Motion-corrected and high-resolution anatomically-assisted (MOCHA) reconstruction of arterial spin labelling MRI

TL;DR

This paper introduces MOCHA, a novel model-based reconstruction method for arterial spin labelling MRI that enhances image quality, corrects motion, and reduces partial-volume effects, enabling high-resolution cerebral blood flow mapping from low-resolution data.

Contribution

The paper presents MOCHA, a new reconstruction framework that integrates motion correction, PSF modeling, and anatomical assistance to improve ASL MRI resolution and accuracy.

Findings

MOCHA outperforms standard reconstruction in image quality and accuracy.

MOCHA provides robust results with reduced control-label pairs and undersampling.

In-vivo results show good agreement with high-resolution scans, enabling faster imaging.

Abstract

A model-based reconstruction framework is proposed for MOtion-Corrected and High-resolution anatomically-Assisted (MOCHA) reconstruction of 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 (PSF) blurring and partial-volume effect (PVE).Six volunteers were recruited for CBF imaging using PCASL labelling, 2-shot 3D-GRASE sequences and high-resolution T1-weighted MRI. For two 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 hyper-perfused lesion and hyper/hypo-perfusion abnormalities. MOCHA was compared to standard reconstruction and a 3D linear regression (3DLR) PVE correction method and was further evaluated for acquisitions with reduced control-label pairs and k-space undersampling. MOCHA reconstructions of low-resolution ASL data showed enhanced image quality particularly in the partition direction. In simulations, both MOCHA and 3DLR provided more accurate CBF maps than the standard reconstruction, however MOCHA resulted in the lowest errors and well delineated the abnormalities. MOCHA reconstruction of standard-resolution in-vivo data showed good agreement with higher-resolution scans requiring 4x and 9x longer acquisitions. MOCHA was found to be robust for 4x-accelerated ASL acquisitions, achieved by reduced control-label pairs or k-space undersampling. Conclusion: MOCHA reconstruction reduces PVE by direct reconstruction of CBF maps in the high-resolution space of the corresponding anatomical image, incorporating motion correction and PSF modelling. Following further evaluation, MOCHA should promote the clinical application of ASL.