Deep learning based methods for gamma ray interaction location estimation in monolithic scintillation crystal detectors

In this work, we explore deep learning based techniques using the information from mean detector response functions (MDRFs) as a new method to estimate gamma ray interaction location in monolithic scintillation crystal detectors. Compared with searching based methods, deep learning techniques do not require recording all the MDRF information once the prediction networks are trained, which means the memory cost could be significantly reduced. In addition, the event positioning process using deep learning techniques only requires running through the network once, without the need to do searching in the reference dataset. This could greatly speed up the positioning process for each event. We have designed and trained four different neural networks to estimate the gamma ray interaction location given the MDRF data. We have studied network structures consisting only of fully connected (FC) layers, as well as Conv neural networks (CNNs). In addition, we tried to use both regression and classification to generate the final prediction of the gamma ray interaction position. We evaluated the estimation accuracy, testing speed and memory cost (numbers of parameters) of different network architectures, and also compared them with the exhaustive search method. Our results indicate that deep learning based estimation methods with a well designed network structure can achieve a relative positioning error with respect to the ground truth determined by the exhaustive search method of below 1 mm in both x and y directions (depth information is not considered in this work), which would imply a very high performance positioning algorithm for practical monolithic scintillation crystal detectors. The deep learning network also achieves a testing speed that is more than 400 times faster than the exhaustive search method. With proper design of the network structure, the deep learning based positioning methods have the potential to save memory cost by a factor of up to 100.