Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
Magn Reson Imaging. 2022 Oct;92:1-9. doi: 10.1016/j.mri.2022.05.016. Epub 2022 May 26.
In echo-planar diffusion-weighted imaging, correcting for susceptibility-induced artifacts typically requires acquiring pairs of images, known as blip-up blip-down acquisitions, to create an undistorted volume as a target to correct distortions that are often focal where regions with differences in magnetic susceptibility interface, such as the frontal and temporal areas. However, blip-up blip-down acquisitions are not always available, and distortion effects may not be specifically localized to such areas, with subtle effects potentially extending throughout the brain. Here, we apply a deep learning technique to generate an undistorted volume to correct susceptibility-induced artifacts and demonstrate implications for image fidelity and diffusion-based inference outside of areas where high focal distortion is present.
To demonstrate differences due to susceptibility artifact correction, uncorrected baseline images were compared to identical images where correction was performed using an undistorted target volume produced by the deep learning tool "PreQual". Widespread geometric distortion was assessed visually by referencing diffusion-weighted images to T1-weighted images. Tract-based spatial statistics (TBSS) were utilized to perform whole brain analysis of fractional anisotropy (FA) values to assess differences between subject groups (depressed vs. non-depressed) via permutation-based, voxel-wise testing. Multivariate regression models were then used to contrast TBSS results between corrected and non-corrected diffusion images.
Susceptibility artifact correction resulted in visible, widespread improvement in image fidelity when referenced to T1-weighted images. TBSS results were dependent on susceptibility artifact correction with correction resulting in widespread structural alterations of the mean FA skeleton, changes in skeletal FA, and additional positive tests of significance of regression coefficients in subsequent regression models.
Our results indicated that EPI distortion effects are not purely focal, and that reducing distortion can result in significant differences in the interpretation of diffusion data, even in areas remote from high distortion.
在 磁共振扩散加权成像中，校正由于磁化率引起的伪影通常需要采集一对图像，称为“上点扰相-下点扰相”采集，以创建一个无失真的容积作为目标来校正失真，这些失真通常是聚焦的，在磁化率差异界面的区域，如额区和颞区。然而，“上点扰相-下点扰相”采集并不总是可用的，而且失真效应可能并不特定地局限于这些区域，细微的效应可能会延伸到整个大脑。在这里，我们应用一种深度学习技术来生成一个无失真的容积来校正磁化率引起的伪影，并展示其对图像保真度和扩散推理的影响，而不仅仅是在存在高焦点失真的区域。
为了演示由于磁化率伪影校正而导致的差异，将未校正的基线图像与使用深度学习工具“PreQual”生成的无失真目标容积进行校正的相同图像进行比较。通过参考扩散加权图像与 T1 加权图像，对广泛的几何失真进行视觉评估。利用基于体素的空间统计学（TBSS）对各向异性分数（FA）值进行全脑分析，通过基于置换的体素级检验，评估抑郁组与非抑郁组之间的差异。然后，使用多元回归模型对比校正和未校正扩散图像之间的 TBSS 结果。
当参考 T1 加权图像时，磁化率伪影校正导致图像保真度的显著改善。TBSS 结果依赖于磁化率伪影校正，校正导致平均 FA 骨架的广泛结构改变，FA 骨架的改变，以及在随后的回归模型中回归系数的显著性检验的附加阳性结果。
我们的结果表明，EPI 失真效应不是纯粹的焦点，减少失真会导致扩散数据解释的显著差异，即使在远离高失真的区域。
