SwinCCIR: An end-to-end deep network for Compton camera imaging reconstruction

TL;DR

This paper introduces SwinCCIR, an end-to-end deep learning model using swin-transformer blocks for improved Compton camera imaging reconstruction, addressing artifacts and systematic errors in traditional methods.

Contribution

The paper presents a novel deep learning framework that directly maps list-mode events to source distribution, outperforming traditional iterative and back-projection based methods.

Findings

Effective reduction of artifacts and deformation in reconstructed images

Validated on both simulated and real datasets with promising results

Potential for practical application in gamma imaging systems

Abstract

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.