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

基于任务的个性化低剂量 PET/CT 呼吸运动校正

基本信息

批准号：
10436864
负责人：
Chi Liu
金额：
$ 62.82万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-02 至 2024-06-30
项目状态：
已结题

项目摘要

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.

项目摘要宠物在癌症管理中起着重要作用。但是，图像模糊和不匹配的衰减呼吸运动引起的校正可以实质上降低检测功效和定量精度适用于肺和腹部的肿瘤。现有的运动校正方法可能会提供令人满意的定期呼吸模式的患者的结果，约占患者的60％。但是，对于剩下的40％的患有不规则呼吸模式的患者忽略了由于周期间和周期内运动变化而导致的门内运动。另外，随着剂量减少宠物成像变得越来越重要，现有运动校正方法通常会放大图像噪音并降低其在低计数数据上的表现。另一个重要的挑战是不匹配在CT和PET之间，使用A限制每个封闭式PET图像相匹配的衰减校正单螺旋CT。因此，要获得准确的量化以评估对癌症治疗的反应使用低剂量PET方案对肿瘤的可靠检测，特别是对于呼吸模式的患者变化包括可变运动振幅，基线变化和振幅变化，至关重要制定针对个别患者的呼吸模式优化的个性化运动校正策略以及消除闸内运动和不匹配的衰减校正的成像任务剂量宠物。扩展我们现有的合作，耶鲁大学和西门子组成了一个理想的团队，以优化综合解决方案以校正呼吸模式可变性，并具有内在相匹配的衰减在前两个目标中，对常规呼吸器和不规则呼吸器的校正。然后，我们将开发和翻译个性化的策略，自动确定每种及时有效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更正方法和策略尤其是针对低计数宠物数据的方法，旨在将辐射剂量降低至25％-50％当前宠物方案中的剂量。这项研究的结果将是全面的运动校正包含四种更正方法和一个个性化策略，该策略自动优化了每个患者。这一发展将准备转化为商业宠物/CT扫描仪，临床最终用户。由于现有运动校正方法仅适用于约60％的常规呼吸器，但对于剩余的约40％的不规则呼吸器有很大的限制，我们提出的开发可以为所有规则和不规则的患者提供统一的运动校正框架呼吸。与工业合作伙伴的这种快速翻译可以导致大量及时的临床对癌症管理的影响。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

MCP-Net: Introducing Patlak Loss Optimization to Whole-body Dynamic PET Inter-frame Motion Correction.

MCP-Net：将 Patlak 损失优化引入全身动态 PET 帧间运动校正。

DOI：
10.1109/tmi.2023.3290003
发表时间：
2023
期刊：
IEEE transactions on medical imaging
影响因子：
10.6
作者：
Guo,Xueqi;Zhou,Bo;Chen,Xiongchao;Chen,Ming-Kai;Liu,Chi;Dvornek,NichaC
通讯作者：
Dvornek,NichaC

TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction.

TAI-GAN：用于动态心脏 PET 运动校正中早期到晚期帧转换的时间和解剖学信息 GAN。

DOI：
10.1007/978-3-031-44689-4_7
发表时间：
2023
期刊：
Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)
影响因子：
0
作者：
Guo,Xueqi;Shi,Luyao;Chen,Xiongchao;Zhou,Bo;Liu,Qiong;Xie,Huidong;Liu,Yi-Hwa;Palyo,Richard;Miller,EdwardJ;Sinusas,AlbertJ;Spottiswoode,Bruce;Liu,Chi;Dvornek,NichaC
通讯作者：
Dvornek,NichaC

Patient motion correction for dynamic cardiac PET: Current status and challenges.

动态心脏 PET 的患者运动校正：现状和挑战。