全身体图像到图像翻译用于医疗数字孪生中的虚拟扫描仪
Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin
AI总结：
本文提出了一种基于区域分割的GAN方法，用于全身体CT到PET的图像翻译，通过分割身体为四个区域并使用区域特定的GANs提升翻译精度，从而在医疗数字孪生中实现更准确的虚拟PET扫描。
摘要：
生成正电子发射断层扫描（PET）图像从计算机断层扫描（CT）扫描通过深度学习提供了一条有前景的途径，以减少与PET成像相关的辐射暴露和成本，从而改善患者护理和功能性成像的可及性。全身体图像翻译由于解剖异质性而面临挑战，通常限制了通用模型的性能。我们提出了一种框架，将全身体CT图像分割成四个区域-头部、躯干、手臂和腿部-并使用区域特定的生成对抗网络（GANs）进行定制的CT到PET翻译。每个区域的合成PET图像被拼接在一起以重建全身体扫描。与基线非分割GAN的比较以及与Pix2Pix和CycleGAN架构的实验测试了配对和非配对场景。在区域、全身体和病变层面的定量评估显示，我们的区域特定GANs在性能上有了显著的提升。Pix2Pix产生了更优的指标，确保了精确的高质量图像合成。通过解决解剖异质性，这种方法在全身体CT到PET翻译中实现了最先进的结果。该方法通过从CT数据中启用准确的虚拟PET扫描，支持医疗数字孪生，创建虚拟成像表示以监测、预测和优化健康结果。
英文摘要：
Generating positron emission tomography (PET) images from computed tomography (CT) scans via deep learning offers a promising pathway to reduce radiation exposure and costs associated with PET imaging, improving patient care and accessibility to functional imaging. Whole-body image translation presents challenges due to anatomical heterogeneity, often limiting generalized models. We propose a framework that segments whole-body CT images into four regions-head, trunk, arms, and legs-and uses district-specific Generative Adversarial Networks (GANs) for tailored CT-to-PET translation. Synthetic PET images from each region are stitched together to reconstruct the whole-body scan. Comparisons with a baseline non-segmented GAN and experiments with Pix2Pix and CycleGAN architectures tested paired and unpaired scenarios. Quantitative evaluations at district, whole-body, and lesion levels demonstrated significant improvements with our district-specific GANs. Pix2Pix yielded superior metrics, ensuring precise, high-quality image synthesis. By addressing anatomical heterogeneity, this approach achieves state-of-the-art results in whole-body CT-to-PET translation. This methodology supports healthcare Digital Twins by enabling accurate virtual PET scans from CT data, creating virtual imaging representations to monitor, predict, and optimize health outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
