Precise diffusion anisotropy measurement using diffusion tensor MRI
- 1. GE Yokogawa Medical Systems Ltd., Hino, Tokyo (Japan)
- 2. Tokyo Univ., Graduate School of Medicine, Tokyo (Japan)
Description
Magnetic Resonance Diffusion Tensor Imaging (DTI) has been widely used to measure diffusion anisotropy in white matter and those diffusion anisotropy measurement showed clinical usefulness in various fields. Typical diffusion anisotropy index, such as Fractional Anisotropy (FA) are accurate at single fiber but this index has its intrinsic limitation at crossing fiber. It has been shown that high angular resolution diffusion measurement or q-space measurement were able to provide accurate diffusion characteristic measurement at the voxel that includes multi-directional fibers. However these methods requires huge amount of data to decompose crossing fibers, and that makes scan time extremely long. Applying those methods to clinical study is virtually impossible due to such a long scan time. In this study, diffusion properties are estimated from sparse data set to reduce scan time. Fiber directions were estimated by DTI and novel image processing method. Then Sparsely sampled high angular diffusion-weighted image data set were analyzed with the estimated fiber orientations as ''a prior'' information to measure diffusion properties at the crossing fiber voxel. (author)
Additional details
Publishing Information
- Journal Title
- Medical Imaging Technology
- Journal Volume
- 26
- Journal Issue
- 3
- Journal Page Range
- p. 162-168
- ISSN
- 0288-450X
INIS
- Country of Publication
- Japan
- Country of Input or Organization
- Japan
- INIS RN
- 39089536
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- ANISOTROPY; CEREBRAL CORTEX; COMPUTERIZED SIMULATION; DIFFUSION; FIBERS; IMAGE PROCESSING; MATHEMATICAL MODELS; NMR IMAGING; PHANTOMS; SIGNALS; TENSORS
- Descriptors DEC
- BODY; BRAIN; CENTRAL NERVOUS SYSTEM; CEREBRUM; DIAGNOSTIC TECHNIQUES; MOCKUP; NERVOUS SYSTEM; ORGANS; PROCESSING; SIMULATION; STRUCTURAL MODELS