标题： 双输入动态卷积用于正电子发射断层扫描图像重建中的正电子射程校正 显示英文标题

标题： Dual-Input Dynamic Convolution for Positron Range Correction in PET Image Reconstruction

摘要： 正电子射程（PR）模糊会降低正电子发射断层扫描（PET）图像的分辨率，尤其是对于高能发射体如镓-68（68Ga）来说更为明显。 我们引入了双输入动态卷积（DDConv），这是一种计算效率高的新方法，通过蒙特卡罗（MC）模拟获得的体素特定PR点扩散函数（PSFs）进行训练，并设计用于在迭代重建算法中执行PR校正（PRC）。 通过训练好的卷积神经网络（CNN）动态推断局部模糊内核，DDConv比之前的方法更准确地捕捉复杂的组织界面。 至关重要的是，它还计算PR算子的转置，确保在迭代PET重建中的一致性。 与一种最先进的、依赖于组织的校正方法进行比较，证实了DDConv在恢复异质区域（包括骨-软组织和肺-软组织边界）的高分辨率细节方面的优势。 在数字体模、MC模拟数据和患者扫描中的实验验证表明，DDConv通过GPU加速的卷积保持了临床实用性，在显著减少计算时间的同时提供了接近MC的准确性。 这些结果突显了DDConv作为PET成像常规工具的潜力，提高了分辨率和保真度，而不会对重建资源提出过高的要求。 显示英文摘要

摘要： Positron range (PR) blurring degrades positron emission tomography (PET) image resolution, particularly for high-energy emitters like gallium-68 (68Ga). We introduce Dual-Input Dynamic Convolution (DDConv), a novel computationally efficient approach trained with voxel-specific PR point spread functions (PSFs) from Monte Carlo (MC) simulations and designed to be utilized within an iterative reconstruction algorithm to perform PR correction (PRC). By dynamically inferring local blurring kernels through a trained convolutional neural network (CNN), DDConv captures complex tissue interfaces more accurately than prior methods. Crucially, it also computes the transpose of the PR operator, ensuring consistency within iterative PET reconstruction. Comparisons with a state-of-the-art, tissue-dependent correction confirm the advantages of DDConv in recovering higher-resolution details in heterogeneous regions, including bone-soft tissue and lung-soft tissue boundaries. Experiments across digital phantoms, MC-simulated data, and patient scans verify that DDConv remains clinically practical through GPU-accelerated convolutions, offering near-MC accuracy while significantly reducing computation times. These results underline DDConv's potential as a routine tool in PET imaging, improving both resolution and fidelity without placing excessive demands on reconstruction resources.