A framework for efficient synergistic spatiotemporal reconstruction of PET-MR dynamic data

PET-MR动态数据高效协同时空重建框架

• 批准号: EP/P022200/1
• 负责人: Kris Thielemans
• 金额: $ 66.72万
• 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: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP022200%2F1
• 关键词: framework; efficient; synergistic; spatiotemporal; reconstruction

Magnetic resonance (MR) and radionuclide imaging using positron emission tomography (PET) are now essential in many areas of medical diagnosis, clinical research and drug development. The complementary information provided by PET and MR has recently lead to a new generation of dual-modality systems. Clinical applications of these devices are emerging in many priority areas for human health, including dementia, cardiology, and oncology. The previously established Collaborative Computational Project (CCP) for synergistic PET-MR reconstruction connects researchers working on sophisticated algorithms to estimate stacks of slices through the patient from the measured data. In this flagship project, we extend this work towards imaging of dynamic processes. Measuring the redistribution over time of contrasts agents or labelled molecules while taking organ movement into account provides considerable information on the biology, allowing extraction of parameters such as blood flow, receptor density and tissue elasticity.By exploiting correlations between these parameters in both space and time, we can probe fast dynamic processes at higher accuracy than achievable in single modality imaging. This flagship will provide freely available software and methods to the community to accelerate research and development in this area.

磁共振 (MR) 和使用正电子发射断层扫描 (PET) 的放射性核素成像现在在医学诊断、临床研究和药物开发的许多领域至关重要。 PET 和 MR 提供的互补信息最近催生了新一代双模态系统。这些设备的临床应用正在人类健康的许多优先领域出现，包括痴呆症、心脏病学和肿瘤学。先前建立的用于协同 PET-MR 重建的协作计算项目 (CCP) 将研究复杂算法的研究人员联系起来，根据测量数据估计患者体内的切片堆栈。在这个旗舰项目中，我们将这项工作扩展到动态过程的成像。测量造影剂或标记分子随时间的重新分布，同时考虑器官运动，可以提供大量的生物学信息，从而可以提取血流、受体密度和组织弹性等参数。通过利用这些参数在空间和时间上的相关性，我们可以比单模态成像更高的精度探测快速动态过程。该旗舰产品将向社区免费提供软件和方法，以加速该领域的研究和开发。

项目成果

Motion-Corrected PET Reconstruction with SIRF

使用 SIRF 进行运动校正 PET 重建

• 发表时间: 2018
• 作者: Brown, R

Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach

• DOI: 10.1109/access.2021.3056150
• 发表时间: 2021-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Abascal, Juan F. P. J.;Ducros, Nicolas;Peyrin, Francoise
• 通讯作者: Peyrin, Francoise

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

Motion-Corrected Reconstruction of Parametric Images from Dynamic PET Data with the Synergistic Image Reconstruction Framework (SIRF)

使用协同图像重建框架 (SIRF) 根据动态 PET 数据对参数图像进行运动校正重建

• 发表时间: 2018
• 作者: Brown, R

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