ArticleMagnetic resonance imaging2020
A multi-scale residual network for accelerated radial MR parameter mapping.
Article in Magnetic resonance imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Exempting axillary staging surgery in breast cancer using multimodal ultrasound imaging and radiomics of sentinel lymph nodes.EClinicalMedicine · 2026Article
- Enhancing computation speed and accuracy in deep image prior-based parameter mapping.Magnetic resonance in medicine · 2025Article
- Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data.Magnetic resonance in medicine · 2025Article
- HSGDNet: Hybrid Synthetic-Data-Guided Deep Learning With NLS Refinement for Fast Multi-Component T1ρ Knee Mapping.NMR in biomedicine · 2025Article
- Article
- HDNLS: Hybrid Deep-Learning and Non-Linear Least Squares-Based Method for Fast Multi-Component T1ρ Mapping in the Knee Joint.Bioengineering (Basel, Switzerland) · 2024Article
- Uncertainty-aware physics-driven deep learning network for free-breathing liver fat and RMagnetic resonance in medicine · 2023Article
- SuperMAP: Deep ultrafast MR relaxometry with joint spatiotemporal undersampling.Magnetic resonance in medicine · 2023Article
- Convolutional Neural Network-Based Computer-Assisted Diagnosis of Hashimoto's Thyroiditis on Ultrasound.The Journal of clinical endocrinology and metabolism · 2022Article
- Abdominal T2-Weighted Imaging and T2 Mapping Using a Variable Flip Angle Radial Turbo Spin-Echo Technique.Journal of magnetic resonance imaging : JMRI · 2022Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
A deep learning MR parameter mapping framework which combines accelerated radial data acquisition with a multi-scale residual network (MS-ResNet) for image reconstruction is proposed. The proposed supervised learning strategy uses input image patches from multi-contrast images with radial undersampling artifacts and target image patches from artifact-free multi-contrast images. Subspace filtering is used during pre-processing to denoise input patches. For each anatomy and relaxation parameter, an individual network is trained. in vivo T
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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.