ArticleMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2021
Accelerated Acquisition of High-resolution Diffusion-weighted Imaging of the Brain with a Multi-shot Echo-planar Sequence: Deep-learning-based Denoising.
Article in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of 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 18 papers.
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Who cites it
18 citing papers in PubMed, 33 citations in OpenAlex.
- Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging.AJNR. American journal of neuroradiology · 2025Article
- Clinical evaluation of accelerated diffusion-weighted imaging of rectal cancer using a denoising neural network.European journal of radiology · 2024Article
- Deep Learning-based Image Enhancement Techniques for Fast MRI in Neuroimaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024Review
- Convolutional network denoising for acceleration of multi-shot diffusion MRI.Magnetic resonance imaging · 2024Article
- Denoising diffusion MRI: Considerations and implications for analysis.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- [Diffusion tensor field estimation based on 3D U-Net and diffusion tensor imaging model constraint].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network.Bioengineering (Basel, Switzerland) · 2023Article
- Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.Journal of digital imaging · 2023Review
- Accelerated Diffusion-Weighted MR Image Reconstruction Using Deep Neural Networks.Journal of digital imaging · 2023Article
- Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives.Biomedicines · 2023Review
- versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database.Frontiers in neuroinformatics · 2023Article
- Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation.La Radiologia medica · 2022Article
- Quantitative diffusion MRI of the abdomen and pelvis.Medical physics · 2022Article
- Artificial Intelligence in Radiation Therapy.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Self-supervised IVIM DWI parameter estimation with a physics based forward model.Magnetic resonance in medicine · 2022Article
- Simultaneous Multislice for Accelerating Diffusion MRI in Clinical Neuroradiology Protocols.AJNR. American journal of neuroradiology · 2021Article
- Applicability of deep learning-based reconstruction trained by brain and knee 3T MRI to lumbar 1.5T MRI.Acta radiologica open · 2021Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To accelerate high-resolution diffusion-weighted imaging with a multi-shot echo-planar sequence, we propose an approach based on reduced averaging and deep learning. Denoising convolutional neural networks can reduce amplified noise without requiring extensive averaging, enabling shorter scan times and high image quality. The preliminary experimental results demonstrate the superior performance of the proposed denoising method over state-of-the-art methods such as the widely used block-matching and 3D filtering.
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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.