Self-trained deep convolutional neural network for noise reduction in CT

Zhongxing Zhou; Akitoshi Inoue; Cynthia H. McCollough; Lifeng Yu

doi:10.1117/12.2646717

17 October 2022 Self-trained deep convolutional neural network for noise reduction in CT

Zhongxing Zhou, Akitoshi Inoue, Cynthia H. McCollough, Lifeng Yu

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Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 123042B (2022) https://doi.org/10.1117/12.2646717
Event: Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), 2022, Baltimore, United States

Abstract

Supervised deep convolutional neural network (CNN)-based methods have been actively used in clinical CT to reduce image noise. The networks of these methods are typically trained using paired high- and low-quality data from a large number of patients and/or phantom images. This training process is tedious, and the network trained under a given condition may not be generalizable to patient images acquired and reconstructed at different conditions. In this paper, we propose a self-trained deep CNN (ST_CNN) method which does not rely on pre-existing training datasets. The training is accomplished using extensive data augmentation through projection domain and the inference is applied to the data itself. Preliminary evaluation on patient images demonstrated that the proposed method could achieve similar image quality in comparison with conventional deep CNN denoising methods pre-trained on external datasets.

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Zhongxing Zhou, Akitoshi Inoue, Cynthia H. McCollough, and Lifeng Yu "Self-trained deep convolutional neural network for noise reduction in CT", Proc. SPIE 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography, 123042B (17 October 2022); https://doi.org/10.1117/12.2646717

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