Personalized Task-Based Respiratory Motion Correction for Low-Dose PET/CT

项目摘要 PET在癌症管理中发挥着重要作用。然而，图像模糊和失配衰减 由于呼吸运动引起的校正可以显著降低检测效率和量化精度 用于肺部和腹部的肿瘤现有的运动校正方法可以提供令人满意的结果。 结果显示，呼吸规律的患者约占患者的60%。但对于 其余40%的患者呼吸模式不规则，这些方法忽略了主要影响， 由于周期间和周期内运动变化而引起的门内运动。此外，由于剂量减少， PET成像已经变得越来越重要，现有的运动校正方法通常放大图像， 噪声并降低它们在低计数数据上的性能。另一个重要的挑战是 限制了使用 单层螺旋CT。因此，为了实现对癌症治疗反应的准确定量评价， 使用低剂量PET协议进行可靠的肿瘤检测，特别是对于具有呼吸模式的患者 包括可变运动幅度、基线变化和幅度变化在内的变化， 开发针对个体患者呼吸模式优化的个性化运动校正策略 以及消除门内运动和失配衰减校正的成像任务， 剂量PET。扩展我们现有的合作，耶鲁大学和西门子形成了一个理想的团队，以优化 利用固有相位匹配衰减校正呼吸模式可变性的综合解决方案 在前两个目标中对规则和不规则呼吸器进行校正。然后我们将开发和翻译一个 个性化的策略，自动识别最具时效性的运动校正方法 个体患者，考虑任务和呼吸模式。我们将优化我们的个性化运动校正 方法和策略，特别是低计数PET数据，旨在将辐射剂量降低到25%-50%， 目前PET方案中的剂量。这项研究的成果将是一个全面的运动校正 包，包括四种校正方法和个性化策略，自动优化， 每一个病人。这一发展将准备转化为商业PET/CT扫描仪， 临床最终用户。由于现有的运动校正方法仅适用于~60%的常规呼吸，但是 对于剩余的~40%的不规则呼吸器有很大的限制，我们建议的发展可以 为所有规则和不规则运动的患者提供统一的运动校正框架 呼吸了这种与工业合作伙伴的快速翻译可以带来重要而及时的临床 影响癌症管理。

Project Abstract PET plays an important role in cancer management. However, image blurring and mismatched attenuation correction due to respiratory motion can substantially degrade detection efficacy and quantification accuracy for tumors located in the lung and abdomen. Existing motion correction methods might provide satisfactory results for patients with regular breathing patterns, which account for about 60% of patients. However, for the remaining 40% of patients with irregular breathing patterns, these methods neglect the major effects of intra-gate motion due to inter-cycle and intra-cycle motion variations. In addition, as dose reduction in PET imaging has become increasingly important, existing motion correction methods typically amplify image noise and degrade their performances on low-count data. Another important challenge is the mismatch between CT and PET that limits phase-matched attenuation correction for every gated PET image using a single helical CT. Therefore, to achieve accurate quantification for evaluation of response to cancer therapy and reliable detection of tumors using low-dose PET protocols, particularly for patients with breathing pattern changes including variable motion amplitude, baseline variation, and amplitude variation, it is critical to develop personalized motion correction strategies optimized for individual patient's breathing patterns and the imaging task to eliminate intra-gate motion and mismatched attenuation correction for low- dose PET. Extending our existing collaboration, Yale and Siemens form an ideal team to optimize a comprehensive solution to correct for breathing pattern variability with intrinsically phase-matched attenuation correction for both regular and irregular breathers in the first two Aims. We will then develop and translate a personalized strategy to automatically identify the most time-effective motion correction approach for each individual patient, considering task and breathing pattern. We will optimize our personalized motion correction methods and strategy particularly for low-count PET data, aiming to reduce radiation dose to 25%-50% of the dose in current PET protocols. The outcome of this research will be a comprehensive motion correction package including four correction approaches and a personalized strategy that is automatically optimized for each individual patient. This development will be ready to translate to commercial PET/CT scanners and clinical end-users. As existing motion correction methods only apply to ~60% regular breathers, but have substantial limitation for the remaining ~40% irregular breathers, our proposed development can provide a unified motion correction framework for all patients with both regular and irregular breathing. This fast translation with industrial partners can lead to a significant and timely clinical impact for cancer management.