ArticleMagnetic resonance in medicine2018
A diffusion model-free framework with echo time dependence for free-water elimination and brain tissue microstructure characterization.
Article in Magnetic resonance in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Deep learning-based free-water correction for single-shell diffusion MRI.Magnetic resonance imaging · 2025Article
- Learning-based Free-Water Correction using Single-shell Diffusion MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Diffusion MR Imaging with T2-based Water Suppression (T2wsup-dMRI).Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Article
- Combined diffusion-relaxometry microstructure imaging: Current status and future prospects.Magnetic resonance in medicine · 2021Review
- Data-Driven multi-Contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping.Medical image analysis · 2021Article
- miR-34a overexpression protects against hippocampal neuron damage caused by ketamine-induced anesthesia in immature rats through the Notch-1/NF-κB signaling pathway.American journal of translational research · 2021Article
- Brain White-Matter Degeneration Due to Aging and Parkinson Disease as Revealed by Double Diffusion Encoding.Frontiers in neuroscience · 2020Article
- A diffusion model-free framework with echo time dependence for free-water elimination and brain tissue microstructure characterization.Magnetic resonance in medicine · 2018Article
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
Abstract
purposeThe compartmental nature of brain tissue microstructure is typically studied by diffusion MRI, MR relaxometry or their correlation. Diffusion MRI relies on signal representations or biophysical models, while MR relaxometry and correlation studies are based on regularized inverse Laplace transforms (ILTs). Here we introduce a general framework for characterizing microstructure that does not depend on diffusion modeling and replaces ill-posed ILTs with blind source separation (BSS). This framework yields proton density, relaxation times, volume fractions, and signal disentanglement, allowing for separation of the free-water component. THEORY AND
methodsDiffusion experiments repeated for several different echo times, contain entangled diffusion and relaxation compartmental information. These can be disentangled by BSS using a physically constrained nonnegative matrix factorization.
resultsComputer simulations, phantom studies, together with repeatability and reproducibility experiments demonstrated that BSS is capable of estimating proton density, compartmental volume fractions and transversal relaxations. In vivo results proved its potential to correct for free-water contamination and to estimate tissue parameters.
conclusionFormulation of the diffusion-relaxation dependence as a BSS problem introduces a new framework for studying microstructure compartmentalization, and a novel tool for free-water elimination.
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