ArticleMagnetic resonance imaging2019
Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI.
Article in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed, 49 citations in OpenAlex.
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- Learning-based Free-Water Correction using Single-shell Diffusion MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- E(3) × SO(3)-Equivariant Networks for Spherical Deconvolution in Diffusion MRI.Proceedings of machine learning research · 2024Article
- Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study.bioRxiv : the preprint server for biology · 2023Article
- Mapping the impact of nonlinear gradient fields with noise on diffusion MRI.Magnetic resonance imaging · 2023Article
- Using mesoscopic tract-tracing data to guide the estimation of fiber orientation distributions in the mouse brain from diffusion MRI.NeuroImage · 2023Article
- A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-Weighted MRI.Computational diffusion MRI. CDMRI (Workshop) · 2023Article
- Diffusion tensor estimation with transformer neural networks.Artificial intelligence in medicine · 2022Article
- Informative and Reliable Tract Segmentation for Preoperative Planning.Frontiers in radiology · 2022Article
- Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2021Article
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- Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI.NeuroImage · 2021Review
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13 authors at 2 institutions in 1 country.
Funding
Abstract
purposeDiffusion-weighted magnetic resonance imaging (DW-MRI) is of critical importance for characterizing in-vivo white matter. Models relating microarchitecture to observed DW-MRI signals as a function of diffusion sensitization are the lens through which DW-MRI data are interpreted. Numerous modern approaches offer opportunities to assess more complex intra-voxel structures. Nevertheless, there remains a substantial gap between intra-voxel estimated structures and ground truth captured by 3-D histology.
methodsHerein, we propose a novel data-driven approach to model the non-linear mapping between observed DW-MRI signals and ground truth structures using a sequential deep neural network regression using residual block deep neural network (ResDNN). Training was performed on two 3-D histology datasets of squirrel monkey brains and validated on a third. A second validation was performed using scan-rescan datasets of 12 subjects from Human Connectome Project. The ResDNN was compared with multiple micro-structure reconstruction methods and super resolved-constrained spherical deconvolution (sCSD) in particular as baseline for both the validations.
resultsAngular correlation coefficient (ACC) is a correlation/similarity measure and can be interpreted as accuracy when compared with a ground truth. The median ACC of ResDNN is 0.82 and median ACC's of different variants of CSD are 0.75, 0.77, 0.79. The mean, median and std. of ResDNN & sCSD ACC across 12 subjects from HCP are 0.74, 0.88, 0.31 and 0.61, 0.71, 0.31 respectively.
conclusionThis work highlights the ability of deep learning to capture linkages between ex-vivo ground truth data with feasible MRI sequences. The data-driven approach is applicable to human in-vivo data and results in intriguingly high reproducibility of orientation structure.
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