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10 March 2020 Estimating standard-dose PET from low-dose PET with deep learning
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Abstract
Oncology PET protocols utilize administered activities ranging between 10-15 mCi and 2-3 min/bed positions to obtain diagnostic quality PET images. There is a continued desire to reduce administered activity to reduce radiation exposure and to decrease scanning time to improve patient comfort. However, reducing those parameters lowers photon counts and degrades quantification accuracy. In this work, we proposed a deep-learning-based method to estimate the diagnostic PET image from low count data. A cycle-consistent generative adversarial network (Cycle GAN) was introduced to capture the relationship from low count to full count PET images while simultaneously supervising an inverse full count to low count (full-to-low) transformation model. The network simultaneously makes itself better at both creating synthetic full count PET images and learns how to identify full count PET images. Residual blocks were integrated into the models to catch the differences between low count and full count PET in the training dataset and better handle noise. The proposed model was implemented and evaluated on whole-body FDG PET images with only 1/8th counts. The proposed method obtained the average mean error and normalized mean square error in the whole body of 0.14%±1.43% and 0.52%±0.19%. Normalized cross-correlation was increased to 0.996, and the peak signal-to-noise ratio is increased to 46.0 dB with the proposed method. We developed a deep learning-based approach to accurately estimate diagnostic quality PET datasets from one-eighth of photons, with great potential to substantially reduce the administered dose or scan duration while maintaining high diagnostic quality.
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Yang Lei, Xue Dong, Tonghe Wang, Kristin Higgins, Tian Liu, Walter J. Curran, Hui Mao, Jonathan A. Nye, and Xiaofeng Yang "Estimating standard-dose PET from low-dose PET with deep learning", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131322 (10 March 2020); https://doi.org/10.1117/12.2548461
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