Prior knowledge driven machine learning approach for PET sinogram data denoising

Siming Lu; Jiaxing Tan; Yongfeng Gao; Yongyi Shi; Zhengrong Liang

doi:10.1117/12.2549900

16 March 2020 Prior knowledge driven machine learning approach for PET sinogram data denoising

Siming Lu, Jiaxing Tan, Yongfeng Gao, Yongyi Shi, Zhengrong Liang

Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 113124A (2020) https://doi.org/10.1117/12.2549900
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Machine learning, especially convolutional neural network (CNN) approach has been successfully applied in noise suppression in natural image. However, shifting from natural image to medical image filed remains challenging due to specific difficulties such as training samples limitation, clinically meaningful image quality requirement and so on. To address this challenge, one possible solution is to incorporate our human prior knowledge into the machine learning model to better benefit its power. Therefore, in this work, we propose one prior knowledge driven machine learning based approach for positron emission tomography (PET) sinogram data denoising. Two main properties of PET sinogram data were considered in CNN architecture design, which are the Poisson statistics of the data and different correlation strength in the detector and view directions. Specially, for the statistical property, the sparse non-local method was used. For the correlation property, separate convolution was applied in two directions respectively. Experimental results showed the proposed model outperform the CNN model without prior knowledge. Results also demonstrate our insight of applying human knowledge strength the power of machine learning in medical imaging field.

Citation Download Citation

Siming Lu, Jiaxing Tan, Yongfeng Gao, Yongyi Shi, and Zhengrong Liang "Prior knowledge driven machine learning approach for PET sinogram data denoising", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113124A (16 March 2020); https://doi.org/10.1117/12.2549900

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