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

项目总结 定量动态对比增强(DCE)MRI指标，如组织灌注率、运动 参数、血管外体积和血浆体积可以描述组织中的细微差异 神经和心血管疾病中与缺血、血管、炎症和纤维化相关的状态， 以及在胰腺癌(PDAC)等癌症中。定量成像的可重复性 这使得它比传统的“定性”成像更适合于多中心或纵向研究。 已证明，定量DCE指标对于风险评估、早期检测、分期、 PDAC和其他疾病的特征、特征和治疗监测。 DCE MRI在注射基于Gd的造影剂之前、期间和之后进行成像。 有几个主要的挑战，特别是在移动器官方面：i)心脏运动必须为心脏处理 扫描通常通过同步采集和心电信号进行，这也给心律失常患者带来困难 由于成像效率低和对全心3D成像的挑战；ii)必须处理呼吸运动， 通常是通过患者屏息；iii)围绕Gd造影剂的安全性问题降低了风险效益 在许多情况下的比率。 该项目的目标是开发低剂量、运动分辨率、定量的动态对比 用于PDAC的增强(DCE)MRI。这将通过开发和验证MR多任务来实现 用于多动态、高时间分辨率T1映射的框架，并将MRI测量与组织学相关联 在接受手术切除的患者中。多任务处理围绕图像的概念设计DCE MRI 多个时间维度的函数，每个对应于不同的动态过程(例如，运动，T1， DCE)。它集成了机器学习、低阶张量建模、压缩感知和深度学习 即使在自由呼吸的条件下，也可以从6D DCE图像中提取可重现的定量测量结果。 由此产生的技术将成为定量描述运动中的MRI组织特征的有力工具 作为胰腺切除前监测新辅助治疗的一种工具，该方法具有很好的应用前景。

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.