CCP in Synergistic Reconstruction for Biomedical Imaging

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，图像是层析成像的：从测量数据中，可以使用复杂的算法重建代表患者解剖和功能特性的（成堆）切片或体积。越来越多地，来自多种类型系统的图像，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）和x射线计算机断层扫描（CT）的放射性核素成像，被一起分析。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大量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的、同步的PET-MR扫描仪的图像重建数据。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。同时，顶级的多模态系统价格昂贵，在某些情况下，将不同时间点和系统的单模态扫描结合起来可以提供更具成本效益的解决方案。协同图像重建的目的是利用不同模态和时间点数据之间的共性。然而，跨模态方法尤其具有挑战性。因此，我们将扩展网络，以利用生物医学应用中多模态、多对比度、多时间点信息的协同作用，重点关注协同图像重建的后勤和计算方面。该CCP提供的开源软件平台将是一种使能技术，它消除了处理原始医学成像数据集时经常遇到的障碍，加速了图像重建的创新发展，并最终通过建立处理多个数据集原始数据的有效管道，使协同图像重建成为可能。

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.