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

在放射治疗期间管理呼吸诱导的肿瘤运动仍然是一个重大挑战，以确保预期的辐射剂量被传递到肿瘤，同时确保不超过附近关键器官的辐射剂量限制。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，同时患者可以在最小限度地中断光束传输的情况下自由呼吸。要成功地实现实时肿瘤跟踪，需要准确的肿瘤位置识别、考虑射束定位响应时间延迟的肿瘤运动预测、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跟踪的方法包括在移动的肿瘤内或附近植入多个标记物。不幸的是，这些技术的好处可能超过了标记物和植入程序的成本，与该程序相关的潜在毒性，包括过度出血或气胸，潜在的治疗延误，以及标记物的迁移。使用外部替代物作为预测肿瘤位置的手段已被提出，但有两个主要限制。首先，大多数外部代理是放置在可能与内部运动不太相关的任意位置的单个块。其次，呼吸运动模型通常基于传统的4D-CT采集，每个切片只考虑1-2个呼吸周期，当患者呼吸不规律时，容易出现运动伪影。我们认为，表面引导放射治疗(SGRT)使用3-3D立体摄像吊舱跟踪患者表面预定的感兴趣区域，结合实时透视千伏成像，可以非侵入性地实时预测肿瘤的位置，从而实现动态肿瘤跟踪。为了方便预测模型，我们的目标是开发一种针对患者的4D-CT呼吸运动模型，该模型可以通过使用体积CT扫描仪进行成像，在一分钟的扫描过程中获得几乎消除的运动伪影。该扫描仪可在上/下方向成像16厘米，每0.28s同时提供肿瘤和皮肤表面的3D-CT图像。采用回顾性CT重建方法，可将图像采集时间缩短至0.1s。我们的目标是使用生物力学建模和深度学习的组合来建立皮肤表面运动和内部肿瘤运动之间的相关性。这些技术的验证将在呼吸运动模体和临床前模型上进行，然后再在人类癌症患者身上进行测试。体积4D-CT、SGRT、实时千伏成像和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结合将为非侵入性动态肿瘤跟踪提供一种新的方法。

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.