Good quality of medical images is a prerequisite for the success of
subsequent image analysis pipelines. Quality assessment of medical images is
therefore an essential activity and for large population studies such as the UK
Biobank (UKBB), manual identification of artefacts such as those caused by
unanticipated motion is tedious and time-consuming. Therefore, there is an
urgent need for automatic image quality assessment techniques. In this paper,
we propose a method to automatically detect the presence of motion-related
artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep
learning architectures to classify poor quality CMR images: 1) 3D
spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent
Convolutional Network (LRCN). Though in real clinical setup motion artefacts
are common, high-quality imaging of UKBB, which comprises cross-sectional
population data of volunteers who do not necessarily have health problems
creates a highly imbalanced classification problem. Due to the high number of
good quality images compared to the relatively low number of images with motion
artefacts, we propose a novel data augmentation scheme based on synthetic
artefact creation in k-space. We also investigate a learning approach using a
predetermined curriculum based on synthetic artefact severity. We evaluate our
pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images.
The LRCN architecture outperformed the 3D-CNN architecture and was able to
detect 2D+time short axis images with motion artefacts in less than 1ms with
high recall. We compare our approach to a range of state-of-the-art quality
assessment methods. The novel data augmentation and curriculum learning
approaches both improved classification performance achieving overall area
under the ROC curve of 0.89.

本研究提出了基于深度学习的方法，通过数据增强技术和学习曲线，自动检测心脏磁共振心动图像（CMR）中与运动相关的伪影，该方法在 UK Biobank 数据集上取得了 0.89 ROC 曲线下区域的高分类性能