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

Abstract

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