Fully Quantitative Low-Dose, Motion-Resolved Dynamic Contrast-Enhanced MRI in Pancreatic Adenocarcinoma

项目摘要 定量动态对比增强（DCE）MRI指标，如组织灌注率、动力学 参数、血管外体积和血浆体积允许表征组织中的细微差异 与神经和心血管疾病中的缺血、血管分布、炎症和纤维化相关的状态， 以及在癌症如胰腺腺癌（PDAC）中。定量成像的可重复性 使其比传统的“定性”成像更适合于多中心或纵向研究。 定量DCE指标已被证明对于风险评估、早期检测、分期 PDAC和其他疾病的表征和治疗监测。 DCE MRI在注射钆（Gd）造影剂之前、期间和之后进行成像。 有几个主要的挑战，特别是在运动器官：i）心脏运动必须处理心脏 扫描，通常通过与ECG信号同步采集，也导致心律失常患者的困难 因为低成像效率和对整个心脏3D成像的挑战; ii）必须处理呼吸运动， 通常通过患者屏气; iii）围绕Gd造影剂的安全性问题降低了获益风险比 在很多情况下，比例。 本项目的目标是开发低剂量、运动分辨、定量的动态对比 PDAC的增强MRI（DCE）。这将通过开发和验证MR多任务来实现 多动态、高时间分辨率T1标测的框架，并将MRI测量与组织学相关联 在接受手术切除的患者中。多任务设计DCE MRI围绕图像的概念， 多个时间维度的函数，每个时间维度对应于不同的动态过程（例如，运动，T1， DCE）。它集成了机器学习、低秩张量建模、压缩感知和深度学习， 从6D DCE图像中提取可重复的定量测量值，即使在自由呼吸条件下也是如此。 由此产生的技术将是一个强大的工具，定量MRI组织定征在移动 器官，并将有望作为胰腺切除术前监测新辅助治疗的工具。

PROJECT SUMMARY Quantitative dynamic contrast enhancement (DCE) MRI metrics such as tissue perfusion rates, kinetic parameters, extravascular volume, and plasma volume allow characterization of subtle differences in tissue states related to ischemia, vascularity, inflammation, and fibrosis in neurological and cardiovascular diseases, and in cancers such as pancreatic adenocardinoma (PDAC). The reproducible nature of quantitative imaging makes it more suitable for multi-center or longitudinal studies than conventional “qualitative” imaging. Quantitative DCE metrics have been shown to be important for risk assessment, early detection, staging, characterization, and treatment monitoring of PDAC and other diseases. DCE MRI performs imaging before, during, and after injection of a gadolinium (Gd)-based contrast agent. There are several major challenges, especially in moving organs: i) cardiac motion must be dealt with for heart scans, generally by syncing acquisition with an ECG signal, leading to difficulty in arrhythmia patients as well as low imaging efficiency and challenges for whole-heart 3D imaging; ii) respiratory motion must be dealt with, typically by patient breath-holding; iii) safety questions surrounding Gd contrast agents lower the benefit-to-risk ratio in many situations. The objective of this project is to develop low-dose, motion-resolved, quantitative dynamic contrast enhanced (DCE) MRI for PDAC. This will be accomplished by developing and validating the MR multitasking framework for multi-dynamic, highly time-resolved T1 mapping, and correlating MRI measurements to histology in patients undergoing surgical resection. Multitasking designs DCE MRI around the concept of images as functions of multiple time dimensions, each corresponding to a different dynamic process (e.g., motion, T1, DCE). It integrates machine learning, low-rank tensor modeling, compressed sensing, and deep learning to extract reproducible, quantitative measurements from 6D DCE images, even under free-breathing conditions. The resulting technology would be a powerful tool for quantitative MRI tissue characterization in moving organs, and would be promising as a tool for monitoring neoadjuvant therapies prior to pancreatic resection.