Automatic detection of motion artifacts on MRI using Deep CNN

Motion artifacts on brain Magnetic Resonance Images (MRI) constitute an important factor that degrades the image quality, impacting the quantitative analysis based on structural segmentation. Thus, assessing the image quality is essential to determine if the image fulfills the minimal quality level necessary to the research analysis. Nowadays an MR expert, responsible for quality control, performs a visual check on every acquired image. The MRI database is huge, and this quality screening is time-consuming and fatiguing. We propose to automatically detect the images containing motion artifacts using Deep Convolutional Neural Networks (CNN), currently presenting the best performance on image classification contests. Four renowned architectures were chosen to be fine-tuned, and have their results combined to report the motion artifacts presence on the acquisition. Besides, as Deep CNN filters from lower layers map to smaller regions in the original input and our goal is to detect fine-grained image corruption, the CNNs were adapted to use the output from lower intermediate level as features to the binary classifier. The adapted CNNs were trained and tested using an annotated dataset composed of MRI T1-weighted volumetric acquisitions. The training subset contains 48 images, while the testing subset has 20 images. The method consists of two steps. Firstly CNNs were trained using patches from the three MRI planes (sagittal, axial and coronal). Secondly, the results from the patches are combined to provide the result per acquisition. On the second step, an Artificial Neural Network (ANN) classifier was trained combining the patches results from the four modified Deep CNNs and the patch location information. The overall performance on the test set was 88.27% per patch and 100% per acquisition. The proposed technique can be applied to large datasets, providing, to the quality control expert, the motion artifact presence probability, minimizing the time spent on manual quality control.