paper-with-me

홈 › Papers

Rootlets-based registration to the spinal cord PAM50 template

2025-04-30 · Sandrine Bédard, Jan Valošek, Valeria Oliva, Kenneth A. Weber II, Julien Cohen-Adad

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.

📄 PDF Abstract BibTeX arXiv:2505.00115

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Automatic Segmentation of the Spinal Cord Nerve Rootlets

2024-02-01 · Jan Valosek, Theo Mathieu, Raphaelle Schlienger, Olivia S. Kowalczyk 외

Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the spinal cord. The goal of this study was to develop an automatic method for the semantic s…

Active LearningSemantic Segmentation

Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction

2018-02-01 · NeuroImage 2018 2 · Claudia Blaiotta, Patrick Freund, M. Jorge Cardoso, John Ashburner

In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a…

Diffeomorphic Medical Image RegistrationImage RegistrationMedical Image Registration

Ultrasound-Guided Real-Time Spinal Motion Visualization for Spinal Instability Assessment

2026-02-13 · Feng Li, Yuan Bi, Tianyu Song, Zhongliang Jiang 외 arxiv

Purpose: Spinal instability is a widespread condition that causes pain, fatigue, and restricted mobility, profoundly affecting patients' quality of life. In clinical practice, the gold standard for diagnosis is dynamic X…

Enabling Augmented Segmentation and Registration in Ultrasound-Guided Spinal Surgery via Realistic Ultrasound Synthesis from Diagnostic CT Volume

2023-01-05 · Ang Li, Jiayi Han, Yongjian Zhao, Keyu Li 외

This paper aims to tackle the issues on unavailable or insufficient clinical US data and meaningful annotation to enable bone segmentation and registration for US-guided spinal surgery. While the US is not a standard par…

DiagnosticSegmentation

MSR:Hybrid Field Modeling for CT-MRI Rigid-Deformable Registration of the Cervical Spine with an Annotated Dataset

2026-04-30 · Bohai Zhang, Wenjie Chen, Mu Li, Kaixing Long 외 arxiv

Accurate CT-MRI registration of the cervical spine is essential for preoperative planning because this region is anatomically complex,highly variable,and vulnerable to injury of the vertebral arteries and spinal cord. Ho…