Literature DB >> 35014901

Synthetic FLAIR as a Substitute for FLAIR Sequence in Acute Ischemic Stroke.

Joseph Benzakoun1, Marc-Antoine Deslys1, Laurence Legrand1, Ghazi Hmeydia1, Guillaume Turc1, Wagih Ben Hassen1, Sylvain Charron1, Clément Debacker1, Olivier Naggara1, Jean-Claude Baron1, Bertrand Thirion1, Catherine Oppenheim1.   

Abstract

Background In acute ischemic stroke (AIS), fluid-attenuated inversion recovery (FLAIR) is used for treatment decisions when onset time is unknown. Synthetic FLAIR could be generated with deep learning from information embedded in diffusion-weighted imaging (DWI) and could replace acquired FLAIR sequence (real FLAIR) and shorten MRI duration. Purpose To compare performance of synthetic and real FLAIR for DWI-FLAIR mismatch estimation and identification of patients presenting within 4.5 hours from symptom onset. Materials and Methods In this retrospective study, all pretreatment and early follow-up (<48 hours after symptom onset) MRI data sets including DWI (b = 0-1000 sec/mm2) and FLAIR sequences obtained in consecutive patients with AIS referred for reperfusion therapies between January 2002 and May 2019 were included. On the training set (80%), a generative adversarial network was trained to produce synthetic FLAIR with DWI as input. On the test set (20%), synthetic FLAIR was computed without real FLAIR knowledge. The DWI-FLAIR mismatch was evaluated on both FLAIR data sets by four independent readers. Interobserver reproducibility and DWI-FLAIR mismatch concordance between synthetic and real FLAIR were evaluated with κ statistics. Sensitivity and specificity for identification of AIS within 4.5 hours were compared in patients with known onset time by using McNemar test. Results The study included 1416 MRI scans (861 patients; median age, 71 years [interquartile range, 57-81 years]; 375 men), yielding 1134 and 282 scans for training and test sets, respectively. Regarding DWI-FLAIR mismatch, interobserver reproducibility was substantial for real and synthetic FLAIR (κ = 0.80 [95% CI: 0.74, 0.87] and 0.80 [95% CI: 0.74, 0.87], respectively). After consensus, concordance between real and synthetic FLAIR was almost perfect (κ = 0.88; 95% CI: 0.82, 0.93). Diagnostic value for identifying AIS within 4.5 hours did not differ between real and synthetic FLAIR (sensitivity: 107 of 131 [82%] vs 111 of 131 [85%], P = .2; specificity: 96 of 104 [92%] vs 96 of 104 [92%], respectively, P > .99). Conclusion Synthetic fluid-attenuated inversion recovery (FLAIR) had diagnostic performances similar to real FLAIR in depicting diffusion-weighted imaging-FLAIR mismatch and in helping to identify early acute ischemic stroke, and it may accelerate MRI protocols. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Carroll and Hurley in this issue.

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Year:  2022        PMID: 35014901     DOI: 10.1148/radiol.211394

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  2 in total

1.  When "Deep Faking" Results Means "Improving Diagnosis".

Authors:  Timothy J Carroll; Michael C Hurley
Journal:  Radiology       Date:  2022-01-11       Impact factor: 29.146

2.  Research Highlight: Use of Generative Images Created with Artificial Intelligence for Brain Tumor Imaging.

Authors:  Ji Eun Park; Philipp Vollmuth; Namkug Kim; Ho Sung Kim
Journal:  Korean J Radiol       Date:  2022-04-04       Impact factor: 7.109

  2 in total

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