Correction of Motion Artifacts Using a Multiscale Fully Convolutional Neural Network

Fellows’ Journal Club

The authors implement and validate an MRI motion-artifact correction method using a multiscale fully convolutional neural network. Application of the network resulted in notably improved image quality without the loss of morphologic information. For synthetic test data, the average reduction in mean squared error was 41.84%. The blinded reader study on the real-world test data resulted in significant reduction in mean artifact scores across all cases.

Abstract

Architecture of the used Foveal fully convolutional neural network. The input image was split into different patches (indicated by red box), and each patch was processed in conjunction with larger, down-sampled patches at the same location (blue and green boxes). The size of the patches was chosen to account for the loss of border pixels in every convolutional layer. Each feature-extraction path consisted of 2 layers, each comprising a convolutional layer (C), batch normalization (B), and a rectified linear unit (R) activation. Feature integration was realized using average unpooling (U) and convolutional layers. Kernel sizes and the number of channels are denoted as k and n, respectively. The output was the estimate of the motion artifacts by the network for the selected image patch.

BACKGROUND AND PURPOSE

Motion artifacts are a frequent source of image degradation in the clinical application of MR imaging (MRI). Here we implement and validate an MRI motion-artifact correction method using a multiscale fully convolutional neural network.

MATERIALS AND METHODS

The network was trained to identify motion artifacts in axial T2-weighted spin-echo images of the brain. Using an extensive data augmentation scheme and a motion artifact simulation pipeline, we created a synthetic training dataset of 93,600 images based on only 16 artifact-free clinical MRI cases. A blinded reader study using a unique test dataset of 28 additional clinical MRI cases with real patient motion was conducted to evaluate the performance of the network.

RESULTS

Application of the network resulted in notably improved image quality without the loss of morphologic information. For synthetic test data, the average reduction in mean squared error was 41.84%. The blinded reader study on the real-world test data resulted in significant reduction in mean artifact scores across all cases (P < .03).

CONCLUSIONS

Retrospective correction of motion artifacts using a multiscale fully convolutional network is promising and may mitigate the substantial motion-related problems in the clinical MRI workflow.

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