Data‐driven synthetic MRI FLAIR artifact correction via deep neural network

• Published in: Journal of magnetic resonance imaging Vol. 50; no. 5; pp. 1413 - 1423
• Main Authors: Ryu, Kanghyun, Nam, Yoonho, Gho, Sung‐Min, Jang, Jinhee, Lee, Ho‐Joon, Cha, Jihoon, Baek, Hye Jin, Park, Jiyong, Kim, Dong‐Hyun
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.11.2019; Wiley Subscription Services, Inc
• Subjects: Artificial neural networks; Cerebrospinal fluid; convolutional neural network; Diagnostic systems; Females; Field strength; Image quality; Machine learning; Magnetic resonance imaging; Males; MDME; Medical imaging; Modelling; Neural networks; Neuroimaging; Statistical analysis; Statistical tests; Substantia alba; Substantia grisea; synthetic FLAIR artifact correction; synthetic MRI; MDME; synthetic MRI; synthetic FLAIR artifact correction; convolutional neural network
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.26712

Background FLAIR (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. Purpose To correct artifacts in synthetic FLAIR using a DL method. Study Type Retrospective. Subjects A 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/Sequence 3 T MRI using a multiple‐dynamic multiple‐echo acquisition (MDME) sequence for synthetic MRI and a conventional FLAIR sequence. Assessment Normalized 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 Tests Pairwise Student's t‐tests and a Wilcoxon test were performed. Results For 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 Conclusion The DL approach provides a promising method to correct artifacts in synthetic FLAIR. Level of Evidence: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1413–1423.