QC-Automator: Deep Learning-Based Automated Quality Control for Diffusion MR Images
QC-Automator: Deep Learning-Based Automated Quality Control for Diffusion MR Images
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DOI：
10.3389/fnins.2019.01456
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发表时间：
2020-01-22
影响因子：
4.3
通讯作者：
Verma, Ragini
中科院分区：
文献类型：
--
作者：
Samani, Zahra Riahi;Alappatt, Jacob Antony;Verma, Ragini
在进行任何分析之前，弥散MRI （dMRI）数据的质量评估是必不可少的，因此可以使用适当的预处理来提高数据质量，并确保MRI伪影的存在不影响后续图像分析的结果。人工对数据的质量评估是主观的，可能容易出错，而且不可行，特别是考虑到越来越多的类似财团的研究，强调了对过程自动化的需要。在本文中，我们开发了一种基于深度学习的自动化质量控制（QC）工具QC- automator，用于dMRI数据，可以处理各种伪影，如运动，多波段交错，重影，敏感性，人字和化学位移。QC-Automator使用卷积神经网络和迁移学习在标记数据集上训练自动伪影检测，该数据集类似于332,000片dMRI数据，来自155个独特的受试者和5台具有不同dMRI采集的扫描仪，检测伪影的准确率达到98%。该方法快速，为在大数据集中进行高效的伪影检测铺平了道路。它也被证明是可复制的其他数据集与不同的采集参数。
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 similar to 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.