ArticleNMR in biomedicine2022
A supervised deep neural network approach with standardized targets for enhanced accuracy of IVIM parameter estimation from multi-SNR images.
Article in NMR in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- 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
- In vivo study of intravoxel incoherent motion imaging in assessing obesity-related kidney injury.BMC medical imaging · 2026Article
- Enhancing computation speed and accuracy in deep image prior-based parameter mapping.Magnetic resonance in medicine · 2025Article
- Tissue classification from raw diffusion-weighted images using machine learning.Medical physics · 2025Article
- Reliability of radiomic analysis on multiparametric MRI for patients affected by autosomal dominant polycystic kidney disease.Scientific reports · 2025Article
- Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.Frontiers in oncology · 2025Article
- Reproducibility of spatial penalty-based methodologies for intravoxel incoherent motion analysis with diffusion MRI.Scientific reports · 2024Article
- Multisite MRI Intravoxel Incoherent Motion Repeatability and Reproducibility across 3 T Scanners in a Breast Diffusion Phantom: A BReast Intravoxel Incoherent Motion Multisite (BRIMM) Study.Journal of magnetic resonance imaging : JMRI · 2024Article
- A supervised deep neural network approach with standardized targets for enhanced accuracy of IVIM parameter estimation from multi-SNR images.NMR in biomedicine · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Extraction of intravoxel incoherent motion (IVIM) parameters from noisy diffusion-weighted (DW) images using a biexponential fitting model is computationally challenging, and the reliability of the estimated perfusion-related quantities represents a limitation of this technique. Artificial intelligence can overcome the current limitations and be a suitable solution to advance use of this technique in both preclinical and clinical settings. The purpose of this work was to develop a deep neural network (DNN) approach, trained on numerical simulated phantoms with different signal to noise ratios (SNRs), to improve IVIM parameter estimation. The proposed approach is based on a supervised fully connected DNN having 3 hidden layers, 18 inputs and 3 targets with standardized values. 14 × 10
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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.