Motion Modelling and Motion Compensated Reconstruction from Cone-Beam CT Projection Data

锥束 CT 投影数据的运动建模和运动补偿重建

• 批准号: 2577647
• 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: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2577647
• 关键词: Motion; Modelling; Compensated; Reconstruction; Cone

1) Brief description of the context of the research including potential impactOn-board Cone-Beam CT (CBCT) imaging systems are available on the majority of Radiotherapy treatment machines, but image acquisition times of ~1 minute make it difficult to reconstruct images showing respiratory motion. This research project will develop methods to model the respiratory motion directly from unreconstructed projection data, and use these models to perform a motion-compensated reconstruction of the CBCT volume. This could potentially lead to more accurate, safer, and more effective treatments for lung cancer patients.2) Aims and Objectives1. Develop, implement, and evaluate 'surrogate-driven' and 'surrogate-free' motion models that can be applied to CBCT projection data.2. Investigate different methods for performing the motion-compensated reconstruction of the CBCT data, including modern iterative and learning based approaches.3. Investigate modern machine learning based approaches for fitting the motion models to the projection data.4. Establish tools and workflows for using the motion models to inform and guide adaptive radiotherapy. 3) Novelty of Research MethodologyInitial work has been undertaken at UCL and elsewhere on fitting motion models directly to projection data, but so far this has not been successfully applied to real patient data and there are a number of challenges still to be overcome. Furthermore, all previous work has focussed on 'surrogate-driven' models. Surrogate-free motion models using a low-rank decomposition approach have recently been proposed for MRI but have not previously been applied to CBCT data. Novel methods will also be investigated for performing the motion compensated reconstructions and for fitting the motion models using modern machine learning approaches. Finally, the motion models will enable novel methods to be developed for adapting radiotherapy treatment to better account for the respiratory motion.4) Alignment to EPSRC's strategies and research areasThis project is aligned with EPSRC's Healthcare technology theme, especially the challenges of Expanding the Frontiers of Physical Intervention and Optimising Disease Prediction, Diagnosis, and Intervention. It is also aligned with the Medical Imaging research area. 5) Any companies or collaborators involvedElekta will be involved in the project.The Christie Hospital and the Institute of Cancer Research will also collaborate on the project by providing CBCT data and expert advice

1）对研究的上下文进行简要说明，包括大多数放疗治疗机器上都可以使用潜在的影响板锥束CT（CBCT）成像系统，但是图像采集时间为〜1分钟，因此很难重建显示呼吸运动的图像。该研究项目将开发直接从未结构的投影数据对呼吸运动建模的方法，并使用这些模型对CBCT体积进行运动补偿的重建。这可能会导致对肺癌患者的更准确，更安全，更有效的治疗方法。2）目标和目标1。开发，实施和评估可以应用于CBCT投影数据的“替代驱动”和“无替代”运动模型2。研究了执行CBCT数据的运动补偿重建的不同方法，包括现代迭代和基于学习的方法3。研究基于机器学习的方法，将运动模型拟合到投影数据4。建立工具和工作流，用于使用运动模型来告知和指导自适应放射疗法。 3）研究方法论工作的新颖性是在UCL和其他地方直接用于投影数据的其他地方进行的，但是到目前为止，这尚未成功地应用于真实的患者数据，并且仍有许多挑战要克服。此外，所有以前的工作都集中在“替代驱动”模型上。最近已经提出了使用低级分解方法的替代运动模型，但以前尚未应用于CBCT数据。还将研究新方法，以执行运动补偿的重建以及使用现代机器学习方法拟合运动模型的方法。最后，运动模型将促进新的方法来适应放射疗法治疗以更好地说明呼吸运动。4）与EPSRC的策略和研究领域保持一致，该项目与EPSRC的医疗保健技术主题一致，尤其是扩大疾病预测和优化疾病预测的挑战，尤其是扩大了疾病预测的挑战，诊断和干预。它也与医学成像研究区域保持一致。 5）任何涉及的公司或合作者都将参与该项目。克里斯蒂医院和癌症研究所也将通过提供CBCT数据和专家建议来就该项目进行合作