ReviewMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2022
Recent Advances in Parameter Inference for Diffusion MRI Signal Models.
Review in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Neurite orientation dispersion and density imaging for preoperative evaluation of cerebral microstructure and neurodevelopment in children with congenital heart disease.Quantitative imaging in medicine and surgery · 2026Article
- Detection of white matter microstructural changes in patients with systemic lupus erythematosus based on multiple diffusion models and related diffusion metrics.Neural regeneration research · 2026Article
- Assessing cognitive impairment in OSAHS patients through NODDI-based gray matter analysis.BMC neurology · 2025Article
- Synthetic X-Q space learning for diffusion MRI parameter estimation: a pilot study in breast DKI.International journal of computer assisted radiology and surgery · 2025Article
- Data Science in Medical and Healthcare: Current Landscape.Juntendo medical journal · 2025Review
- Neural Circuitry-Related Biomarkers for Drug Development in Psychiatry: An Industry Perspective.Advances in neurobiology · 2024Review
Corrections and comments
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Authors and funding
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Abstract
In this paper, fundamentals and recent progress for obtaining biological features quantitatively by using diffusion MRI are reviewed. First, a brief description of diffusion MRI history, application, and development was presented. Then, well-known parametric models including diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), and neurite orientation dispersion diffusion imaging (NODDI) are introduced with several classifications in various viewpoints with other modeling schemes. In addition, this review covers mathematical generalization and examples of methodologies for the model parameter inference from conventional fitting to recent machine learning approaches, which is called Q-space learning (QSL). Finally, future perspectives on diffusion MRI parameter inference are discussed with the aspects of imaging modeling and simulation.
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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.