期刊
NEUROIMAGE
卷 102, 期 -, 页码 817-827出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2014.08.057
关键词
Spinal cord; MRI; Template; Group analysis; Registration
资金
- Association Francaise contre les Myopathies (AFM)
- Institut pour la Recherche sur la Moelle epiniere et l'Encephale (IRME)
- SensoriMotor Rehabilitation Research Team of the Canadian Institute of Health Research
- National MS Society [FG1892A1/1]
- Fonds de Recherche du Quebec-Sante
- Quebec BioImaging Network
- Natural Sciences and Engineering Research Council of Canada
- French National Research Agency (Investissements d'Avenir, A*MIDEX) [ANR-11-IDEX-0001-02]
The field of spinal cord MRI is lacking a common template, as existing for the brain, which would allow extraction of multi-parametric data (diffusion-weighted, magnetization transfer, etc.) without user bias, thereby facilitating group analysis and multi-center studies. This paper describes a framework to produce an unbiased average anatomical template of the human spinal cord. The template was created by co-registering T2-weighted images (N=16 healthy volunteers) using a series of pre-processing steps followed by non-linear registration. A white and gray matter probabilistic template was then merged to the average anatomical template, yielding the MNI-Poly-AMU template, which currently covers vertebral levels C1 to T6. New subjects can be registered to the template using a dedicated image processing pipeline. Validation was conducted on 16 additional subjects by comparing an automatic template-based segmentation and manual segmentation, yielding amedian Dice coefficient of 0.89. The registration pipeline is rapid (similar to 15 min), automatic after one C2/C3 landmark manual identification, and robust, thereby reducing subjective variability and bias associated with manual segmentation. The template can notably be used for measurements of spinal cord cross-sectional area, voxel-based morphometry, identification of anatomical features (e. g., vertebral levels, white and gray matter location) and unbiased extraction of multi-parametric data. (C) 2014 Elsevier Inc. All rights reserved.
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