Deep Learning guided Imaging to correlate imaging from a whole organ to cellular level

分层相衬断层扫描（HiP-CT）是一种使用高能x射线开发的x射线技术，使我们能够以高分辨率（约1微米）和三维方式创建整个人体器官的图像。这使我们能够更好地了解人体的复杂结构和功能，以及更好地了解疾病引起的变化。我们的新成像技术类似于传统医学成像中广泛使用的x射线CT，但使用的是基于ESRF（格勒诺布尔欧洲同步辐射设施）的同步加速器x射线源。这种x射线源提供了世界上最明亮、最相干的光束，并且与我们正在开发的HIP-CT技术相结合，使我们能够以25微米的分辨率对整个人体器官（包括肺、心脏、大脑）进行成像，并在不切割组织的情况下以1.2微米的分辨率放大细胞结构。我们对健康和疾病中的人体器官进行了成像，包括Covid-19受害者。目标和目标具体目标是：开发和应用深度学习技术来分割HiP-CT数据（气道，血管，细胞等），以便绘制生物学见解并进一步进行生物物理模拟。探索更先进的机器学习技术，如生成对抗网络，以便将HiP-CT数据与其他模式（如组织学、光片、MRI和CT）的图像关联起来。这种类型的分析将能够更好地解释髋关节- ct，从而提供定量的生物学和医学见解。研究方法的新颖性与EPSRC的战略和研究领域保持一致任何公司或合作者参与该项目是UCL， ESRF和DLS的科学家和数学家以及汉诺威生物银行，美因茨和海德堡，UCL和伦敦帝国理工学院的临床医生之间的国际跨学科合作，以及许多其他合作者。

Brief description of the context of the research including potential impactHierarchical Phase-Contrast Tomography (HiP-CT) is an X-ray technique developed using high-energy X-rays, that allow us to create images of whole human organs at high resolution (ca. 1 um) and in three dimensions. This allows us to better understand the complex structure and function of the human body, as well as to better understand changes caused by disease. Our new imaging technique is similar to the X-ray CT used widely in conventional medical imaging but uses a synchrotron X-ray source based at the ESRF (European Synchrotron Radiation facility in Grenoble). This X-ray source offers the brightest and most coherent beam in the world, and, coupled to the HIP-CT technique we're developing, allows us to image entire human organs (including lung, heart, brain) with 25um resolution, and zoom in on cellular structures at ~1.2um resolution without cutting the tissue. We have imaged human organs in health and disease including Covid-19 victims.Aims and ObjectivesThe specific objectives are to:Develop and apply deep learning techniques to segment HiP-CT data (airways, blood vessels, cells, etc.) to enable biological insights to be drawn and for further biophysical simulations. Explore more advanced machine learning techniques such as generative adversarial networks, in order to correlate HiP-CT data with images from other modalities (such as histology, lightsheet, MRI and CT). This type of analysis will enable substantially better interpretation of HiP-CT so that it can provide quantitative biological and medical insights. Novelty of Research MethodologyAlignment to EPSRC's strategies and research areasAny companies or collaborators involvedThe project is an international interdisciplinary collaboration between scientists and mathematicians at UCL, ESRF and DLS, and clinicians at Hannover-biobank, Mainz and Heidelberg, UCL and Imperial College London, together with many other collaborators.