ArticleMagnetic resonance in medicine2022
Self-supervised IVIM DWI parameter estimation with a physics based forward model.
Article in 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 18 papers.
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18 citing papers in PubMed, 52 citations in OpenAlex.
- Neural networks improve repeatability of intravoxel incoherent motion (IVIM) parameter estimation in pancreatic diffusion-weighted MRI.Medical & biological engineering & computing · 2026Article
- SynQ-Breast: a flexible deep learning framework for quantitative MRI of breast tumors via synthetic data with IVIM-MRI validation.Quantitative imaging in medicine and surgery · 2026Article
- Self-Supervised Deep Learning Framework for Rician Distribution Based Denoising and Modeling of Multi-b Prostate Diffusion MRI.Magnetic resonance in medicine · 2026Article
- Accurate estimation of intravoxel incoherent motion parameters based on implicit neural representation.Medical physics · 2026Article
- A Comparative Study of IVIM-MRI Fitting Techniques in Glioma Grading: Conventional, Bayesian, and Voxel-Wise and Spatially-Aware Deep Learning Approaches.Journal of magnetic resonance imaging : JMRI · 2026Article
- Towards Clinical Translation of Intravoxel Incoherent Motion MRI: Acquisition and Analysis Consensus Recommendations.Journal of magnetic resonance imaging : JMRI · 2026Article
- Hierarchical Bayesian Modelling Improves Microstructural Parameter Mapping in Diffusion and Exchange MRI Data.NMR in biomedicine · 2026Article
- Simulation-based inference at the theoretical limit for fast, robust microstructural MRI with minimal diffusion data.Communications medicine · 2026Article
- Rician Likelihood Loss for Quantitative MRI With Self-Supervised Deep Learning.NMR in biomedicine · 2025Article
- Radiologists and treatment response assessment: conventional and functional radiological criteria for predicting therapeutic response in virus-related and non-virus-related cancers.Infectious agents and cancer · 2025Review
- IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent Motion (IVIM) analysis for functional fetal lung maturity assessment from diffusion-weighted MRI data.Medical image analysis · 2025Article
- Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI.Radiology. Artificial intelligence · 2025Article
- Quantitative evaluation of fibrosis in idiopathic orbital inflammatory pseudotumor by IVIM-DWI.Frontiers in ophthalmology · 2025Article
- Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions.Journal of magnetic resonance imaging : JMRI · 2024Review
- Improved quantitative parameter estimation for prostate TMagma (New York, N.Y.) · 2024Article
- DIMOND: DIffusion Model OptimizatioN with Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Improved myelin water fraction mapping with deep neural networks using synthetically generated 3D data.Medical image analysis · 2024Article
- Self Supervised Denoising Diffusion Probabilistic Models for Abdominal DW-MRI.Computational diffusion MRI. CDMRI (Workshop) · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo assess the robustness and repeatability of intravoxel incoherent motion model (IVIM) parameter estimation for the diffusion-weighted MRI in the abdominal organs under the constraints of noisy diffusion signal using a novel neural network method.
methodsClinically acquired abdominal scans of Crohn's disease patients were retrospectively analyzed with regions segmented in the kidney cortex, spleen, liver, and bowel. A novel IVIM parameter fitting method based on the principle of a physics guided self-supervised convolutional neural network that does not require reference parameter estimates for training was compared to a conventional non-linear least squares (NNLS) algorithm, and a voxelwise trained artificial neural network (ANN).
resultsResults showed substantial increase in parameter robustness to the noise corrupted signal. In an intra-session repeatability experiment, the proposed method showed reduced coefficient of variation (CoV) over multiple acquisitions in comparison to conventional NLLS method and comparable performance to ANN. The use of D and f estimates from the proposed method led to the smallest misclassification error in linear discriminant analysis for characterization between normal and abnormal Crohn's disease bowel tissue. The fitting of
conclusionThe proposed method yields robust estimates of D and f IVIM parameters under the constraints of noisy diffusion signal. This indicates a potential for the use of the proposed method in conjunction with accelerated DW-MRI acquisition strategies, which would typically result in lower signal to noise ratio.
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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.