Deep-Learning Generated Synthetic Double Inversion Recovery Images Improve Multiple Sclerosis Lesion Detection.

Finck, Tom; Li, Hongwei; Grundl, Lioba; Eichinger, Paul; Bussas, Matthias; Mühlau, Mark; Menze, Bjoern; Wiestler, Benedikt

doi:10.1097/RLI.0000000000000640

2020

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Dokumenttyp:: Journal Article
Autor(en):: Finck, Tom; Li, Hongwei; Grundl, Lioba; Eichinger, Paul; Bussas, Matthias; Mühlau, Mark; Menze, Bjoern; Wiestler, Benedikt
Titel:: Deep-Learning Generated Synthetic Double Inversion Recovery Images Improve Multiple Sclerosis Lesion Detection.
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.
Zeitschriftentitel:: Invest Radiol
Jahr:: 2020
Band / Volume:: 55
Heft / Issue:: 5
Seitenangaben Beitrag:: 318-323
Volltext / DOI:: doi:10.1097/RLI.0000000000000640
PubMed:: http://view.ncbi.nlm.nih.gov/pubmed/31977602
Print-ISSN:: 0020-9996
TUM Einrichtung:: Fachgebiet Neuroradiologie (Prof. Zimmer); Neurologische Klinik und Poliklinik
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