In vivo assessment of liver function, perfusion and fat distribution in the animal model by using MRI

在研究单位QuaLiPerF的总体目标中，获得数据集成，多尺度理解肝灌注与功能及其在扩展肝切除术（ePHx）后恢复之间的相互作用，该项目致力于对大鼠进行实验性MRI研究。旨在建立一个全面的9.4T小动物磁共振成像工具箱，提供各种实验条件下肝脏大小、功能、灌注等空间分辨率、多参数的体内定量信息。该工具箱将应用于大鼠门静脉结扎术和肝部分切除术前后的扫描。在再生过程中对动物进行纵向监测。此外，还将纵向研究脂肪饮食对用于表征肝脏状况的不同MRI参数的影响。获得的MRI数据用于监测和分析饮食、PVL后和ePHx后残肝再生过程中引起的生理适应。与研究单位内其他项目提供的补充实验和临床数据一起，它们形成了与数学模型预测的时间变化进行比较的重要信息来源，这些模型也在研究单位的相应项目中开发。在这个项目中收集的数据将被传递到基于统计方法的不同尺度的模型中。在研究单位的第一个资助期完成后，将获得大量的体内实验数据，再加上迭代计算建模和数据集成，将能够更好地预测肝功能的恢复。

Within the overarching goal of the research unit QuaLiPerF to gain a data-integrated, multi-scale understanding of the interplay between hepatic perfusion and function and its recovery after extended hepatectomy (ePHx), this project is devoted to experimental MRI investigations in rats. It aims to establish a comprehensive MR imaging toolbox for small animal imaging at 9.4T and to provide spatially resolved, multi-parametric quantitative in vivo information on liver size, function, and perfusion under various experimental conditions. The toolbox will be applied to scan rats before and after portal vein ligation (PVL) and partial hepatectomy. Animals are monitored longitudinally during regeneration. In addition, the effects of a fat diet on the different MRI parameters, used to characterize the liver status, will also be investigated longitudinally. The acquired MRI data serve to monitor and analyze the physiological adaptations induced by the diet, after PVL and during regeneration of the remnant liver after ePHx. Together with complementary experimental and clinical data contributed by the other projects within the research unit, they form an important source of information for comparison with the temporal changes predicted by the mathematical models, which are also developed in the corresponding projects of the research unit. The data collected in this project will be passed on to be integrated into models on different scales based on statistical approaches. Upon completion of the first funding period of the research unit, a substantial body of experimental in vivo data will be available, which together with the iterative computational modeling and data integration will allow a better prediction of the recovery of liver function.