用端到端深度学习提升伽马相机成像质量，减少伪影和系统误差。
SwinCCIR: An end-to-end deep network for Compton camera imaging reconstruction
- 基于Swin Transformer与反卷积生成模块，直接从事件数据重建图像
- 在模拟与实测数据上均显著降低伪影，提升图像清晰度
- 适合需要高精度成像的核医学、辐射探测等场景
康普顿相机（CCs）通过康普顿散射原理确定入射伽马射线的方向，但其重建依赖于康普顿锥的反投影，易产生严重伪影和形变。此外，设备性能导致的部分系统误差难以通过校准消除，进一步恶化成像质量。尽管迭代算法与基于深度学习的方法已被广泛采用，但多数仍基于反投影结果进行优化。为此，本文提出一种端到端深度学习框架SwinCCIR，采用Swin-Transformer块与基于反卷积的图像生成模块，建立列表模式事件与放射源分布之间的映射关系。SwinCCIR在模拟与实际数据集上完成训练与验证。实验表明，该方法有效克服了传统康普顿成像中的伪影与失真问题，具备实际应用前景。
原文摘要 · Abstract (English)
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.
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