Machine learning augmented hierarchical tomographic image reconstruction of human organs

1)简要描述研究的背景，包括潜在的影响器官在多分辨率下的成像对于了解它们的功能是非常有用的，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的话。X射线断层成像是可用于在细胞水平上成像的方法之一；然而，对于完整器官的扫描，通常只有当样本较小时才能实现，例如小鼠器官(毫米大小)。对于人体器官，传统上只有活组织才能以这样的分辨率进行扫描。这些活组织检查会扭曲，使得高分辨率结果与低分辨率图像之间的关联具有挑战性。UCL和ESRF共同开发了一种新技术-分层相位对比层析成像(HIP-CT)，该技术可以从较低分辨率的完整器官扫描开始，以较高分辨率分层放大感兴趣体积(VOI)，从而实现完整器官感兴趣体积(VOI)的细胞级分辨率。这使得人们有可能拥有一个完整的器官，并在任何部位放大以研究其细胞水平的功能。(见Human-器官-atlas.esrf.eu和mecheng.ucl.ac.uk/ip-ct)HIP-CT获得的断层图像提供了各种尺度的信息。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来获得比从单一分辨率获得更好的特征提取。如果成功，这将使超分辨率成像成为可能，最终目标是向临床放射学提供信息。2)目的和目的本项目的目的是应用和开发机器学习算法，以利用来自整个器官的低分辨率扫描的数据来辅助高分辨率VOI的重建。目标如下：研究超分辨率和产生式对抗性网络等产生式模型在辅助VOI重建中的使用。研究无监督学习算法在重建过程中识别要使用的特征点的使用。调查所有器官重建使用一个模型或不同器官或器官组的不同模型的使用。考虑到数据变化的可能限制，调查来自其他来源的数据的潜在用途，以帮助通过非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)研究方法的新颖性HiP-CT是最近才开发的，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去噪算法。使用机器学习技术来重建利用较低分辨率扫描的数据还有待研究。4)与EPSRC的战略和研究领域保持一致该项目属于EPSRC工程研究领域。该项目旨在开发机器学习技术，以帮助重建髋关节CT扫描，这有助于了解人体器官的结构。它符合EPSRC的战略重点，即人工智能、数字化和数据，以及改变健康和医疗保健。来自欧洲同步辐射设施(ESRF)的保罗·塔弗罗。

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).