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
This project aims to establish an academic-industrial partnership between Memorial Sloan Kettering Cancer Center and General Electric Healthcare to develop and disseminate advances in dynamic contrast-enhanced (DCE) MRI for use in cancer patients. The new developments, which include radial imaging, compressed sensing, and deep learning, will deliver rapid motion-resistant DCE-MRI with high spatial and temporal resolution for more accurate and reproducible quantification of MRI-derived metrics. The new technology to be disseminated as a prototype on GE scanners will promote the use of quantitative DCE-MRI biomarkers in clinical practice, a long-desired goal.