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

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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.