Data-driven optimization and evaluation of 2D EPI and 3D PRESTO for BOLD fMRI at 7 Tesla: I. Focal coverage

基于数据驱动的二维EPI和三维PRESTO序列在7特斯拉BOLD fMRI成像中的优化与评估:I. 焦点覆盖

阅读:1

Abstract

Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is commonly performed using 2D single-shot echo-planar imaging (EPI). However, single-shot EPI at 7 Tesla (T) often suffers from significant geometric distortions (due to low bandwidth (BW) in the phase-encode (PE) direction) and amplified physiological noise. Recent studies have suggested that 3D multi-shot sequences such as PRESTO may offer comparable BOLD contrast-to-noise ratio with increased volume coverage and decreased geometric distortions. Thus, a four-way group-level comparison was performed between 2D and 3D acquisition sequences at two in-plane resolutions. The quality of fMRI data was evaluated via metrics of prediction and reproducibility using NPAIRS (Non-parametric Prediction, Activation, Influence and Reproducibility re-Sampling). Group activation maps were optimized for each acquisition strategy by selecting the number of principal components that jointly maximized prediction and reproducibility, and showed good agreement in sensitivity and specificity for positive BOLD changes. High-resolution EPI exhibited the highest z-scores of the four acquisition sequences; however, it suffered from the lowest BW in the PE direction (resulting in the worst geometric distortions) and limited spatial coverage, and also caused some subject discomfort through peripheral nerve stimulation (PNS). In comparison, PRESTO also had high z-scores (higher than EPI for a matched in-plane resolution), the highest BW in the PE direction (producing images with superior geometric fidelity), the potential for whole-brain coverage, and no reported PNS. This study provides evidence to support the use of 3D multi-shot acquisition sequences in lieu of single-shot EPI for ultra high field BOLD fMRI at 7T.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。