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.

在放射治疗过程中管理呼吸引起的肿瘤运动仍然是一个重大挑战，需要确保将预期的放射剂量输送到肿瘤，同时确保不超过附近关键器官的放射剂量限制。放射治疗期间的实时肿瘤跟踪代表了一种近乎理想的运动管理策略，因为它可以通过消除治疗余量来实现对目标的适形剂量分布，同时患者自由呼吸，并最大限度地减少射束传输中断。成功实施实时肿瘤跟踪需要准确的肿瘤位置识别、肿瘤运动预测（同时考虑射束定位响应的时间延迟）、射束重新定位技术以及准确的 4D 剂量计算模型。目前可以进行动态肿瘤追踪的方法涉及在移动肿瘤中或附近植入多个标记物。不幸的是，这些技术的好处可能会被标记物和植入程序的成本、与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物迁移）所抵消。已经提出使用外部替代物作为预测肿瘤位置的手段，但有两个主要局限性。首先，大多数外部代理是放置在任意位置的单个块，可能与内部运动没有很好的相关性。其次，呼吸运动模型通常基于传统的 4D-CT 采集，每层仅考虑 1-2 个呼吸周期，并且当患者呼吸不规则时容易受到运动伪影的影响。我们提出，表面引导放射治疗 (SGRT) 使用 3-3D 立体摄像头跟踪患者表面的预定感兴趣区域，结合实时荧光透视 kV 成像，可以无创地预测肿瘤的位置实时进行动态肿瘤跟踪。为了促进预测模型的建立，我们的目标是开发一种患者特定的 4D-CT 呼吸运动模型，该模型可以通过使用体积 CT 扫描仪成像在一分钟的扫描中获得，几乎消除了运动伪影。该扫描仪可以向上/向下方向 16 厘米成像，每 0.28 秒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缩短至0.1秒。我们的目标是结合生物力学建模和深度学习来建立皮肤表面运动和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，将在呼吸运动模型和临床前模型上对这些技术进行验证。体积 4D-CT、SGRT、实时 kV 成像以及结合深度学习和肺生物力学特性的新型呼吸运动模型的结合将为非侵入性动态肿瘤跟踪提供一种新方法。