Effects of motion and retrospective motion correction on the visualization and quantification of perivascular spaces in ultrahigh resolution T2‐weighted images at 7T

Abstract

Motion can strongly affect MRI image quality and derived imaging measures. We studied the effects of motion and retrospective motion correction (MC) on the visualization and quantitative measures of the perivascular space and penetrating vessel (PVSV) complex, an essential part of the glymphatic system, on highresolution T 2 -weighted MRI images at 7T. Methods: MC was achieved by adjusting k-space data based on head positions measured using fat navigator images. PVSV visibility and quantitative measures including diameter, volume fraction (VF), count, and contrast were compared between images with and without MC. Results: Without MC, VF, and count decreased significantly with increasing head rotation. MC improved PVSV visualization in all cases with severe motion artifacts. MC decreased diameter in white matter (WM) and increased VF, count, and contrast in basal ganglia and WM. The changes of VF, count, and contrast after MC strongly correlated with motion severity. MC eliminated the significant dependences of VF and count on rotation and reduced the inter-subject variations of VF and count. The effect sizes of age and breathing gas effects on VF and count, and contrast increased in most cases after MC, while those on diameter exhibited inconsistent behavior. Conclusions: Motion affects PVSV quantification without MC. MC improves PVSV visibility and increases the statistical power of detecting physiological PVSV VF, count, and contrast changes but may have limited benefits for increasing the power for detecting diameter changes.

Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast MRI at 7 T

“…A FatNav image 12 was acquired within each TR to monitor head motion. In Exp 1, the FatNav images (voxel size = 2.2 × 2.2 × 2.2 mm 3 , TE/TR/TA = 1.5 ms/3.1 ms/0.89 s, flip angle = 7°) were not utilized to correct motion in the present study, although they had been utilized to perform retrospective MC in a separate study 17 . In Exp 2, the FatNav images (voxel size = 3 × 3 × 3 mm 3 , TE/TR/TA = 1.31 ms/3 ms/0.47 s, flip angle = 7°) were used to measure motion parameters relative to the first FatNav image and perform prospective MC.…”

“…Details of FatNav image reconstruction has been described before, 17 where we applied the GRAPPA reconstruction algorithm 19 . The TSE images were reconstructed by the vendor provided software on the scanner.…”

Assessment of PVS Filtering Methods Using a Three-Dimensional Computational Model

“…visual ratings and manual counts). Nevertheless, motion correction can come at the expense of real PVS being removed (Bernal et al, 2021b) and, to the eyes of the experts, does not necessarily lead to better PVS visualisation (Zong et al, 2021). We therefore recommend careful utilisation of retrospective compensation strategies.…”

“…Retrospective motion correction has been explored to compensate for the detrimental effect of motion artefacts on PVS assessments (Bernal et al, 2021b(Bernal et al, , 2020Zong et al, 2021). These studies have demonstrated that retrospective motion correction successfully reduces dependencies between image quality and PVS measurements and can improve the agreement between computational and manual estimation (e.g.…”

“…Machine learning, convolutional neural networks specifically, has been utilised in recent years as an alternative to classical techniques leveraging explicit geometrical information (Boutinaud et al, 2021;Dubost et al, 2020Dubost et al, , 2019bDubost et al, , 2019aHuang et al, 2021;Jung et al, 2019;Lian et al, 2018;Yang et al, 2021;Zong et al, 2021). Its use could certainly be advantageous as these techniques can make use of a plethora of visual cues, including intensity, shape, and size, that would enable them to separate PVS from other lesions (Valdes Hernandez et al, 2013).…”