ArticleIEEE transactions on medical imaging2016
Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking.
Article in IEEE transactions on medical imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Detailed Delineation of the Fetal Brain in Diffusion MRI via Multi-Task Learning.IEEE transactions on medical imaging · 2026Article
- HERON: High-Efficiency Real-Time Motion Quantification and Re-Acquisition for Fetal Diffusion MRI.IEEE transactions on medical imaging · 2025Article
- FetDTIAlign: A deep learning framework for affine and deformable registration of fetal brain dMRI.NeuroImage · 2025Article
- Regional Changes in the Fetal Telencephalic Wall Diffusion Metrics Across Late Second and Third Trimesters.Human brain mapping · 2025Article
- A detailed spatiotemporal atlas of the white matter tracts for the fetal brain.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- White Matter Tract Crossing and Bottleneck Regions in the Fetal Brain.Human brain mapping · 2025Article
- Anatomically constrained tractography of the fetal brain.NeuroImage · 2024Article
- Advances in Fetal Brain Imaging.Magnetic resonance imaging clinics of North America · 2024Review
- White matter tract crossing and bottleneck regions in the fetal brain.bioRxiv : the preprint server for biology · 2024Article
- Population Atlas Analysis of Emerging Brain Structural Connections in the Human Fetus.Journal of magnetic resonance imaging : JMRI · 2024Article
- A detailed spatio-temporal atlas of the white matter tracts for the fetal brain.bioRxiv : the preprint server for biology · 2024Article
- Characterizing microstructural development in the fetal brain using diffusion MRI from 23 to 36 weeks of gestation.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI.IEEE transactions on medical imaging · 2023Article
- What's new and what's next in diffusion MRI preprocessing.NeuroImage · 2022Review
- Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain.Frontiers in neurology · 2022Article
- Spatiotemporal changes in diffusivity and anisotropy in fetal brain tractography.Human brain mapping · 2021Article
- Article
- Retrospective Distortion and Motion Correction for Free-Breathing DW-MRI of the Kidneys Using Dual-Echo EPI and Slice-to-Volume Registration.Journal of magnetic resonance imaging : JMRI · 2021Article
- Tractography of the Cerebellar Peduncles in Second- and Third-Trimester Fetuses.AJNR. American journal of neuroradiology · 2021Article
- Fetal magnetic resonance imaging: supratentorial brain malformations.Pediatric radiology · 2020Review
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6 authors.
Funding
Abstract
This work proposes a novel approach for motion-robust diffusion-weighted (DW) brain MRI reconstruction through tracking temporal head motion using slice-to-volume registration. The slice-level motion is estimated through a filtering approach that allows tracking the head motion during the scan and correcting for out-of-plane inconsistency in the acquired images. Diffusion-sensitized image slices are registered to a base volume sequentially over time in the acquisition order where an outlier-robust Kalman filter, coupled with slice-to-volume registration, estimates head motion parameters. Diffusion gradient directions are corrected for the aligned DWI slices based on the computed rotation parameters and the diffusion tensors are directly estimated from the corrected data at each voxel using weighted linear least squares. The method was evaluated in DWI scans of adult volunteers who deliberately moved during scans as well as clinical DWI of 28 neonates and children with different types of motion. Experimental results showed marked improvements in DWI reconstruction using the proposed method compared to the state-of-the-art DWI analysis based on volume-to-volume registration. This approach can be readily used to retrieve information from motion-corrupted DW imaging data.
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