ArticleFrontiers in neurology2022
Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Sensitivity of quantitative diffusion MRI tractography and microstructure to anisotropic spatial sampling.Magnetic resonance imaging · 2025Article
- Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study.Medical image analysis · 2024Article
- Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study.bioRxiv : the preprint server for biology · 2023Article
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9 authors.
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Abstract
Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ an unsupervised learning algorithm using an autoencoder neural network for single-image through-plane super-resolution by leveraging a large amount of data. Our framework, which can also be used for slice outliers replacement, overperformed conventional interpolations quantitatively and qualitatively on pre-term newborns of the developing Human Connectome Project. The evaluation was performed on both the original diffusion-weighted signal and the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser. The network was able to generalize fetal data with different levels of motions and we qualitatively showed its consistency, hence supporting the relevance of pre-term datasets to improve the processing of fetal brain images.
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