Reproducibility of arterial spin labeling cerebral blood flow image processing: A report of the ISMRM open science initiative for perfusion imaging (OSIPI) and the ASL MRI challenge.

PURPOSE: Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis. METHODS: Eight teams analyzed the challenge data, which included a high-resolution T1-weighted anatomical image and 10 pseudo-continuous ASL datasets simulated using a digital reference object to generate ground-truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra-class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches. RESULTS: Absolute errors in CBF estimates compared to ground-truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground-truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results. CONCLUSIONS: Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top-performing approaches as a step toward ASL standardization.

Three-dimensional gradient and spin-echo readout for time-encoded pseudo-continuous arterial spin labeling: Influence of segmentation factor and flow compensation.

PURPOSE: To monitor the complete passage of the labeled blood through the vascular tree into tissue and improve the quantification of ASL maps, we evaluated the effect of 3D gradient and spin-echo (GRASE) readout segments on temporal SNR (tSNR) and image blurriness for time-encoded pseudo-continuous arterial spin labeling and the effect of flow-compensation gradients on the presence of intravascular signal. METHODS: Fifteen volunteers were scanned using time-encoded pCASL with 2D EPI and single-segment, two-segments, and three-segments 3D-GRASE readouts with first-order flow compensation (FC) gradients. Two-segments 3D-GRASE scans were acquired with 25%, 50%, 75%, and 100% of full first-order FC. Temporal SNR was assessed, and cerebral blood flow and arterial blood volume were quantified for all readout strategies. RESULTS: For single-segment 3D GRASE, tSNR was comparable to 2D EPI for perfusion signal but worse for the arterial signal. Two-segments and three-segments 3D GRASE resulted in higher tSNR than 2D EPI for perfusion and arterial signal. The arterial signal was not well visualized for 3D-GRASE data without FC. Visualization of the intravascular signal at postlabeling delays of 660 ms and 1060 ms was restored with FC. Adequate visualization of the intravascular signal was achieved from 75% of FC gradient strength at a postlabeling delay of 660 ms. For a postlabeling delay of 1060 ms, full-FC gradients were the best option to depict intravascular signal. CONCLUSION: Segmented GRASE provided higher effective tSNR compared with 2D-EPI and single-segment GRASE. Flow compensation with GRASE readout should be carefully controlled when applying for time-encoded pCASL to visualize intravascular signal.

Intravoxel incoherent motion MRI in neurological and cerebrovascular diseases.

Intravoxel Incoherent Motion (IVIM) is a recently rediscovered noninvasive magnetic resonance imaging (MRI) method based on diffusion-weighted imaging. It enables the separation of the intravoxel signal into diffusion due to Brownian motion and perfusion-related contributions and provides important information on microperfusion in the tissue and therefore it is a promising tool for applications in neurological and neurovascular diseases. This review focuses on the basic principles and outputs of IVIM and details it major applications in the brain, such as stroke, tumor, and cerebral small vessel disease. A bi-exponential model that considers two different compartments, namely capillaries, and medium-sized vessels, has been frequently used for the description of the IVIM signal and may be important in those clinical applications cited before. Moreover, the combination of IVIM and arterial spin labeling MRI enables the estimation of water permeability across the blood-brain barrier (BBB), suggesting a potential imaging biomarker for disrupted-BBB diseases.