PET image reconstruction from incomplete data, such as the gap between adjacent detector blocks generally introduces partial projection data loss, is an important and challenging problem in medical imaging. This work proposes an efficient convolutional neural network (CNN) framework, called GapFill-Recon Net, that jointly reconstructs PET images and their associated sinogram data. GapFill-Recon Net including two blocks: the Gap-Filling block first address the sinogram gap and the Image-Recon block maps the filled sinogram onto the final image directly. A total of 43,660 pairs of synthetic 2D PET sinograms with gaps and images generated from the MOBY phantom are utilized for network training, testing and validation. Whole-body mouse Monte Carlo (MC) simulated data are also used for evaluation. The experimental results show that the reconstructed image quality of GapFill-Recon Net outperforms filtered back-projection (FBP) and maximum likelihood expectation maximization (MLEM) in terms of the structural similarity index metric (SSIM), relative root mean squared error (rRMSE), and peak signal-to-noise ratio (PSNR). Moreover, the reconstruction speed is equivalent to that of FBP and was nearly 83 times faster than that of MLEM. In conclusion, compared with the traditional reconstruction algorithm, GapFill-Recon Net achieves relatively optimal performance in image quality and reconstruction speed, which effectively achieves a balance between efficiency and performance.

从不完整数据重建 PET 图像，例如相邻检测器块之间的间隙通常会引入部分投影数据丢失，是医学成像中一个重要且具有挑战性的问题。这项工作提出了一种高效的卷积神经网络 (CNN) 框架，称为 GapFill-Recon Net，可联合重建 PET 图像及其相关正弦图数据。GapFill-Recon Net 包括两个块：Gap-Filling 块首先解决正弦图间隙，Image-Recon 块将填充的正弦图直接映射到最终图像上。总共 43,660 对合成 2D PET 正弦图具有间隙和从 MOBY 模体生成的图像用于网络训练、测试和验证。全身小鼠蒙特卡罗 (MC) 模拟数据也用于评估。实验结果表明，GapFill-Recon Net的重建图像质量在结构相似性指标度量（SSIM）、相对均方根误差（rRMSE）方面优于滤波反投影（FBP）和最大似然期望最大化（MLEM）和峰值信噪比 (PSNR)。此外，重建速度与 FBP 相当，比 MLEM 快近 83 倍。综上所述，与传统的重建算法相比，GapFill-Recon Net在图像质量和重建速度上实现了相对最优的性能，有效地实现了效率和性能的平衡。