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，与癌细胞结合的分子被标记为放射性核素，并注入患者体内以靶向放射。使用CT和核成像（SPECT/PET）的多模态成像可以进行个性化治疗剂量以及量化肿瘤和危险器官（OAR）的吸收剂量。人们对使用外来放射性核素的兴趣越来越大，包括像锕-225这样的α -发射器，它们有可能增加对肿瘤的剂量，同时最大限度地减少对OARs的影响，特别是当与专门针对肿瘤中受体或蛋白质的新分子结合使用时。2)目的和目标目的是开发新的图像重建方法，以实现具有低水平伽玛辐射的外来放射性核素的定量成像，重点是α发射器，如锕-225。该方法将基于物理的机器学习方法整合到重建过程中，结合使用先进的成像物理建模、规划扫描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、幻影扫描（作为该项目的一部分获得）以及作为临床试验的一部分获得的潜在患者数据。研究方法的新颖性由于这些新型放射性核素产生的伽马光子丰度很低，因此成像非常具有挑战性。一般来说，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本项目与EPSRC的战略一致，即“发现和加速新干预措施的发展”和“改善人口健康”，通过优化成像和剂量学来使用mrt进行治疗。