Computational Models of Perfusion Circuits for Organ Transplantation

需要对诊断和治疗技术进行临床前研究，以便在用于人类环境之前探索、测试和验证新方法。动物研究的结果经常被用作人体解剖学的模型，尽管在某些情况下生理学有很大的不同。同样，基于对缺乏含氧血液的担忧，来自离体人体组织实验的结果的相关性可能会受到质疑。在某些情况下，来自临床前模型的不正确假设被纳入人体试验，影响其价值。本博士论文将研究和开发器官灌注技术，该技术可以结合先进的传感和成像以及用于推断器官状态和改变灌注系统特性的计算模型。将使用灌注液（血液或人工灌注液）采样研究组织监测，以分析器官功能的生化标志物。在线光学传感器将能够通过安装外部成像和传感技术的接入端口连续监测氧合水平。外部安装的摄像机和组织标记将允许使用视觉绘制器官表面，并且该系统将用于开发新模型，该新模型可以通过训练系统配对外部和内部感测信息来推断组织特性，而无需介入传感器。

Preclinical research of diagnostic and therapeutic technologies is required to explore, test and validate new methodologies before use in the human setting. Results from animal studies are often used as a model of human anatomy despite vastly different physiology in some cases. Similarly, the relevance of results derived from ex vivo human tissue experiments may be questioned based on concerns over the lack of oxygenated blood. In some cases, incorrect assumptions, derived from preclinical models, are incorporated into human trials impacting their value. This doctorate thesis will research and develop organ perfusion technology that can incorporate advanced sensing and imaging as well as computational models for inferring organ state and altering the perfusion system characteristics. Tissue monitoring will be investigated using sampling of perfusate (blood or artificial perfusion solution) in order to analyse biochemical markers of organ function. In-line optical sensors will enable continuous monitoring of oxygenation levels with access ports for mounting of external imaging and sensing technologies. Externally mounted cameras and tissue markers will allow mapping of the organ's surface using vision and the system will be used to develop new models than can infer tissue properties without interventional sensors by training to systems paring the extrinsic and internal sensing information.