用神经根束精确定位脊髓,提升功能磁共振分析精度。
Rootlets-based registration to the spinal cord PAM50 template
- 以脊髓神经根束替代椎间盘进行非线性配准。
- 跨个体和颈部姿态的对齐更稳定,激活区域扩大两倍以上。
- 适合脊髓功能成像的群体分析,尤其多中心研究场景。
脊髓功能磁共振研究需精准定位脊髓节段以实现可靠的体素级群体分析。传统基于模板的脊髓配准依赖椎间盘对齐,但椎体与脊髓节段间存在显著个体解剖差异。本研究提出一种新方法,利用脊髓后侧颈段神经根束分割并将其非线性对齐至PAM50脊髓模板。在多被试多中心数据集(n=267,44个站点)及不同颈部姿势下的多被试数据(n=10,3次扫描)上验证该方法,并在任务态功能磁共振(n=23)中对比根束配准与传统椎间盘配准的群体激活图。结果表明,根束配准在跨个体间具有更高对齐精度,且在不同颈部姿态下根束位置更稳定。使用根束配准的群体分析使Z值升高,激活体素数从3292增至7978,显著提升。该方法同时改善了组内与组间解剖对齐,提升了群体水平功能磁共振的空间归一化效果。研究证实,根束配准可提高脊髓神经影像群体分析的精确性与可靠性。
原文摘要 · Abstract (English)
Spinal cord functional MRI studies require precise localization of spinal levels for reliable voxelwise group analyses. Traditional template-based registration of the spinal cord uses intervertebral discs for alignment. However, substantial anatomical variability across individuals exists between vertebral and spinal levels. This study proposes a novel registration approach that leverages spinal nerve rootlets to improve alignment accuracy and reproducibility across individuals. We developed a registration method leveraging dorsal cervical rootlets segmentation and aligning them non-linearly with the PAM50 spinal cord template. Validation was performed on a multi-subject, multi-site dataset (n=267, 44 sites) and a multi-subject dataset with various neck positions (n=10, 3 sessions). We further validated the method on task-based functional MRI (n=23) to compare group-level activation maps using rootlet-based registration to traditional disc-based methods. Rootlet-based registration showed superior alignment across individuals compared to the traditional disc-based method. Notably, rootlet positions were more stable across neck positions. Group-level analysis of task-based functional MRI using rootlet-based increased Z scores and activation cluster size compared to disc-based registration (number of active voxels from 3292 to 7978). Rootlet-based registration enhances both inter- and intra-subject anatomical alignment and yields better spatial normalization for group-level fMRI analyses. Our findings highlight the potential of rootlet-based registration to improve the precision and reliability of spinal cord neuroimaging group analysis.
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