Rapid motion-robust quantitative DCE-MRI for the assessment of gynecologic cancers

项目总结 妇科癌症是影响女性的最致命的疾病之一。全球范围内，有一名妇女死于 每两分钟就得一次宫颈癌。磁共振成像越来越多地应用于妇科和其他许多疾病的评估 癌症。除了已确定的癌症分期用途外，人们对使用核磁共振成像一直很感兴趣 定量测量，以获得对肿瘤微环境的洞察。从以下位置获得的参数贴图 动态增强(DCE)MRI数据的量化可用于研究肿瘤的血管和识别 更好的血流和充氧的肿瘤，因此对某些治疗更敏感，如 化疗和放射治疗。然而，目前的MRI成像速度和运动灵敏度相对较低 技术导致DCE-MRI数据的不可靠和不可重现的量化，这限制了其 在临床上的应用。 我们的团队在开发快速抗动DCE-MRI技术方面处于世界领先地位，尤其是使用 径向成像和压缩传感的组合。我们开发了一种名为GRAPH的技术，它 被构思为学术和产业合作伙伴关系，现在已经成功地转化为标准 临床实践。虽然功能强大，但第一代GRAPH也有局限性。首先，径向成像是稳健的 运动，但不是自由运动，这通常会导致模糊。其次，GRAPH使用非常简单的稀疏化 转换为压缩感知，这可能会带来量化问题。第三，抓手不是 最初是为药代动力学分析而开发的，错过了重要的成分，如AIF的整合 估计和T1映射。第四，图像重建时间仍然很长--大约几分钟。 我们已经开发了新的进展来绕过这些限制，并提供了一种新的DCE-MRI技术 提高速度、运动阻力和个性化的AIF估计和药代动力学T1映射 分析。遵循PAR-18-009指导方针，我们的主要目标是形成学术和产业合作伙伴关系。 在纪念斯隆·凯特琳癌症中心和通用电气医疗中心之间翻译这些新的 用于妇科和其他类型癌症患者的定量DCE-MRI的进展。特定的 目标如下： 1.开发并实现了一种超越掌握的快速运动抵抗定量DCE-MRI技术 提供更高的速度和运动阻力；动态T1映射；以及个性化和自动化 药代动力学分析 2.评估快速运动的重复性、再现性和初步的肿瘤反应评估- 稳健的定量DCE-MRI技术(“DCE-NEW”)并将DCE-NEW与标准护理DCE-MRI进行比较 (“DCE标准”)在妇科癌症患者中的应用 3.开发和评估基于深度学习的快速图像重建算法

PROJECT SUMMARY Gynecologic cancers are some of the most lethal diseases affecting women. Globally, one woman dies of cervical cancer every two minutes. MRI is increasingly used in the evaluation of gynecologic and many other cancers. Beyond its established use for cancer staging, there has long been an interest in the use of MRI-derived quantitative metrics to gain insights into the tumor microenvironment. Parametric maps obtained from quantification of dynamic contrast enhanced (DCE) MRI data can be used to study tumor vascularity and identify tumors that are better perfused and oxygenated and thus more sensitive to some treatments such as chemotherapy and radiation. However, the relative slow imaging speed and motion sensitivity of current MRI technology results in non-reliable and non-reproducible quantification of DCE-MRI data, which restricts its application in clinical practice. Our group is a world leader in development of rapid motion-resistant DCE-MRI techniques, in particular using combinations of radial imaging and compressed sensing. We developed the technique called GRASP, which was conceived as an academic-industrial partnership and has now been successfully translated into standard clinical practice. Though powerful, the first generation of GRASP has limitations. First, radial imaging is robust to motion, but not free of motion, which usually results in blurring. Second, GRASP uses a very simple sparsifying transform for compressed sensing, which can introduce issues with quantification. Third, GRASP was not originally developed for pharmacokinetic analysis and misses important ingredients such as integration of AIF estimation and T1 mapping. Fourth, image reconstruction time is still very long – in the order of several minutes. We have developed new advances to circumvent these limitations and offer a new DCE-MRI technique with increased speed, motion-resistance and personalized AIF estimation and T1 mapping for pharmacokinetic analysis. Following the PAR-18-009 guidelines, our main goal is to form an academic-industrial partnership between Memorial Sloan Kettering Cancer Center and General Electric Healthcare to translate these new developments in quantitative DCE-MRI for use in patients with gynecologic and other type of cancers. Specific Aims are as follows: 1. Develop and implement a fast motion-resistant quantitative DCE-MRI technique that goes beyond GRASP to offer increased speed and resistance to motion; dynamic T1 mapping; and personalized and automated pharmacokinetic analysis 2. Evaluate the repeatability, reproducibility and preliminary tumor response assessment of the fast motion- robust quantitative DCE-MRI technique (“DCE-new”) and compare DCE-new to standard of care DCE-MRI (“DCE-standard”) in patients with gynecologic cancer 3. Develop and evaluate fast image reconstruction algorithms based on deep learning