Imaging to inform models of whole organ behaviour in health and disease

1)研究背景的简要描述包括潜在影响伦敦大学学院的研究人员最近开发了一种方法，使用高能x射线创建高分辨率（约1微米）和三维的整个人体器官图像，称为HiP-CT。该方法的3D（和3D加时间）图像将用于模拟人体中的生物物理过程，从血液和空气流动到关节运动；允许对真实人体器官几何形状的动力学过程进行调查，分辨率比以前的研究高100倍。这些图像是由位于格勒诺布尔的欧洲同步辐射设施（ESRF）生成的。该x射线源提供了世界上最明亮、最相干的高能光束，允许以25um分辨率对整个人体器官（包括肺、心脏、大脑）进行成像，然后在不切割组织的情况下以~1.2um分辨率放大细胞结构。EPRSC的研究人员对健康和疾病中的人体器官进行了成像（Covid-19受害者，见https://mecheng.ucl.ac.uk/HiP-CT）。由科学家、x射线物理学家和医生组成的HiP-CT研究小组最近获得了陈-扎克伯格倡议的资助，以进一步发展这项技术，并将其应用于整个人体器官。这个博士项目将开发机器学习分割技术，然后使用分割的同步加速器数据作为血流预测模型、气流预测模型和其他传输过程（例如治疗药物的输送）的输入。根据研究人员的兴趣，关节运动模型也可能与机械加载过程中纳米应变的DVC测量相结合。这些模型将被用作从结构数据集推断生理学和病理生理学见解的工具。2)目的和目标具体目标是：使用来自各种器官的分段同步加速器数据作为输入计算模型：血流预测，气流预测，药物输送。利用这些模型，将从分割的结构信息中预测功能信息，为人体在健康和疾病（包括Covid-19）中的功能提供新的见解。3)研究方法的新颖性HiP-CT成像方法使人体器官几何成像的分辨率比以前的研究高100倍。在血流预测、气流预测和药物输送模型中使用这些高分辨率几何图形可以深入了解健康和疾病中的器官行为。4)与EPSRC的战略和研究领域保持一致该项目与EPSRC医疗技术主题的优先事项保持一致，特别是围绕开发新疗法和优化疾病预测、诊断和干预。博士项目汇集了最先进的成像，计算建模方法，以深入了解器官病理生理学，疾病进展和对治疗的反应。这些技术将通过开放科学计划共享，供活跃在相关领域的更广泛的社区采用。5)任何参与“陈-扎克伯格倡议”的公司或合作者

1) Brief description of the context of the research including potential impactUCL researchers have recently developed a method for using high-energy X-rays to create images of whole human organs at high resolution (ca. 1 um) and in three dimensions, called HiP-CT. The 3D (and 3D plus time) images from this method will be used to simulate biophysical processes in the human body from blood and air flow to joint movement; allowing investigations into dynamics processes with real human organ geometries with a resolution up to 100 times greater than prior studies. The images are generated using the European Synchrotron Radiation facility (ESRF) in Grenoble. This X-ray source offers the brightest and most coherent high energy beam in the world, allowing imaging of entire human organs (including lung, heart, brain) with 25um resolution, and then zoom in on cellular structures at ~1.2um resolution without cutting the tissue. EPRSC researchers have imaged human organs in health and disease (Covid-19 victims, see https://mecheng.ucl.ac.uk/HiP-CT). The HiP-CT research team of scientists, X-ray physicists and medics has recently received a Chan Zuckerberg Initiative grant to further develop this technique and apply it to whole human organs. This PhD project will develop ML segmentation techniques and then use the segmented synchrotron data as input for models of blood flow prediction, air flow prediction, and other transport processes (for example delivery of therapeutics). Models of joint motion are also possible, coupled to DVC measurement of nano-strains during mechanical loading, depending on the researcher's interests. The models will be used as a tool to infer insights about physiology and pathophysiology from the structural data sets.2) Aims and ObjectivesThe specific objectives are to: use segmented synchrotron data from a variety of organs as inputs computational models of: blood flow prediction, air flow prediction, drug delivery. Using these models, functional information will be predicted from segmented structural information, providing new insights into the function of the human body in health and disease (including Covid-19).3) Novelty of Research MethodologyThe HiP-CT imaging method enables imaging of human organ geometries with a resolution up to 100 times greater than prior studies. Using these high resolution geometries in models of blood flow prediction, air flow prediction and drug delivery could provide insights into organ behaviour in health and disease.4) Alignment to EPSRC's strategies and research areasThe project aligns with the priorities of the EPSRC Healthcare Technologies Theme, specifically around Developing New Therapies and Optimising Disease Prediction, Diagnosis and Intervention. The PhD project brings together state-of-the-art imaging, computational modelling methodologies to gain insight into organ pathophysiology, disease progression and response to therapy. The technologies will be shared through open science initiatives for take up by the broader communities active in related areas.5) Any companies or collaborators involvedChan Zuckerberg Initiative