A deep learning method for eliminating head motion artifacts in computed tomography. Issue 1 (10th December 2021)

• Record Type: Journal Article
• Authors: Su, Bin; Wen, Yuting; Liu, Yanyan; Liao, Shu; Fu, Jianwei; Quan, Guotao; Li, Zhenlin
• Is Part Of: Medical physics. Volume 49:Issue 1(2022)
• Page Start: 411
• Page End: 419
• Publication Date: 2021-12-10
• Subjects: CT -- image reconstruction -- machine learning; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15354
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: Involuntary patient movement results in data discontinuities during computed tomography (CT) scans which lead to a serious degradation in the image quality. In this paper, we specifically address artifacts induced by patient motion during a head scan. Method: Instead of trying to solve an inverse problem, we developed a motion simulation algorithm to synthesize images with motion‐induced artifacts. The artifacts induced by rotation, translation, oscillation and any possible combination are considered. Taking advantage of the powerful learning ability of neural networks, we designed a novel 3D network structure with both a large reception field and a high image resolution to map the artifact‐free images from artifact‐contaminated images. Quantitative results of the proposed method were evaluated against the results of U‐Net and proposed networks without dilation structure. Thirty sets of motion contaminated images from two hospitals were selected to do a clinical evaluation. Result: Facilitating the training dataset with artifacts induced by variable motion patterns and the neural network, the artifact can be removed with good performance. Validation dataset with simulated random motion pattern showed outperformed image correction, and quantitative results showed the proposed network had the lowest normalized root‐mean‐square error, highest peak signal‐to‐noise ratio and structure similarity, indicating our network gave the best approximation of gold standard. Clinical image processing results further confirmed the effectiveness of our method. Conclusion: We proposed a novel deep learning‐based algorithm to eliminate motion artifacts. The convolutional neural networks trained with synthesized image pairs achieved promising results in artifacts reduction. The corrected images increased the diagnostic confidence compared with artifacts contaminated images. We believe that the correction method can restore the ability to successfully diagnose and avoid repeated CT scans in certain clinical circumstances.