ArticleMagnetic resonance in medicine2021
Training a neural network for Gibbs and noise removal in diffusion MRI.
Article in Magnetic resonance in medicine, 2021. 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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Who cites it
27 citing papers in PubMed.
- Collaborative Reconstruction of PROPELLER-EPI Data Using POCSMUSE (CORPUSE) for High-Fidelity Diffusion MRI.Magnetic resonance in medicine · 2026Article
- Attention-Guided TransMorph for Real-Time Tumor Tracking in Cine-MRI.Journal of imaging informatics in medicine · 2026Article
- Axon Diameter Mapping From Myelin Water Diffusion MRI.IEEE transactions on medical imaging · 2026Article
- FOCUS-DWI improves prostate cancer detection through deep learning reconstruction with IQMR technology.Abdominal radiology (New York) · 2026Article
- Evaluating the impact of denoising diffusion MRI data on tractometry metrics of optic tract abnormalities in glaucoma.Scientific reports · 2025Article
- Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging.Magnetic resonance in medicine · 2025Review
- Comparison of different neurite density metrics with brain asymmetry evaluation.Zeitschrift fur medizinische Physik · 2025Article
- Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI.Journal of imaging informatics in medicine · 2025Article
- Development and evaluation of a deep learning model for multi-frequency Gibbs artifact elimination.Quantitative imaging in medicine and surgery · 2025Article
- The effect of elimination of gibbs ringing, noise and systematic errors on the DTI metrics and tractography in a rat brain.Scientific reports · 2024Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Generative AI for rapid diffusion MRI with improved image quality, reliability, and generalizability.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Deep learning enabled fast 3D brain MRI at 0.055 tesla.Science advances · 2023Article
- High-fidelity mesoscale in-vivo diffusion MRI through gSlider-BUDA and circular EPI with S-LORAKS reconstruction.NeuroImage · 2023Article
- Tensor denoising of multidimensional MRI data.Magnetic resonance in medicine · 2023Article
- Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.Journal of digital imaging · 2023Review
- Efficient PCA denoising of spatially correlated redundant MRI data.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- Deep Learning and Its Application to Function Approximation for MR in Medicine: An Overview.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Review
- Deep learning-based motion artifact removal in functional near-infrared spectroscopy.Neurophotonics · 2022Article
- Exploiting Missing Value Patterns for a Backdoor Attack on Machine Learning Models of Electronic Health Records: Development and Validation Study.JMIR medical informatics · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeTo develop and evaluate a neural network-based method for Gibbs artifact and noise removal.
methodsA convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images.
resultsBoth machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions.
conclusionsThe proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.
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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.