Utility of deep learning super‐resolution in the context of osteoarthritis MRI biomarkers. Issue 3 (16th July 2019)

• Record Type: Journal Article
• Authors: Chaudhari, Akshay S.; Stevens, Kathryn J.; Wood, Jeff P.; Chakraborty, Amit K.; Gibbons, Eric K.; Fang, Zhongnan; Desai, Arjun D.; Lee, Jin Hyung; Gold, Garry E.; Hargreaves, Brian A.
• Is Part Of: Journal of magnetic resonance imaging. Volume 51:Issue 3(2020)
• Page Start: 768
• Page End: 779
• Publication Date: 2019-07-16
• Subjects: super‐resolution -- artificial intelligence -- machine learning interpretability -- osteoarthritis biomarkers -- image acceleration -- cartilage segmentation; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.26872
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Super‐resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown. Purpose: To evaluate MRI super‐resolution using quantitative and qualitative metrics of cartilage morphometry, osteophyte detection, and global image blurring. Study Type: Retrospective. Population: In all, 176 MRI studies of subjects at varying stages of osteoarthritis. Field Strength/Sequence: Original‐resolution 3D double‐echo steady‐state (DESS) and DESS with 3× thicker slices retrospectively enhanced using super‐resolution and tricubic interpolation (TCI) at 3T. Assessment: A quantitative comparison of femoral cartilage morphometry was performed for the original‐resolution DESS, the super‐resolution, and the TCI scans in 17 subjects. A reader study by three musculoskeletal radiologists assessed cartilage image quality, overall image sharpness, and osteophytes incidence in all three sets of scans. A referenceless blurring metric evaluated blurring in all three image dimensions for the three sets of scans. Statistical Tests: Mann–Whitney U ‐tests compared Dice coefficients (DC) of segmentation accuracy for the DESS, super‐resolution, and TCI images, along with the image quality readings and blurring metrics. Sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals compared osteophyte detection for the super‐resolution and TCI images, with the original‐resolution as a reference. Results: DC for the original‐resolution (90.2 ± 1.7%) and super‐resolution (89.6 ± 2.0%) were significantly higher ( P < 0.001) than TCI (86.3 ± 5.6%). Segmentation overlap of super‐resolution with the original‐resolution (DC = 97.6 ± 0.7%) was significantly higher ( P < 0.0001) than TCI overlap (DC = 95.0 ± 1.1%). Cartilage image quality for sharpness and contrast levels, and the through‐plane quantitative blur factor for super‐resolution images, was significantly ( P < 0.001) better than TCI. Super‐resolution osteophyte detection sensitivity of 80% (76–82%), specificity of 93% (92–94%), and DOR of 32 (22–46) was significantly higher ( P < 0.001) than TCI sensitivity of 73% (69–76%), specificity of 90% (89–91%), and DOR of 17 (13–22). Data Conclusion: Super‐resolution appears to consistently outperform naïve interpolation and may improve image quality without biasing quantitative biomarkers. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:768–779.