Deep-Learning Generated Synthetic Double Inversion Recovery Images Improve Multiple Sclerosis Lesion Detection. Issue 5 (May 2020)

• Record Type: Journal Article
• Authors: Finck, Tom; Li, Hongwei; Grundl, Lioba; Eichinger, Paul; Bussas, Matthias; Mühlau, Mark; Menze, Bjoern; Wiestler, Benedikt
• Is Part Of: Investigative radiology. Volume 55:Issue 5(2020)
• Publication Date: 2020-05
• Subjects: artificial intelligence -- deep learning -- generative adversarial networks -- multiple sclerosis -- double inversion recovery; Diagnosis, Radioscopic -- Periodicals; Radiology, Medical -- Periodicals; 616.0757
• DOI: 10.1097/RLI.0000000000000640
• Languages: English
• ISSNs: 0020-9996

Abstract

Abstract : Objectives: The aim of the study was to implement a deep-learning tool to produce synthetic double inversion recovery (synthDIR) images and compare their diagnostic performance to conventional sequences in patients with multiple sclerosis (MS). Materials and Methods: For this retrospective analysis, 100 MS patients (65 female, 37 [22–68] years) were randomly selected from a prospective observational cohort between 2014 and 2016. In a subset of 50 patients, an artificial neural network ( DiamondGAN ) was trained to generate a synthetic DIR (synthDIR) from standard acquisitions (T1, T2, and fluid-attenuated inversion recovery [FLAIR]). With the resulting network, synthDIR was generated for the remaining 50 subjects. These images as well as conventionally acquired DIR (trueDIR) and FLAIR images were assessed for MS lesions by 2 independent readers, blinded to the source of the DIR image. Lesion counts in the different modalities were compared using a Wilcoxon signed-rank test, and interrater analysis was performed. Contrast-to-noise ratios were compared for objective image quality. Results: Utilization of synthDIR allowed to detect significantly more lesions compared with the use of FLAIR images (31.4 ± 20.7 vs 22.8 ± 12.7, P < 0.001). This improvement was mainly attributable to an improved depiction of juxtacortical lesions (12.3 ± 10.8 vs 7.2 ± 5.6, P < 0.001). Interrater reliability was excellent in FLAIR 0.92 (95% confidence interval [CI], 0.85–0.95), synthDIR 0.93 (95% CI, 0.87–0.96), and trueDIR 0.95 (95% CI, 0.85–0.98). Contrast-to-noise ratio in synthDIR exceeded that of FLAIR (22.0 ± 6.4 vs 16.7 ± 3.6, P = 0.009); no significant difference was seen in comparison to trueDIR (22.0 ± 6.4 vs 22.4 ± 7.9, P = 0.87). Conclusions: Computationally generated DIR images improve lesion depiction compared with the use of standard modalities. This method demonstrates how artificial intelligence can help improving imaging in specific pathologies.