ArticleSensors (Basel, Switzerland)2021
Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- A Systematic Review of Medical Image Quality Assessment.Journal of imaging · 2025Review
- Denoising diffusion MRI: Considerations and implications for analysis.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Quantifying MR head motion in the Rhineland Study - A robust method for population cohorts.NeuroImage · 2023Article
- The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights.Sensors (Basel, Switzerland) · 2023Article
- A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images.Journal of imaging · 2022Article
- Multi-Dimensional Feature Fusion Network for No-Reference Quality Assessment of In-the-Wild Videos.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images. Then, to improve the quality prediction performance, the support vector machine regression (SVR) technique is employed on the features generated by fused networks. In the paper, several promising network architectures are introduced, investigated, and experimentally compared with state-of-the-art NR-IQA methods on two representative MRIQA benchmark datasets. One of the datasets is introduced in this work. As the experimental validation reveals, the proposed fusion of networks outperforms related approaches in terms of correlation with subjective opinions of a large number of experienced radiologists.
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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.