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

深度学习引导成像将整个器官的成像与细胞水平相关联

• 批准号: 2581659
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2581659
• 关键词: Deep; Learning; guided; Imaging; correlate

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.

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，它使我们能够以高分辨率（约1000像素）创建整个人体器官的图像。1 μ m）和三维。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，但使用的是位于格勒诺布尔的ESRF（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和最相干的光束，再加上我们正在开发的HIP-CT技术，使我们能够以25 um的分辨率对整个人体器官（包括肺，心脏，大脑）进行成像，并以约1.2 um的分辨率放大细胞结构而无需切割组织。我们对健康和疾病中的人体器官进行成像，包括Covid-19受害者。目的和目标具体目标是：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）。以使生物学见解能够被绘制并用于进一步的生物物理模拟。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）的图像相关联。这种类型的分析将能够更好地解释HiP-CT，从而提供定量生物学和医学见解。研究方法的新奇与EPSRC的战略和研究领域相一致任何参与的公司或合作者该项目是UCL，ESRF和DLS的科学家和数学家与汉诺威生物银行，美因茨和海德堡，UCL和帝国理工学院伦敦的临床医生以及许多其他合作者之间的国际跨学科合作。