SwinCCIR: An end-to-end deep network for Compton camera imaging reconstruction
This work addresses imaging quality deterioration in Compton cameras for gamma-ray detection, representing an incremental improvement over existing methods.
The paper tackled the problem of severe artifacts and deformation in Compton camera imaging reconstruction by proposing an end-to-end deep learning framework called SwinCCIR, which effectively overcomes these issues as validated on simulated and practical datasets.
Compton cameras (CCs) are a kind of gamma cameras which are designed to determine the directions of incident gammas based on the Compton scatter. However, the reconstruction of CCs face problems of severe artifacts and deformation due to the fundamental reconstruction principle of back-projection of Compton cones. Besides, a part of systematic errors originated from the performance of devices are hard to remove through calibration, leading to deterioration of imaging quality. Iterative algorithms and deep-learning based methods have been widely used to improve reconstruction. But most of them are optimization based on the results of back-projection. Therefore, we proposed an end-to-end deep learning framework, SwinCCIR, for CC imaging. Through adopting swin-transformer blocks and a transposed convolution-based image generation module, we established the relationship between the list-mode events and the radioactive source distribution. SwinCCIR was trained and validated on both simulated and practical dataset. The experimental results indicate that SwinCCIR effectively overcomes problems of conventional CC imaging, which are expected to be implemented in practical applications.