Reduction of motion artifacts in head CT exams using multi-scale convolutional neural network

Bin Su; Yanyan Liu; Jianwei Fu; Guotao Quan

doi:10.1117/12.2548899

10 March 2020 Reduction of motion artifacts in head CT exams using multi-scale convolutional neural network

Bin Su, Yanyan Liu, Jianwei Fu, Guotao Quan

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131324 (2020) https://doi.org/10.1117/12.2548899
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Patient motion during computed tomography (CT) scan can result in serious degradation of imaging quality, and is of increasing concern due to the aging population and associated diseases. In this paper, we address this problem by focusing on the reduction of head motion artifacts. To achieve this, we introduce a head motion simulation system and a multi-scale deep learning architecture. The proposed motion simulation system can simulate rigid movement including translation and rotation. The images with simulated motion serve as the training set for the network, and the original motion free images serve as the gold standard. Motion artifacts exhibit in the image space as streaks and patchy shadows. We propose a multiscale neural network to learn the artifact. With different branches equipped with ResBlock and down-sampling, the network can learn long scale streaks and short scale shadow artifacts. Although we trained the network on simulated images, we find that the learned network generalizes well to images with real motion artifacts.

Citation Download Citation

Bin Su, Yanyan Liu, Jianwei Fu, and Guotao Quan "Reduction of motion artifacts in head CT exams using multi-scale convolutional neural network", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131324 (10 March 2020); https://doi.org/10.1117/12.2548899

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KEYWORDS

Head

Convolution

Computed tomography

Convolutional neural networks

3D image processing

Neural networks

X-ray computed tomography

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