Development of a non-invasive real-time tumour motion tracking method using surface-guided radiation therapy

使用表面引导放射治疗开发非侵入性实时肿瘤运动跟踪方法

• 批准号: RGPIN-2020-06702
• 负责人: Gaede, Stewart
• 金额: $ 1.75万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760713
• 关键词: Development; non; invasive; real; time

Managing respiratory-induced tumour motion during radiation therapy still poses a major challenge in ensuring the intended radiation dose is delivered to the tumour while ensuring that nearby critical organ radiation dose limits are not exceeded. Real-time tumour tracking during radiation therapy represents a near ideal motion management strategy as it can allow for a conformal dose distribution to the target by eliminating treatment margins while the patient breathes freely with minimal beam delivery interruption. Successful implementation of real-time tumour tracking requires accurate tumour position identification, tumour motion prediction that also accounts for time delay in beam positioning response, beam repositioning technique, and an accurate 4D dose calculation model. Current methods that can perform dynamic tumour tracking involve implantation of multiple markers in or near the moving tumour. Unfortunately, the benefits of these techniques may be outweighed by the cost of the markers and implanting procedure, potential toxicities associated with the procedure, including excessive bleeding or pneumothorax, potential treatment delays, and marker migration. The use of external surrogates as a means of predicting tumour position have been proposed but have two major limitations. The first is that most external surrogates are single blocks placed at arbitrary positions that may not be well correlated with the internal motion. Second, breathing motion models are typically based on conventional 4D-CT acquisition which only consider 1-2 breathing cycles per slice and are susceptible to motion artifacts when patients breathe irregularly. We propose that surface-guided radiation therapy (SGRT) that uses 3-3D stereo camera pods to track a predefined region of interest on a patient's surface, together with real-time fluoroscopic kV imaging, can non-invasively predict the position of the tumour in real-time to allow for dynamic tumour tracking. To facilitate the prediction model, we aim to develop a patient-specific 4D-CT breathing motion model that can be acquired over one minute of scanning with near eliminated motion artifacts by imaging with a volumetric CT scanner. This scanner can image 16cm in the superior/inferior direction providing 3D-CT images of the tumour and skin surface, simultaneously every 0.28s. With retrospective CT reconstruction methods, we can decrease the image acquisition time to 0.1s. We aim to use a combination of biomechanical modeling and deep learning to establish a correlation between the skin surface motion and internal tumour motion. Validation of these techniques will be performed on respiratory motion phantoms and a preclinical model before testing on human cancer patients. The combination of volumetric 4D-CT, SGRT, real-time kV imaging, and a novel breathing motion model that combines deep learning and lung biomechanical properties will provide a novel approach to non-invasive dynamic tumour tracking.

在放射治疗期间管理呼吸道诱导的肿瘤运动仍然在确保预期的辐射剂量输送到肿瘤的同时确保附近的关键器官辐射剂量限制不超过预期的辐射剂量方面构成了重大挑战。放射疗法期间的实时肿瘤跟踪代表了一种接近理想的运动管理策略，因为它可以通过消除治疗范围来使靶剂量分布到目标，而患者则在最小的束递送中断时自由呼吸。实时肿瘤跟踪的成功实现需要准确的肿瘤位置识别，肿瘤运动预测，该预测还解释了光束定位响应的时间延迟，梁重新定位技术和准确的4D剂量计算模型。可以执行动态肿瘤跟踪的当前方法涉及在移动肿瘤中或附近植入多个标记。不幸的是，这些技术的好处可能会超过标记的成本和植入程序，与该程序相关的潜在毒性，包括过度出血或气胸，潜在的治疗延迟和标记迁移。已经提出了使用外部替代物作为预测肿瘤位置的一种手段，但有两个主要局限性。首先是，大多数外部替代物是位于任意位置的单个块，可能与内部运动没有很好的相关。其次，呼吸运动模型通常是基于常规的4D-CT采集，该恢复仅考虑每切片1-2呼吸周期，并且在患者不规则地呼吸时易受运动伪像的影响。我们提出，使用3-3D立体声摄像头豆荚跟踪患者表面上感兴趣的预定区域的表面引导放射治疗（SGRT），以及实时荧光透镜KV成像，可以非侵入地预测实时的肿瘤位置，以实时允许动态肿瘤跟踪。为了促进预测模型，我们旨在开发一种患者特异性的4D-CT呼吸运动模型，该模型可以在一分钟的扫描中通过使用体积的CT扫描仪进行成像，并在一分钟的扫描中获取。该扫描仪可以在上/下方向上成像16厘米，从而每0.28同时提供肿瘤和皮肤表面的3D-CT图像。通过回顾性CT重建方法，我们可以将图像采集时间降低到0.1。我们的目标是结合生物力学建模和深度学习，以建立皮肤表面运动与内部肿瘤运动之间的相关性。在对人类癌症患者进行测试之前，将对这些技术的验证在呼吸运动幻像和临床前模型上进行。体积4D-CT，SGRT，实时KV成像以及结合深度学习和肺部生物力学特性的新型呼吸运动模型的组合将为非侵入性动态肿瘤跟踪提供一种新颖的方法。