Fusing multi-scale information in convolution network for MR image super-resolution reconstruction
Open accessAbstract
BACKGROUND: Magnetic resonance (MR) images are usually limited by low spatial resolution, which leads to errors in post-processing procedures. Recently, learning-based super-resolution methods, such as sparse coding and super-resolution convolution neural network, have achieved promising reconstruction results in scene images. However, these methods remain insufficient for recovering detailed information from low-resolution MR images due to the limited size of training dataset. METHODS: To investigate the different edge responses using different convolution kernel sizes, this study employs a multi-scale fusion convolution network (MFCN) to perform super-resolution for MRI images. Unlike traditional convolution networks that simply stack several convolution layers, the proposed network is stacked by multi-scale fusion units (MFUs). Each MFU consists of a main path and some sub-paths and finally fuses all paths within the fusion layer. RESULTS: We discussed our experimental network parameters setting using simulated data to achieve trade-offs between the reconstruction performance and computational efficiency. We also conducted super-resolution reconstruction experiments using real datasets of MR brain images and demonstrated that the proposed MFCN has achieved a remarkable improvement in recovering detailed information from MR images and outperforms state-of-the-art methods. CONCLUSIONS: We have proposed a multi-scale fusion convolution network based on MFUs which extracts different scales features to restore the detail information. The structure of the MFU is helpful for extracting multi-scale information and making full-use of prior knowledge from a few training samples to enhance the spatial resolution.
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Deep learning approaches have been introduced for MRI super-resolution.
“Recently deep learning approaches has been introduced, e.g. generating super-resolution single (no reference information) and multi-contrast (applying a high-resolution image of another modality as reference) brain MR images using CNNs [197]; constructing superresolution brain MRI by a CNN stacked by multi-scale fusion units [198]; and super-resolution musculoskeletal MRI ('DeepResolve') [199].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [198]
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- Authors
- Liu, C., Wu, X., Yu, X., Tang, Y., Zhang, J., & Zhou, J.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Chengdu University of Information Technology (CN)
- Chengdu University (CN)
- University of Macau (MO)
- Sichuan Normal University (CN)
- Corresponding author
- Wu, Xi
- Venue
- BioMedical Engineering OnLine
- Published
- 2018-08-25
- Volume
- 17
- Issue
- 1
- Pages
- 114-114
- ISSN
- 1475-925X (ISSN-L)
- PMC
- PMC6109361
- OpenAlex
- W2888022684
- Cited by
- 47
- References
- 39
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 9 · 2023: 3 · 2022: 5 · 2021: 9 · 2020: 8 · 2019: 1 · 2018: 3
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BioMedical Engineering OnLine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61502059)
- China Postdoctoral Science Foundation (2016M592656)
- The Educational Commission of Sichuan Province of China (15ZA360)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- MRI super-resolution, multi-scale fusion, convolutional neural networks, brain MRI, edge response, convolution kernel size, detail recovery, limited training data
- MeSH terms
- Algorithms, Brain, Image Processing, Computer-Assisted, Magnetic Resonance Imaging
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