Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America.
Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, United States of America.
Dev Cogn Neurosci. 2023 Apr;60:101234. doi: 10.1016/j.dcn.2023.101234. Epub 2023 Mar 24.
Functional MRI (fMRI) data acquired using echo-planar imaging (EPI) are highly distorted by magnetic field inhomogeneities. Distortion and differences in image contrast between EPI and T1-weighted and T2-weighted (T1w/T2w) images makes their alignment a challenge. Typically, field map data are used to correct EPI distortions. Alignments achieved with field maps can vary greatly and depends on the quality of field map data. However, many public datasets lack field map data entirely. Additionally, reliable field map data is often difficult to acquire in high-motion pediatric or developmental cohorts. To address this, we developed Synth, a software package for distortion correction and cross-modal image registration that does not require field map data. Synth combines information from T1w and T2w anatomical images to construct an idealized undistorted synthetic image with similar contrast properties to EPI data. This synthetic image acts as an effective reference for individual-specific distortion correction. Using pediatric (ABCD: Adolescent Brain Cognitive Development) and adult (MSC: Midnight Scan Club; HCP: Human Connectome Project) data, we demonstrate that Synth performs comparably to field map distortion correction approaches, and often outperforms them. Field map-less distortion correction with Synth allows accurate and precise registration of fMRI data with missing or corrupted field map information.
功能磁共振成像(fMRI)数据采用回波平面成像(EPI)获取,易受到磁场非均匀性的影响而产生扭曲。EPI 与 T1 加权像和 T2 加权像(T1w/T2w)之间的扭曲和图像对比度差异使得它们的配准成为一项挑战。通常,场图数据用于校正 EPI 扭曲。场图校正的配准效果差异很大,取决于场图数据的质量。然而,许多公共数据集完全缺乏场图数据。此外,在高运动性的儿科或发育队列中,可靠的场图数据往往难以获取。为了解决这个问题,我们开发了 Synth,这是一个用于失真校正和跨模态图像配准的软件包,不需要场图数据。Synth 结合了 T1w 和 T2w 解剖图像的信息,构建了一个具有类似对比度特性的理想化无扭曲的合成图像,以作为个体特定失真校正的有效参考。使用儿科(ABCD:青少年大脑认知发展)和成人(MSC:午夜扫描俱乐部;HCP:人类连接组计划)的数据,我们证明了 Synth 的性能与场图失真校正方法相当,并且通常表现优于它们。Synth 无需场图的失真校正可以实现具有缺失或损坏场图信息的 fMRI 数据的准确和精确配准。