QC-Automator: Deep Learning-Based Automated Quality Control for Diffusion MR Images.
Autor: | Samani ZR; Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States., Alappatt JA; Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States., Parker D; Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States., Ismail AAO; Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States., Verma R; Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States. |
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Jazyk: | angličtina |
Zdroj: | Frontiers in neuroscience [Front Neurosci] 2020 Jan 22; Vol. 13, pp. 1456. Date of Electronic Publication: 2020 Jan 22 (Print Publication: 2019). |
DOI: | 10.3389/fnins.2019.01456 |
Abstrakt: | Quality assessment of diffusion MRI (dMRI) data is essential prior to any analysis, so that appropriate pre-processing can be used to improve data quality and ensure that the presence of MRI artifacts do not affect the results of subsequent image analysis. Manual quality assessment of the data is subjective, possibly error-prone, and infeasible, especially considering the growing number of consortium-like studies, underlining the need for automation of the process. In this paper, we have developed a deep-learning-based automated quality control (QC) tool, QC-Automator , for dMRI data, that can handle a variety of artifacts such as motion, multiband interleaving, ghosting, susceptibility, herringbone, and chemical shifts. QC-Automator uses convolutional neural networks along with transfer learning to train the automated artifact detection on a labeled dataset of ∼332,000 slices of dMRI data, from 155 unique subjects and 5 scanners with different dMRI acquisitions, achieving a 98% accuracy in detecting artifacts. The method is fast and paves the way for efficient and effective artifact detection in large datasets. It is also demonstrated to be replicable on other datasets with different acquisition parameters. (Copyright © 2020 Samani, Alappatt, Parker, Ismail and Verma.) |
Databáze: | MEDLINE |
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