ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2017
Graph-Constrained Sparse Construction of Longitudinal Diffusion-Weighted Infant Atlases.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Review on the application of imaging examination for brain injury in premature infants.Frontiers in neurology · 2023Review
- Hierarchical geodesic modeling on the diffusion orientation distribution function for longitudinal DW-MRI analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- A framework to construct a longitudinal DW-MRI infant atlas based on mixed effects modeling of dODF coefficients.Computational diffusion MRI. CDMRI (Workshop) · 2020Article
- Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space.IEEE transactions on medical imaging · 2019Article
- Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks.IEEE transactions on medical imaging · 2019Article
- XQ-SR: Joint x-q space super-resolution with application to infant diffusion MRI.Medical image analysis · 2019Article
- Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space.Medical image analysis · 2019Article
- A framework for multi-component analysis of diffusion MRI data over the neonatal period.NeuroImage · 2019Article
- Computational neuroanatomy of baby brains: A review.NeuroImage · 2019Review
- Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching inFrontiers in neuroinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Constructing longitudinal diffusion-weighted atlases of infant brains poses additional challenges due to the small brain size and the dynamic changes in the early developing brains. In this paper, we introduce a novel framework for constructing longitudinally-consistent diffusion-weighted infant atlases with improved preservation of structural details and diffusion characteristics. In particular, instead of smoothing diffusion signals by simple averaging, our approach fuses the diffusion-weighted images in a patch-wise manner using sparse representation with a graph constraint that encourages spatiotemporal consistency. Diffusion-weighted atlases across time points are jointly constructed for patches that are correlated in time and space. Compared with existing methods, including the one using sparse representation with
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.