CCP in Synergistic Reconstruction for Biomedical Imaging

CCP 在生物医学成像协同重建中的应用

• 批准号: EP/T026693/1
• 负责人: Kris Thielemans
• 金额: $ 60.65万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT026693%2F1
• 关键词: CCP; Synergistic; Reconstruction; Biomedical; Imaging

Biomedical imaging has a crucial role in (pre)clinical research, drug development, medical diagnosis and assessment of therapy response. Often, the images are tomographic: from the measured data, (stacks of) slices or volumes representing anatomical and functional properties of the patient can be reconstructed using sophisticated algorithms. Increasingly, images from multiple types of systems such as Magnetic Resonance (MR), radionuclide imaging using Positron Emission Tomography (PET) or Single Photon Emission Computed Tomography (SPECT) and X-ray Computed Tomography (CT) are analysed together. Image quality is critically dependent on image reconstruction methods. Development and testing of novel algorithms on patient data require considerable expertise and effort in software implementation. In our previous CCP on synergistic reconstruction for PET-MR, we created a network of UK and international researchers working towards integrating image reconstruction of data from integrated, simultaneous, PET-MR scanners. New multi-modality systems are now available or under development, for instance SPECT-MR or even tri-modality PET-SPECT-CT systems. At the same time, top-of-the-range multi-modality systems are expensive and instead combining single-modality scans from different time-points and systems can provide more cost-effective solutions in some cases. Synergistic image reconstruction aims to exploit the commonalities between the data from the different modalities and time points. However, cross-modality methods are particularly challenging. We will therefore extend the network to exploit synergy in multi-modal, multi-contrast, multi-time point information for biomedical applications, concentrating on the logistical and computational aspects of synergistic image reconstruction. The Open Source Software platform to be provided by this CCP will be an enabling technology which removes the frequent obstacles encountered when working with the raw medical imaging datasets, accelerating innovative developments in image reconstruction, and ultimately enabling the possibility of synergistic image reconstruction by establishing validated pipelines for processing raw data of multiple data-sets.

生物医学成像在临床前研究、药物开发、医学诊断和治疗反应评估中发挥着至关重要的作用。通常，图像是断层扫描的：根据测量的数据，可以使用复杂的算法重建表示患者的解剖和功能特性的切片或体积（堆叠）。越来越多地将来自多种类型系统的图像一起分析，例如磁共振（MR）、使用正电子发射断层扫描（PET）或单光子发射计算机断层扫描（SPECT）的放射性核素成像以及X射线计算机断层扫描（CT）。图像质量在很大程度上取决于图像重建方法。在患者数据上开发和测试新算法需要相当多的专业知识和软件实现方面的努力。在我们之前关于PET-MR协同重建的CCP中，我们创建了一个由英国和国际研究人员组成的网络，致力于整合来自集成同步PET-MR扫描仪的数据的图像重建。新的多模态系统现在可用或正在开发中，例如SPECT-MR或甚至三模态PET-SPECT-CT系统。与此同时，顶级的多模态系统是昂贵的，相反，在某些情况下，结合来自不同时间点和系统的单模态扫描可以提供更具成本效益的解决方案。协同图像重建旨在利用来自不同模态和时间点的数据之间的共性。然而，跨模态方法尤其具有挑战性。因此，我们将扩展网络，利用协同作用，多模态，多对比度，多时间点的信息，生物医学应用，集中在物流和计算方面的协同图像重建。该CCP提供的开源软件平台将是一项使能技术，可消除处理原始医学成像数据集时遇到的常见障碍，加速图像重建的创新发展，并最终通过建立经验证的管道来处理多个数据集的原始数据，从而实现协同图像重建。

项目成果

Structure-preserving deep learning

• DOI: 10.1017/s0956792521000139
• 发表时间: 2020-06
• 期刊: European Journal of Applied Mathematics
• 影响因子: 1.9
• 作者: E. Celledoni;Matthias Joachim Ehrhardt;Christian Etmann;R. McLachlan;B. Owren;C. Schönlieb;Ferdia Sherry

Recent Progress in STIR 5.0

STIR 5.0 的最新进展

• DOI: 10.1109/nss/mic44867.2021.9875880
• 发表时间: 2021
• 作者: Biguri A

Score-Based Generative Models for PET Image Reconstruction

• DOI: 10.59275/j.melba.2024-5d51
• 发表时间: 2023-08
• 期刊: ArXiv
• 作者: I. Singh;Alexander Denker;Riccardo Barbano;vZeljko Kereta;Bangti Jin;K. Thielemans;P. Maass;S. Arridge

Normalisation Factor Estimation in non-TOF 3D PET from Multiple-Energy Window Data

根据多能量窗口数据对非 TOF 3D PET 进行归一化因子估计

• DOI: 10.1109/nss/mic42677.2020.9507957
• 发表时间: 2020
• 作者: Brusaferri L

Systematic Evaluation of the Impact of Involuntary Motion in Whole Body Dynamic PET

全身动态 PET 中不自主运动影响的系统评估

• DOI: 10.1109/nss/mic44867.2021.9875689
• 发表时间: 2021
• 作者: Biguri A