A convolutional neural network for Y90 SPECT/CT scatter estimation - Phan , Viet Cuong; Ho , Thi Thao; Le , Tuan Anh; Nguyen , Duc Ton; Nguyen , Dinh Khai; Nguyen , Hong Ha (Nuclear Science and Technology, 2021)
Monte Carlo-based scatter modeling in SPECT has demonstrated the ability on improving image quality and quantitative accuracy but high computational cost. In this study, we describe a deep learning method based on a convolutional neural network (CNN) to increase the image quality, decrease the computation time for SPECT/CT reconstruction. Monte Carlo (MC) simulation and true scatter data are used for training and validation phase and the CNN network is trained to match the MC scatter estimation. In the testing step with a liver subject, visual image quality by CNN was better than MC scatter estimation method. Besides, the CNN scatter estimate was generated over a much shorter period of time than MC model (about 15 seconds for CNN vs ~2 hours for MC). The short processing time with CNN while maintaining quality has high clinical significance for quantitative SPECT imaging.