A framework for motion correction of background suppressed arterial spin labeling perfusion images acquired with simultaneous multi-slice EPI

Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition, quantification, and clinical applications

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.

High temporal resolution arterial spin labeling MRI with whole-brain coverage by combining time-encoding with Look-Locker and simultaneous multi-slice imaging

PURPOSE

The goal of this study was to achieve high temporal resolution, multi-time point pseudo-continuous arterial spin labeling (pCASL) MRI in a time-efficient manner, while maintaining whole-brain coverage.

METHODS

A Hadamard 8-matrix was used to dynamically encode the pCASL labeling train, thereby providing the first source of temporal information. The second method for obtaining dynamic arterial spin labeling (ASL) signal consisted of a Look-Locker (LL) readout of 4 phases that are acquired with a flip-angle sweep to maintain constant sensitivity over the phases. To obtain whole-brain coverage in the short LL interval, 4 slices were excited simultaneously by multi-banded radiofrequency pulses. After subtraction according to the Hadamard scheme, the ASL signal was corrected for the use of the flip-angle sweep and background suppression pulses. The BASIL toolkit of the Oxford Centre for FMRIB was used to quantify the ASL signal.

RESULTS

By combining a time-encoded pCASL labeling scheme with an LL readout and simultaneous multi-slice acquisition, 28 time points of 16 slices with a 75- or 150-ms time resolution were acquired in a total scan time of 10 minutes 20 seconds, from which cerebral blood flow (CBF) maps, arterial transit time maps, and arterial blood volume could be determined.

CONCLUSION

Whole-brain ASL images were acquired with a 75-ms time resolution for the angiography and 150-ms resolution for the perfusion phase by combining the proposed techniques. Reducing the total scan time to 1 minute 18 seconds still resulted in reasonable CBF maps, which demonstrates the feasibility of this approach for practical studies on brain hemodynamics.

A framework for motion correction of background suppressed arterial spin labeling perfusion images acquired with simultaneous multi-slice EPI

PURPOSE

When using simultaneous multi-slice (SMS) EPI for background suppressed (BGS) arterial spin labeling (ASL), correction of through-plane motion could introduce artefacts, because the slices with most effective BGS are adjacent to slices with the least BGS. In this study, a new framework is presented to correct for such artefacts.

METHODS

The proposed framework consists of 3 steps: (1) homogenization of the static tissue signal over the different slices to eliminate most inter-slice differences because of different levels of BGS, (2) application of motion correction, and (3) extraction of a perfusion-weighted signal using a general linear model. The proposed framework was evaluated by simulations and a functional ASL study with intentional head motion.

RESULTS

Simulation studies demonstrated that the strong signal differences between slices with the most and least effective BGS caused sub-optimal estimation of motion parameters when through-plane motion was present. Although use of the M0 image as the reference for registration allowed 82% improvement of motion estimation for through-plane motion, it still led to residual subtraction errors caused by different static tissue signal between control and label because of different BGS levels. By using our proposed framework, those problems were minimized, and the accuracy of CBF estimation was improved. Moreover, the functional ASL study showed improved detection of visual and motor activation when applying the framework as compared to conventional motion correction, as well as when motion correction was completely omitted.

CONCLUSION

When combining BGS-ASL with SMS-EPI, particular attention is needed to avoid artefacts introduced by motion correction. With the proposed framework, these issues are minimized.