High-resolution diffusion-weighted imaging at 7 Tesla: single-shot readout trajectories and their impact on signal-to-noise ratio, spatial resolution and accuracy
Sajjad Feizollah (1, 2), Christine L. Tardif (1, 2, 3) ((1), Department of Neurology, Neurosurgery, Faculty of Medicine, Health, Sciences, McGill University, Montreal, Canada, (2) McConnell Brain Imaging, Centre, Montreal Neurological Institute, McGill University, Montreal

TL;DR
This study compares different k-space trajectories for high-resolution diffusion MRI at 7 Tesla, analyzing their impact on signal-to-noise ratio, spatial resolution, and image accuracy to optimize ultra-high field imaging.
Contribution
It provides a comprehensive evaluation of EPI, partial Fourier EPI, and spiral trajectories at 7T, highlighting their trade-offs in resolution, SNR, and image quality for diffusion MRI.
Findings
EPI offers highest specificity and resolution but lower SNR.
Spirals provide higher SNR but lower specificity.
Spiral trajectories enable higher effective resolution at UHF due to increased SNR.
Abstract
Diffusion MRI (dMRI) is a valuable imaging technique to study the brain in vivo. However, the resolution of dMRI is limited by the low signal-to-noise ratio (SNR) of this technique. Various acquisition strategies have been developed to achieve high resolutions, but they require long scan times. Imaging at ultra-high fields (UHF) could further increase the SNR of single-shot dMRI; however, the shorter T2* and the greater field non-uniformities will degrade image quality. In this study, we investigated the trade-off between the SNR and resolution of different k-space trajectories, including echo planar imaging (EPI), partial Fourier EPI, and spiral, over a range of resolutions at 7T. The effective resolution, spatial specificity and sharpening effect were measured from the point spread function (PSF) of the simulated diffusion sequences for a nominal resolution range of 0.6-1.8 mm.…
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Taxonomy
TopicsAdvanced Neuroimaging Techniques and Applications · Advanced MRI Techniques and Applications · MRI in cancer diagnosis
