ArticleNeuroImage2016
DR-TAMAS: Diffeomorphic Registration for Tensor Accurate Alignment of Anatomical Structures.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
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- White matter trajectories in Down syndrome and Alzheimer's disease: Insights from diffusion tensor-based morphometry.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- FetDTIAlign: A deep learning framework for affine and deformable registration of fetal brain dMRI.NeuroImage · 2025Article
- Association Between Changes in White Matter Volume Detected With Diffusion Tensor-Based Morphometry and Motor Recovery After Stroke.Neurology · 2025Article
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- Volume changes in white matter pathways from infancy to early adulthood measured using diffusion tensor based morphometry.Frontiers in neurology · 2025Article
- Improved cognition and preserved hippocampal fractional anisotropy in subjects undergoing carotid endarterectomy "CEA preserves cognition & hippocampal structure".Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2024Article
- Sex Differences in White Matter Diffusivity in Children with Developmental Dyslexia.Children (Basel, Switzerland) · 2024Article
- MMORF-FSL's MultiMOdal Registration Framework.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Internally consistent and fully unbiased multimodal MRI brain template construction from UK Biobank: Oxford-MM.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Connectivity defines the distinctive anatomy and function of the hand-knob area.Brain communications · 2024Article
- High resolution 0.5mm isotropic TNeuroImage · 2023Article
- Development of high quality TNeuroImage · 2022Article
- The SACT Template: A Human Brain Diffusion Tensor Template for School-age Children.Neuroscience bulletin · 2022Article
- What's new and what's next in diffusion MRI preprocessing.NeuroImage · 2022Review
- DTI Atlases Evaluations.Neuroinformatics · 2022Article
- Development and evaluation of a high resolution 0.5mm isotropic T1-weighted template of the older adult brain.NeuroImage · 2022Article
- Multimodal imaging of essential tremor and dystonic tremor.NeuroImage. Clinical · 2022Article
- Group characterization of impact-induced,Journal of the Royal Society, Interface · 2021Article
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7 authors.
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Abstract
In this work, we propose DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces containing cerebrospinal fluid (CSF). Currently most DTI-based spatial normalization algorithms emphasize alignment of anisotropic structures. While some diffusion-derived metrics, such as diffusion anisotropy and tensor eigenvector orientation, are highly informative for proper alignment of WM, other tensor metrics such as the trace or mean diffusivity (MD) are fundamental for a proper alignment of GM and CSF boundaries. Moreover, it is desirable to include information from structural MRI data, e.g., T1-weighted or T2-weighted images, which are usually available together with the diffusion data. The fundamental property of DR-TAMAS is to achieve global anatomical accuracy by incorporating in its cost function the most informative metrics locally. Another important feature of DR-TAMAS is a symmetric time-varying velocity-based transformation model, which enables it to account for potentially large anatomical variability in healthy subjects and patients. The performance of DR-TAMAS is evaluated with several data sets and compared with other widely-used diffeomorphic image registration techniques employing both full tensor information and/or DTI-derived scalar maps. Our results show that the proposed method has excellent overall performance in the entire brain, while being equivalent to the best existing methods in WM.
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