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Automatic whole brain tract‐based analysis using predefined tracts in a diffusion spectrum imaging template and an accurate registration strategy
Author(s) -
Chen YuJen,
Lo YuChun,
Hsu YungChin,
Fan ChunChieh,
Hwang TzungJeng,
Liu ChihMin,
Chien YiLing,
Hsieh Ming H.,
Liu ChenChung,
Hwu HaiGwo,
Tseng WenYih Isaac
Publication year - 2015
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.22854
Subject(s) - white matter , diffusion mri , computer science , artificial intelligence , image registration , pattern recognition (psychology) , computer vision , magnetic resonance imaging , radiology , medicine , image (mathematics)
Automated tract‐based analysis of diffusion MRI is an important tool for investigating tract integrity of the cerebral white matter. Current template‐based automatic analyses still lack a comprehensive list of tract atlas and an accurate registration method. In this study, tract‐based automatic analysis (TBAA) was developed to meet the demands. Seventy‐six major white matter tracts were reconstructed on a high‐quality diffusion spectrum imaging (DSI) template, and an advanced two‐step registration strategy was proposed by incorporating anatomical information of the gray matter from T1‐weighted images in addition to microstructural information of the white matter from diffusion‐weighted images. The automatic analysis was achieved by establishing a transformation between the DSI template and DSI dataset of the subject derived from the registration strategy. The tract coordinates in the template were transformed to native space in the individual's DSI dataset, and the microstructural properties of major tract bundles were sampled stepwise along the tract coordinates of the subject's DSI dataset. In a validation study of eight well‐known tracts, our results showed that TBAA had high geometric agreement with manual tracts in both deep and superficial parts but significantly smaller measurement variability than manual method in functional difference. Additionally, the feasibility of the method was demonstrated by showing tracts with altered microstructural properties in patients with schizophrenia. Fifteen major tract bundles were found to have significant differences after controlling the family‐wise error rate. In conclusion, the proposed TBAA method is potentially useful in brain‐wise investigations of white matter tracts, particularly for a large cohort study. Hum Brain Mapp 36:3441–3458, 2015 . © 2015 Wiley Periodicals, Inc .

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