论文标题
使用带有闸门的一致性学习的暹罗对抗网络对低剂量门控宠物的同时降解和运动估计
Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning
论文作者
论文摘要
门控通常用于PET成像中，以减少呼吸运动模糊并促进更复杂的运动校正方法。然而，在低剂量PET的应用中，减少注射剂量会导致噪声增加并降低信噪比（SNR），随后损坏运动估计/校正步骤，从而导致较低的图像质量。为了解决这些问题，我们首先提出了一个暹罗对抗网络（SAN），该网络可以有效地从低剂量门控图像体积中有效恢复高剂量的门控图像量。为了确保恢复的封闭式体积之间的外观一致性，然后我们利用纳入SAN的预训练的运动估计网络，从而实现了栅极到门口（G2G）一致性的约束。通过恢复高质量的封闭式体积，可以同时从运动估计网络中输出栅极到门的运动向量。对29名受试者的低剂量门控PET数据集的全面评估表明，我们的方法可以有效地恢复低剂量的门控PET量，平均PSNR为37.16，SSIM为0.97，同时产生可靠的运动估计，从而使后续运动纠正受益。
Gating is commonly used in PET imaging to reduce respiratory motion blurring and facilitate more sophisticated motion correction methods. In the applications of low dose PET, however, reducing injection dose causes increased noise and reduces signal-to-noise ratio (SNR), subsequently corrupting the motion estimation/correction steps, causing inferior image quality. To tackle these issues, we first propose a Siamese adversarial network (SAN) that can efficiently recover high dose gated image volume from low dose gated image volume. To ensure the appearance consistency between the recovered gated volumes, we then utilize a pre-trained motion estimation network incorporated into SAN that enables the constraint of gate-to-gate (G2G) consistency. With high-quality recovered gated volumes, gate-to-gate motion vectors can be simultaneously outputted from the motion estimation network. Comprehensive evaluations on a low dose gated PET dataset of 29 subjects demonstrate that our method can effectively recover the low dose gated PET volumes, with an average PSNR of 37.16 and SSIM of 0.97, and simultaneously generate robust motion estimation that could benefit subsequent motion corrections.
