Three-dimensional convolutional neural networks for simultaneous dual-tracer PET imaging.

In this study, we present a hybrid loss-guided deep learning based framework for dual-tracer PET imaging using sinogram data, which can achieve reconstruction by naturally unifying two functions: the reconstruction of the mixed images and the separation of each tracer. Combined with volumetric dual-tracer images, we adopted a three-dimensional (3D) convolutional neural network (CNN) to learn full features, including spatial information and temporal information simultaneously. In addition, an auxiliary loss layer was introduced to guide the reconstruction of the dual tracers. We used Monte Carlo simulations with data augmentation to generate sucient datasets for training and testing. The results were analyzed by the bias and variance both spatially (different regions of interest) and temporally (different frames). The analysis veried the feasibility of the 3D CNN framework for dual-tracer reconstruction. Furthermore, we compared the reconstruction results with a deep belief network (DBN), which is another deep learning-based technique for the separation of dual-tracer images based on time-activity curves (TACs). The comparison results provide insights into the superior features and performance of the 3D CNN. Furthermore, we tested the [<sup>11</sup>C]FMZ-
 [<sup>11</sup>C]DTBZ images with three total-counts levels (5 x 10<sup>6</sup>, ...

Source: Physics in Medicine and Biology - July 10, 2019 Category: Physics Authors: Xu J, Liu H Tags: Phys Med Biol Source Type: research