• Kanghyun Ryu, Yoonho Nam, Sung-Min Gho, Jinhee Jang, Ho-Joon Lee, Jihoon Cha, Hye Jin Baek, Jiyong Park, and Dong-Hyun Kim.
• Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
• J Magn Reson Imaging. 2019 Nov 1; 50 (5): 1413-1423.

BackgroundFLAIR (fluid attenuated inversion recovery) imaging via synthetic MRI methods leads to artifacts in the brain, which can cause diagnostic limitations. The main sources of the artifacts are attributed to the partial volume effect and flow, which are difficult to correct by analytical modeling. In this study, a deep learning (DL)-based synthetic FLAIR method was developed, which does not require analytical modeling of the signal.PurposeTo correct artifacts in synthetic FLAIR using a DL method.Study TypeRetrospective.SubjectsA total of 80 subjects with clinical indications (60.6 ± 16.7 years, 38 males, 42 females) were divided into three groups: a training set (56 subjects, 62.1 ± 14.8 years, 25 males, 31 females), a validation set (1 subject, 62 years, male), and the testing set (23 subjects, 57.3 ± 20.4 years, 13 males, 10 females).Field Strength/Sequence3 T MRI using a multiple-dynamic multiple-echo acquisition (MDME) sequence for synthetic MRI and a conventional FLAIR sequence.AssessmentNormalized root mean square (NRMSE) and structural similarity (SSIM) were computed for uncorrected synthetic FLAIR and DL-corrected FLAIR. In addition, three neuroradiologists scored the three FLAIR datasets blindly, evaluating image quality and artifacts for sulci/periventricular and intraventricular/cistern space regions.Statistical TestsPairwise Student's t-tests and a Wilcoxon test were performed.ResultsFor quantitative assessment, NRMSE improved from 4.2% to 2.9% (P < 0.0001) and SSIM improved from 0.85 to 0.93 (P < 0.0001). Additionally, NRMSE values significantly improved from 1.58% to 1.26% (P < 0.001), 3.1% to 1.5% (P < 0.0001), and 2.7% to 1.4% (P < 0.0001) in white matter, gray matter, and cerebral spinal fluid (CSF) regions, respectively, when using DL-corrected FLAIR. For qualitative assessment, DL correction achieved improved overall quality, fewer artifacts in sulci and periventricular regions, and in intraventricular and cistern space regions.Data ConclusionThe DL approach provides a promising method to correct artifacts in synthetic FLAIR.Level Of Evidence4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1413-1423.© 2019 International Society for Magnetic Resonance in Medicine.

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