Artificial neural network for myelin water imaging. Issue 5 (31st October 2019)

• Record Type: Journal Article
• Authors: Lee, Jieun; Lee, Doohee; Choi, Joon Yul; Shin, Dongmyung; Shin, Hyeong‐Geol; Lee, Jongho
• Is Part Of: Magnetic resonance in medicine. Volume 83:Issue 5(2020)
• Page Start: 1875
• Page End: 1883
• Publication Date: 2019-10-31
• Subjects: artificial neural network -- multi‐echo gradient and spin echo -- multiple sclerosis -- myelin water imaging -- T2 distribution; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.28038
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: To demonstrate the application of artificial neural network (ANN) for real‐time processing of myelin water imaging (MWI). Methods: Three neural networks, ANN‐IMWF, ANN‐IGMT2, and ANN‐II, were developed to generate MWI. ANN‐IMWF and ANN‐IGMT2 were designed to output myelin water fraction (MWF) and geometric mean T2 of intra‐ and extra‐cellular water signal (GMT2, IEW ), respectively, whereas ANN‐II generates a T2 distribution. For the networks, gradient and spin echo data from 18 healthy controls (HC) and 26 multiple sclerosis patients (MS) were utilized. Among them, 10 HC and 12 MS had the same scan parameters and were used for training (6 HC and 6 MS), validation (1 HC and 1 MS), and test sets (3 HC and 5 MS). The remaining data had different scan parameters and were applied to exam effects of the scan parameters. The network results were compared with those of conventional MWI in the white matter mask and regions of interest. Results: The networks produced highly accurate results, showing averaged normalized root‐mean‐squared error under 3% for MWF and 0.4% for GMT2, IEW in the white matter mask of the test set. In the region of interest analysis, the differences between ANNs and conventional MWI were less than 0.1% in MWF and 0.1 ms in GMT2, IEW (no statistical difference and R 2 > 0.97). Datasets with different scan parameters showed increased errors. The average processing time was 0.68 s in ANNs, gaining 11, 702 times acceleration in the computational speed (conventional MWI: 7, 958 s). Conclusion: The proposed neural networks demonstrate the feasibility of real‐time processing for MWI with high accuracy.