The DOI matches its Crossref record; 51 of the 55 references it cites were found in a registry or an index, and 3 are not checked yet.
Super‐resolution musculoskeletal MRI using deep learning
Open accessAbstract
PURPOSE: To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods. METHODS: We implemented a 3D convolutional neural network entitled DeepResolve to learn residual-based transformations between high-resolution thin-slice images and lower-resolution thick-slice images at the same center locations. DeepResolve was trained using 124 double echo in steady-state (DESS) data sets with 0.7-mm slice thickness and tested on 17 patients. Ground-truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state-of-the-art single-image sparse-coding super-resolution. Comparisons were performed using structural similarity, peak SNR, and RMS error image quality metrics for a multitude of thin-slice downsampling factors. Two musculoskeletal radiologists ranked the 3 data sets and reviewed the diagnostic quality of the DeepResolve, tricubic interpolation, and ground-truth images for sharpness, contrast, artifacts, SNR, and overall diagnostic quality. Mann-Whitney U tests evaluated differences among the quantitative image metrics, reader scores, and rankings. Cohen's Kappa (κ) evaluated interreader reliability. RESULTS: DeepResolve had significantly better structural similarity, peak SNR, and RMS error than tricubic interpolation, Fourier interpolation, and sparse-coding super-resolution for all downsampling factors (p < .05, except 4 × and 8 × sparse-coding super-resolution downsampling factors). In the reader study, DeepResolve significantly outperformed (p < .01) tricubic interpolation in all image quality categories and overall image ranking. Both readers had substantial scoring agreement (κ = 0.73). CONCLUSION: DeepResolve was capable of resolving high-resolution thin-slice knee MRI from lower-resolution thicker slices, achieving superior quantitative and qualitative diagnostic performance to both conventionally used and state-of-the-art methods.
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“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 [199]
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An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012
Mazurowski, M. A., Buda, M., Saha, A., & Bashir, M. R. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26534 · Cited by 554
Seo, H., Badiei Khuzani, M., Vasudevan, V., Huang, C., Ren, H., Xiao, R., Jia, X., & Xing, L. (2020) · Medical Physics · doi:10.1002/mp.13649 · Cited by 300
Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians
Lin, D. J., Johnson, P. M., Knöll, F., & Lui, Y. W. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27078 · Cited by 299
Metadata
- Authors
- Chaudhari, A. S., Fang, Z., Kogan, F., Wood, J., Stevens, K. J., Gibbons, E. K., Lee, J. H., Gold, G. E., & Hargreaves, B. A.
- Affiliations
- Stanford Medicine (US)
- LVIS Corporation Palo Alto California
- University of Utah (US)
- Stanford University (US)
- Corresponding author
- Chaudhari, Akshay S.
- ORCID
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2018-03-26
- Volume
- 80
- Issue
- 5
- Pages
- 2139-2154
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- PMC
- PMC6107420
- OpenAlex
- W2794977498
- Cited by
- 406
- References
- 52
- Citations by year
- 2026: 30 · 2025: 50 · 2024: 52 · 2023: 51 · 2022: 46 · 2021: 88 · 2020: 53 · 2019: 26 · 2018: 9
- FWCI
- 22.01
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Magnetic Resonance in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (R01 AR063643, R01 EB002524, K24 AR062068, P41 EB015891, N01‐AR‐2‐2258, N01‐AR‐2‐2259, N01‐AR‐2‐2260, N01‐AR‐2‐2261, N01‐AR‐2‐2262)
- GE Healthcare
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (AR062068, AR063643, AR22258, AR22259, AR22260, AR22261, AR22262)
- National Institute of Biomedical Imaging and Bioengineering (EB002524, EB015891)
- Thomas and Stacey Siebel Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- MRI super-resolution, knee MRI, musculoskeletal MRI, convolutional neural networks, Fourier interpolation, image quality, reader study, inter-reader reliability
- MeSH terms
- Deep Learning, Algorithms, Cartilage Diseases, Humans, Knee, Magnetic Resonance Imaging, Pilot Projects, Phantoms, Imaging, Osteoarthritis, Knee, Imaging, Three-Dimensional, Signal-To-Noise Ratio
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