Motion correction in MR image for analysis of VSRAD using generative adversarial network.

Abstract

Voxel-based specific region analysis systems for Alzheimer’s disease (VSRAD) are clinically used to measure the atrophied hippocampus captured by magnetic resonance imaging (MRI). However, motion artifacts during acquisition of images may distort the results of the analysis. This study aims to evaluate the usefulness of the Pix2Pix network in motion correction for the input image of VSRAD analysis. Seventy-three patients examined with MRI were distinguished into the training group (n = 51) and the test group (n = 22). To create artifact images, the k-space images were manipulated. Supervised deep learning was employed to obtain a Pix2Pix that generates motion-corrected images, with artifact images as the input data and original images as the reference data. The results of the VSRAD analysis (severity of voxel of interest (VOI) atrophy, the extent of gray matter (GM) atrophy, and extent of VOI atrophy) were recorded for artifact images and motion-corrected images, and were then compared with the original images. For comparison, the image quality of Pix2Pix generated motion-corrected image was also compared with that of U-Net. The Bland-Altman analysis showed that the mean of the limits of agreement was smaller for the motion-corrected images compared to the artifact images, suggesting successful motion correction by the Pix2Pix. The Spearman’s rank correlation coefficients between original and motion-corrected images were almost perfect for all results (severity of VOI atrophy: 0.87–0.99, extent of GM atrophy: 0.88–00.98, extent of VOI atrophy: 0.90–1.00). Pix2Pix generated motion-corrected images that showed generally improved quantitative and qualitative image qualities compared with the U-net generated motion-corrected images. Our findings suggest that motion correction using Pix2Pix is a useful method for VSRAD analysis.