ArticlePloS one2022
Motion correction in MR image for analysis of VSRAD using generative adversarial network.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Intra-MRI Head Motion Tracking and Correction: A Quantitative In Vivo Evaluation Framework.NMR in biomedicine · 2026Article
- Deep learning approach to super-resolution correction of brain MRI motion artifacts for accurate hippocampal volumetry.Scientific reports · 2026Article
- Statistical Parametric Mapping and Voxel-Based Specific Regional Analysis System for Alzheimer's Disease (VSRAD): Principles and Clinical Applications.Brain sciences · 2025Review
- Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction.ArXiv · 2025Article
- Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images.Radiological physics and technology · 2025Article
- Reducing the acquisition time for magnetic resonance imaging using super-resolution image generation and evaluating the accuracy of hippocampal volumes for diagnosing Alzheimer's disease.Frontiers in neurology · 2025Article
- Voxel-based Specific Regional Analysis System for Alzheimer’s Disease and Arterial Spin Labeling in Brain Magnetic Resonance Imaging: A Comparative StudyCurrent medical imaging · 2025Article
- An in-silico simulation study to generate computed tomography images from ultrasound data by using deep learning techniques.BJR artificial intelligence · 2025Article
- Iterative Motion Correction Technique with Deep Learning Reconstruction for Brain MRI: A Volunteer and Patient Study.Journal of imaging informatics in medicine · 2024Article
- Simulating rigid head motion artifacts on brain magnitude MRI data-Outcome on image quality and segmentation of the cerebral cortex.PloS one · 2024Article
- Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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