Automatic whole brain tract-based analysis using predefined tracts in a diffusion spectrum imaging template and an accurate registration strategy.
Yu-Jen ChenYu-Chun LoYung-Chin HsuChun-Chieh FanTzung-Jeng HwangChih-Min LiuYi-Ling ChienMing H HsiehChen-Chung LiuHai-Gwo HwuWen-Yih Isaac TsengPublished in: Human brain mapping (2015)
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.
Keyphrases
- white matter
- deep learning
- multiple sclerosis
- machine learning
- high resolution
- contrast enhanced
- diffusion weighted
- convolutional neural network
- molecularly imprinted
- magnetic resonance imaging
- magnetic resonance
- optical coherence tomography
- high throughput
- brain injury
- health information
- resting state
- photodynamic therapy
- subarachnoid hemorrhage
- cerebral ischemia
- functional connectivity