Machine learning augmented hierarchical tomographic image reconstruction of human organs

机器学习增强人体器官分层断层图像重建

• 批准号: 2720289
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2720289
• 关键词: Machine; learning; augmented; hierarchical; tomographic

1) Brief description of the context of the research including potential impactImaging of organs at multiple resolutions can be extremely useful for understanding their function, especially if we can correlate cellular level information to whole organ processes. X-ray tomography is one of the methods that can be used to image at cellular level information; however, for a scan of an intact organ, normally this can only be achieved when the sample is small such as mouse organs (millimetres in size). For human organs, traditionally only biopsies could be scanned at such resolution. These biopsies distort, making correlation of the high-resolution results to low-resolution images challenging. A new technique, Hierarchical Phase-Contrast Tomography (HiP-CT) has been co-developed by UCL and ESRF, that can achieve cellular level resolution for volumes of interest (VOI) in an intact organ, starting from a full organ scan at lower resolution and hierarchically zoom into VOI at higher resolutions. This made it possible to have an intact organ and zoom in at any part to study its cellular level functions. (see human-organ-atlas.esrf.eu and mecheng.ucl.ac.uk/hip-ct)The tomographic images obtained by HiP-CT provide information across a range of scales. This project will apply and develop machine learning algorithms use the information from all scan resolutions to obtain better feature extraction than can be obtained from a single resolution. If successful, this could enable super-resolution imaging, with a final goal of informing clinical radiology. 2) Aims and ObjectivesThe aim of the project is to apply and develop machine learning algorithms to assist the reconstruction of high-resolution VOIs using data from lower resolution scan of the entire organ.The objectives are as follows:To investigate the use of generative model such as super resolution and generative adversarial nets to assist the reconstruction of VOI.To investigate the use of unsupervised learning algorithms to identify feature points to be used in the reconstruction process.To investigate the use of one model for all organ reconstructions or different models for different organs or organ groups.Considering the possible limitation on data variation, to investigate potential use of data from other sources to help identify key features via unsupervised learning algorithms.To compare the reconstruction results from the machine learning models with the reconstruction results obtained using the reference scan method.3) Novelty of Research MethodologyHiP-CT has only been recently developed, the only method to remove noise during the reconstruction process for extreme off-axis scans is via a reference scan and traditional denoising algorithms. The use of machine learning techniques to reconstruct utilising data from a lower resolution scan is yet to be investigated.4) Alignment to EPSRC's strategies and research areasThe project falls within EPSRC research area of engineering. The project aims to develop machine learning techniques to assist the reconstruction of HiP-CT scans, which helps towards the understanding of human organ structures. It aligns with the EPSRC's strategic priorities of artificial intelligence, digitisation and data, and also transforming health and healthcare.5) Any companies or collaborators involvedDr. Paul Tafforeau from the European Synchrotron Radiation Facility (ESRF).

1)研究背景的简要描述，包括潜在的影响以多种分辨率对器官进行成像，对于了解它们的功能非常有用，特别是如果我们可以将细胞水平的信息与整个器官过程相关联。X射线断层扫描是可用于在细胞水平信息成像的方法之一;然而，对于完整器官的扫描，通常这只能在样本较小时实现，例如小鼠器官（毫米大小）。对于人体器官，传统上只有活组织检查才能以这种分辨率扫描。这些活检扭曲，使高分辨率结果与低分辨率图像的相关性具有挑战性。UCL和ESRF共同开发了一种新技术，即分层相位对比断层扫描（HiP-CT），可以实现完整器官中感兴趣体积（VOI）的细胞水平分辨率，从较低分辨率的全器官扫描开始，分层放大到更高分辨率的VOI。这使得有可能有一个完整的器官，并放大任何部分来研究其细胞水平的功能。(see human-organ-atlas.esrf.eu和mecheng.ucl.ac.uk/hip-ct）通过HiP-CT获得的断层摄影图像提供了一系列尺度上的信息。该项目将应用和开发机器学习算法，使用来自所有扫描分辨率的信息，以获得比单一分辨率更好的特征提取。如果成功的话，这可以实现超分辨率成像，最终目标是为临床放射学提供信息。2)目的和目标本项目的目的是应用和开发机器学习算法，以帮助使用整个器官的低分辨率扫描数据重建高分辨率VOI。目标如下：研究使用生成模型（如超分辨率和生成对抗网络）来辅助VOI重建。研究使用无监督学习算法来识别重建过程中使用的特征点。研究使用一个模型进行所有器官重建或使用不同模型进行重建。不同的器官或器官组。考虑到数据变化的可能限制，研究来自其他来源的数据的潜在用途，以帮助通过无监督学习算法识别关键特征。将机器学习模型的重建结果与使用参考扫描方法获得的重建结果进行比较。3）研究方法的新奇HiP-CT最近才开发出来，在极端离轴扫描的重建过程中去除噪声的唯一方法是通过参考扫描和传统的去噪算法。使用机器学习技术来利用来自较低分辨率扫描的数据进行重建尚待研究。4）与EPSRC的战略和研究领域保持一致该项目福尔斯EPSRC的工程研究领域。该项目旨在开发机器学习技术，以帮助重建HiP-CT扫描，这有助于了解人体器官结构。它符合EPSRC的人工智能，数字化和数据的战略重点，也改变了健康和医疗保健。Paul Tafforeau，欧洲同步辐射实验室（ESRF）。