Using AI to enable imaging of exotic radionuclides for Molecular Radiotherapy

使用人工智能对分子放射治疗中的外来放射性核素进行成像

• 批准号: 2874500
• 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: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2874500
• 关键词: Using; AI; enable; imaging; exotic

1) Brief description of the context of the research including potential impactMolecular Radiotherapy (MRT) is a rapidly growing cancer treatment modality where molecules that bind to cancerous cells are labelled with a radionuclide and injected into the patient for targeted delivery of radiation. Multimodality imaging using CT and nuclear imaging (SPECT/PET) can be performed to personalise the treatment doses as well as quantify absorbed doses to the tumours and organs at risk (OAR). There is increased interest in the use of exotic radionuclides including alpha-emitters such as Actinium-225 that have the potential to increase dose to the tumours while minimising impact on OARs, especially when combined with novel molecules that specifically target receptors or proteins in the tumours. 2) Aims and ObjectivesThe aim is to develop novel image reconstruction methods to enable quantitative imaging of exotic radionuclides with low levels of gamma emissions, focusing on alpha-emitters such as Actinium-225. The approach will integrate physics-based machine learning methods into the reconstruction process, combining the use of advanced modelling of the imaging physics, data from planning scans with state-of-the-art Deep Learning. Optimisation of acquisition protocols will be investigated. Test data will include Monte Carlo simulations, phantom scans (acquired as part of this project) as well as potentially patient data obtained as part of a clinical trial.3) Novelty of Research Methodology Imaging these novel radionuclides is very challenging due to their low abundance of generated gamma photons. Machine learning techniques are revolutionising the field of image reconstruction in general, and for ultra-low signal data in particular.4) Alignment to EPSRC's strategies and research areasThis project is aligned with the EPSRC strategy on "discovering and accelerating the development of new interventions" and "improving population health" by optimising imaging and dosimetry for theranostics using MRT.5) Any companies or collaborators involvedBlue Earth Therapeutics LtdNational Physical Laboratory (NPL)

1) 简要描述研究背景​​，包括潜在影响分子放射治疗 (MRT) 是一种快速发展的癌症治疗方式，其中与癌细胞结合的分子用放射性核素标记，然后注射到患者体内以进行靶向放射治疗。使用 CT 和核成像 (SPECT/PET) 的多模态成像可实现个性化治疗剂量，并量化肿瘤和危及器官 (OAR) 的吸收剂量。人们对使用外来放射性核素（包括 Actinium-225 等 α 发射体）越来越感兴趣，它们有可能增加肿瘤剂量，同时最大限度地减少对 OAR 的影响，特别是与专门针对肿瘤中受体或蛋白质的新型分子结合使用时。 2) 目的和目标 目的是开发新颖的图像重建方法，以实现对低伽马发射水平的外来放射性核素进行定量成像，重点关注 Actinium-225 等 α 发射体。该方法将基于物理的机器学习方法集成到重建过程中，结合使用成像物理的高级建模、计划扫描的数据和最先进的深度学习。将研究采集协议的优化。测试数据将包括蒙特卡罗模拟、模型扫描（作为本项目的一部分获得）以及作为临床试验的一部分获得的潜在患者数据。3) 研究方法的新颖性 由于丰度较低，对这些新型放射性核素进行成像非常具有挑战性产生的伽马光子。机器学习技术正在彻底改变图像重建领域，特别是超低信号数据。4）与 EPSRC 的战略和研究领域保持一致该项目与 EPSRC 关于“发现和加速新干预措施的开发”的战略保持一致以及通过使用 MRT 优化治疗诊断的成像和剂量测定来“改善人口健康”。5) 涉及 Blue Earth Therapeutics Ltd 国家物理实验室 (NPL) 的任何公司或合作者