Küstner Thomas, Liebgott Annika, Mauch Lukas, Martirosian Petros, Bamberg Fabian, Nikolaou Konstantin, Yang Bin, Schick Fritz, Gatidis Sergios
University of Stuttgart, Institute of Signal Processing and System Theory, Stuttgart, Germany.
Department of Radiology, University of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany.
MAGMA. 2018 Apr;31(2):243-256. doi: 10.1007/s10334-017-0650-z. Epub 2017 Sep 20.
Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture.
T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation. Using these patches as input data, a convolutional neural network (CNN) was trained to derive probability maps for the presence of motion artifacts. A deep visualization offers a human-interpretable quality control of the trained CNN. Results were visually assessed on probability maps and as classification accuracy on a per-patch, per-slice and per-volunteer basis.
On visual assessment, a clear difference of probability maps was observed between data sets with and without motion. The overall accuracy of motion detection on a per-patch/per-volunteer basis reached 97%/100% in the head and 75%/100% in the abdomen, respectively.
Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.
我们的目标是提供一种自动方法，用于在头部和腹部的磁共振成像（MRI）中对运动伪影进行空间分辨检测和量化，并对训练好的架构进行质量控制。
在16名健康志愿者处于静息和运动状态下采集头部和上腹部的T1加权MRI图像。图像被分割成不同大小的重叠小块以实现空间分离。以这些小块作为输入数据，训练一个卷积神经网络（CNN）来得出存在运动伪影的概率图。深度可视化提供了对训练好的CNN的可人工解释的质量控制。结果在概率图上进行视觉评估，并以每个小块、每个切片和每个志愿者的分类准确率进行评估。
在视觉评估中，观察到有运动和无运动的数据集之间概率图存在明显差异。在头部，基于每个小块/每个志愿者的运动检测总体准确率分别达到97%/100%，在腹部为75%/100%。
在MRI中自动检测运动伪影在头部和腹部具有良好的准确性，是可行的。所提出的方法提供了伪影的量化和定位以及所学内容的可视化。它可以扩展到其他解剖区域，并用于MR图像的质量保证。
