ArticleNeuroImage2021
Deep learning-based parameter estimation in fetal diffusion-weighted MRI.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Fetal brain MRI atlases and datasets: A review.NeuroImage · 2024Pooled it
- Whole anterior visual pathway segmentation from high-resolution MRI using artificial intelligence.European radiology experimental · 2026Article
- Deep Learning Empowered Microstructure Codebook: New Paradigm for Multi-Parameter Tissue Characterization Estimation.Human brain mapping · 2026Article
- Detailed Delineation of the Fetal Brain in Diffusion MRI via Multi-Task Learning.IEEE transactions on medical imaging · 2026Article
- Deep Learning for fODF Estimation in Infant Brains: Model Comparison, Ground-Truth Impact, and Domain Shift Mitigation.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
- HAITCH: A framework for distortion and motion correction in fetal multi-shell diffusion-weighted MRI.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- White Matter Tract Crossing and Bottleneck Regions in the Fetal Brain.Human brain mapping · 2025Article
- Equivariant spherical CNNs for accurate fiber orientation distribution estimation in neonatal diffusion MRI with reduced acquisition time.Frontiers in neuroscience · 2025Article
- White matter tract crossing and bottleneck regions in the fetal brain.bioRxiv : the preprint server for biology · 2024Article
- Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study.Medical image analysis · 2024Article
- DIMOND: DIffusion Model OptimizatioN with Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- A detailed spatio-temporal atlas of the white matter tracts for the fetal brain.bioRxiv : the preprint server for biology · 2024Article
- Fetal neuroimaging applications for diagnosis and counseling of brain anomalies: Current practice and future diagnostic strategies.Seminars in fetal & neonatal medicine · 2024Review
- 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
- Likelihood-free posterior estimation and uncertainty quantification for diffusion MRI models.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Spherical convolutional neural networks can improve brain microstructure estimation from diffusion MRI data.Frontiers in neuroimaging · 2024Article
- Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) of fetal brain is challenged by frequent fetal motion and signal to noise ratio that is much lower than non-fetal imaging. As a result, accurate and robust parameter estimation in fetal DW-MRI remains an open problem. Recently, deep learning techniques have been successfully used for DW-MRI parameter estimation in non-fetal subjects. However, none of those prior works has addressed the fetal brain because obtaining reliable fetal training data is challenging. To address this problem, in this work we propose a novel methodology that utilizes fetal scans as well as scans from prematurely-born infants. High-quality newborn scans are used to estimate accurate maps of the parameter of interest. These parameter maps are then used to generate DW-MRI data that match the measurement scheme and noise distribution that are characteristic of fetal data. In order to demonstrate the effectiveness and reliability of the proposed data generation pipeline, we used the generated data to train a convolutional neural network (CNN) to estimate color fractional anisotropy (CFA). We evaluated the trained CNN on independent sets of fetal data in terms of reconstruction accuracy, precision, and expert assessment of reconstruction quality. Results showed significantly lower reconstruction error (n=100,p<0.001) and higher reconstruction precision (n=20,p<0.001) for the proposed machine learning pipeline compared with standard estimation methods. Expert assessments on 20 fetal test scans showed significantly better overall reconstruction quality (p<0.001) and more accurate reconstruction of 11 regions of interest (p<0.001) with the proposed method.
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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.