Abstract
PurposeA model‐based reconstruction framework is proposed for motion‐corrected and high‐resolution anatomically assisted (MOCHA) reconstruction of arterial spin labeling (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 blurring and partial volume effect.MethodsSix volunteers were recruited for CBF imaging using pseudo‐continuous ASL labeling, two‐shot 3D gradient and spin‐echo sequences and high‐resolution T1‐weighted MRI. For 2 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 hyperperfused lesion and hyperperfusion/hypoperfusion abnormalities. The MOCHA technique was compared with standard reconstruction and a 3D linear regression partial‐volume effect correction method and was further evaluated for acquisitions with reduced control‐label pairs and k‐space undersampling.ResultsThe MOCHA reconstructions of low‐resolution ASL data showed enhanced image quality, particularly in the partition direction. In simulations, both MOCHA and 3D linear regression provided more accurate CBF maps than the standard reconstruction; however, MOCHA resulted in the lowest errors and well delineated the abnormalities. The MOCHA reconstruction of standard‐resolution in vivo data showed good agreement with higher‐resolution scans requiring 4‐times and 9‐times longer acquisitions. The MOCHA reconstruction was found to be robust for 4‐times‐accelerated ASL acquisitions, achieved by reduced control‐label pairs or k‐space undersampling.ConclusionThe MOCHA reconstruction reduces partial‐volume effect by direct reconstruction of CBF maps in the high‐resolution space of the corresponding anatomical image, incorporating motion correction and point spread function modeling. Following further evaluation, MOCHA should promote the clinical application of ASL.
Highlights
Arterial spin labelling (ASL) is a non-invasive perfusion-weighted magnetic resonance imaging (MRI) technique for quantification of cerebral blood flow (CBF) (1), using magnetically labelled blood water as an endogenous contrast agent
Short post-label delay (PLD) are associated with less T1 decay and higher signal to noise ratio (SNR), too short PLDs may be insufficient for full arrival of labelled blood into the tissues leading to inaccurate CBF quantification
We propose a framework for reconstruction of low-resolution ASL data into the high-resolution space of the anatomical images, corrected for motion, PSF blurring and under-sampling artefacts, with additional noise reduction
Summary
Arterial spin labelling (ASL) is a non-invasive perfusion-weighted magnetic resonance imaging (MRI) technique for quantification of cerebral blood flow (CBF) (1), using magnetically labelled blood water as an endogenous contrast agent. In this technique, blood spins are typically labelled by inversion before flowing into the imaging volume, with pseudo-continuous ASL (PCASL) currently as the preferred method (1). ASL has an intrinsically low signal to noise ratio (SNR), as the volume of labelled blood is only ~1-2% of total cerebral blood volume (~4-5%) and the fact that the magnetic label decays by the T1 relaxation time of blood while it flows from labelling region to imaging volume. Short PLDs are associated with less T1 decay and higher SNR, too short PLDs may be insufficient for full arrival of labelled blood into the tissues leading to inaccurate CBF quantification
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
