with segmented 3D-GRASE. The GM CBF underestimation depended on ASL preparation and imaging parameters; it reached up to 25% at low-segmentation schemes (1 × 2 ) but was considerably lower at high-segmentation schemes (4 × 2 or 8 × 2 ). Theoretical predictions showed that conventional T /T relaxation and true diffusion may account for at most ∼25% of GM CBF estimation bias, whereas the pseudodiffusion effect constituted the major fraction in a typical ASL experiment.
The pseudodiffusion effect leads to considerable estimation bias in ASL-based CBF quantification using 3D-GRASE readouts. This bias can be substantially reduced by increasing the segmentation factors. Magn Reson Med 80:736-747, 2018. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.
利用单次或分段 3D 梯度和自旋回波（GRASE）读出的动脉自旋标记（ASL）评估真扩散和与血流相关的伪扩散在脑血流（CBF）定量中的作用。
最初设计用于使用多个重聚焦 RF 脉冲对 MRI 采集中的 T/T 弛豫和真扩散的影响进行建模的扩展相位图（EPG）算法得到了扩充（aEPG）。该扩充考虑了 3D-GRASE 采集期间血管内 MRI 信号在 k 空间域中与血流相关的伪扩散衰减，这会导致在图像域中沿分区方向模糊。ASL 信号模糊到相邻体素会导致皮质灰质（GM）等小、高流量结构中的 CBF 低估。
通过 aEPG 模拟和 13 名健康受试者的体内实验评估了 3D-GRASE 的扩散敏感性。通过模拟和实验验证，数值评估了不同的标记后延迟、破碎机梯度强度以及沿分区（PAR）和相位编码（PE）方向的分段因子对 CBF 估计偏差的影响。
体内实验表明，分段 3D-GRASE 测量的 GM CBF 存在系统性低估。GM CBF 的低估取决于 ASL 准备和成像参数；在低分段方案（1×2）下可达 25%，但在高分段方案（4×2 或 8×2）下则低得多。理论预测表明，传统的 T/T 弛豫和真扩散最多可能导致 GM CBF 估计偏差的 25%左右，而在典型的 ASL 实验中，伪扩散效应构成了主要部分。
3D-GRASE 读出的 ASL 基于 CBF 定量的伪扩散效应导致相当大的估计偏差。通过增加分段因子可以大大降低这种偏差。磁共振医学 80:736-747, 2018。本文是美国政府的工作，在美国属于公有领域。
