Rapid Motion-Robust and Easy-to-Use Dynamic Contrast-Enhanced MRI for Liver Perfusion Quantification

项目摘要 该应用程序的主要目标是开发一种快速运动-强大且易于使用的动态对比度- 增强磁共振成像（DCE-MRI）框架，用于肝脏灌注定量，并评估 它在肝细胞癌（HCC）定量评估中的表现， 肝脏恶性肿瘤动态对比增强磁共振成像（DCE-MRI）， 基于钆的造影剂目前是用于识别和表征肝脏病变的基础， 包括HCC。然而，目前肝脏DCE-MRI的临床应用仅限于对模式的视觉评估 3-4个多相图像（动脉、静脉和延迟相）中的灌注，并且这些图像是常规的 是在多次屏气中获得的DCE-MRI还具有定量评估灌注的潜力 动力学，可以为非侵入性表征提供对肿瘤微环境的更深入了解 肿瘤的不同组织学特征，如肿瘤血管生成和侵袭性。这是 尤其与HCC相关，HCC通常基于成像诊断而无病理学证实 侵入性活检不幸的是，传统的肝脏灌注MRI技术受到许多缺陷的影响。 限制其临床实施的重要局限性，包括（1）成像速度慢，（2）有限 时空分辨率，（3）对运动伪影的灵敏度，以及（4）耗时的定量灌注 分析.同时，需要预对比T1标测将MR信号转换为钆浓度 进一步使已经麻烦的成像工作流程复杂化。这些挑战和潜在的复杂性 都导致了肝脏灌注MRI的不可再现性能，并显著降低了其 最终的临床效用。在这个项目中，我们建议开发新的快速MRI技术， 运动鲁棒采样策略和先进的重建模型来应对这些挑战。新 成像技术将实现运动鲁棒的3D T1映射，覆盖整个肝脏，以进行有效的估计 对比剂浓度和自由呼吸DCE-MRI的肝脏具有高时空分辨率。我们将 还将最先进的方法纳入深度学习，以进一步提高成像性能， 重建时间，并大大简化灌注定量。这些新的技术发展 将被整合到一个新的肝脏灌注MRI框架中，该框架将被转化为临床环境， 在探索性临床研究中评估HCC。总的假设是，随着新的成像 在该项目中开发的框架，强大的高时空分辨率灌注MRI的肝脏可以 在自由呼吸下实现，并且可以在没有用户的情况下执行肝脏灌注的绝对量化。 互动考虑到美国HCC发病率的迅速上升和巨大的负担， 该项目完成后，将在改进特征描述方面取得重大进展， 治疗HCC和其他具有高临床影响的肝脏疾病。

Project Summary The broad objective of this application is to develop a rapid motion-robust and easy-to-use dynamic contrast- enhanced magnetic resonance imaging (DCE-MRI) framework for liver perfusion quantification and to evaluate its performance in quantitative assessment of hepatocellular carcinoma (HCC), the most prevalent primary malignancy in the liver. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using gadolinium-based contrast agents is currently a cornerstone for identifying and characterizing hepatic lesions, including HCC. However, the current clinical use of liver DCE-MRI is limited to visual assessment of the pattern of perfusion in 3-4 multiphasic images (arterial, venous and delayed phases), and these images are routinely acquired during multiple breath holds. DCE-MRI also has the potential for quantitative assessment of perfusion kinetics, which can provide a deeper insight into the tumor microenvironment for non-invasive characterization of different histological features of the tumor, such as tumor angiogenesis and aggressiveness. This is particularly relevant for HCC, which is typically diagnosed based on imaging without pathological confirmation from invasive biopsy. Unfortunately, conventional liver perfusion MRI techniques suffer from a number of important limitations that restrict its clinical implementation, including (1) slow imaging speed, (2) limited spatiotemporal resolution, (3) sensitivity to motion artifacts, and (4) time-consuming quantitative perfusion analysis. Meanwhile, the need for pre-contrast T1 mapping to convert MR signal to gadolinium concentration further complicates the already-cumbersome imaging workflow. These challenges and underlying complexity have all led to non-reproducible performance of liver perfusion MRI and have significantly diminished its ultimate clinical utility. In this project, we propose to develop new rapid MRI techniques combining novel motion-robust sampling strategies and advanced reconstruction models to address these challenges. The new imaging techniques will enable motion-robust 3D T1 mapping with whole-liver coverage for efficient estimation of contrast concentration and free-breathing DCE-MRI of the liver with high spatiotemporal resolution. We will also incorporate state-of-the-art methods in deep learning to further improve imaging performance, to reduce reconstruction time, and to substantially simplify perfusion quantification. These new technical developments will be integrated into a new liver perfusion MRI framework, which will be translated into the clinical setting for assessment of HCC in an exploratory clinical study. The overall hypothesis is that with the new imaging framework developed in the project, robust high spatiotemporal resolution perfusion MRI of the liver can be achieved under free breathing, and absolute quantification of liver perfusion can be performed without user- interaction. Given the rapidly rising incidence and substantial burden of HCC in the United States, successful completion of this project would enable significant progress towards improved characterization and management of HCC and other liver diseases with high clinical impact.