Real-time radiation dose reconstruction for improved radiation therapy

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Introduction: Radiation is used to help treat about 70% of all cancer patients. However, current treatment using radiotherapy has two major weaknesses - 1) it has yet to come up with a good solution to account for the motion of the tumour during treatment, and 2) it assumes the actual delivered radiation dose is equal to the planned dose. In this proposal, we plan to use the treatment radiation beam itself to address both of these weaknesses. Measuring the amount of radiation exiting the patient during the treatment will allow us to both 1) image the tumour target and perform real-time correction to track the beam to the moving tumour and 2) reconstruct the actual 3D delivered radiation dose to the patient, without assuming it is equal to the planned dose. Methods: Recently developed artificial intelligence (AI) methods offer the promise of providing extremely fast image analysis while still remaining highly accurate. We will train an AI network to recognize patient-generated scattered x-rays which are present in x-ray images acquired during patient radiation treatment. This training requires a large number of x-ray images where the amount of x-ray scatter is known exactly. We will use our expertise in x-ray calculations, including very accurate methods we developed previously but which are too slow for real-time, to generate these images. Once the AI network is correctly trained, it will provide accurate patient x-ray scatter signal estimates in real-time. The removal of the x-ray scatter signal from the x-ray images will result in clearer images, which is beneficial for tumour tracking. Using these clearer images, we will train a second AI network to identify and track tumour movement in the x-ray images in real-time. We will start by working with a pretend lung cancer patient called a `phantom', where we can exactly control the motion of a plastic tumour, then progress to use more-realistic patient data. Finally, we will implement the two AI networks together to track tumour motion in real-time. While the tumour is being tracked in real-time, we will use the clearer images to calculate the radiation dose that was actually delivered to the phantom. Once successfully demonstrated on the phantom, it will be ready to be translated to patient use. Significance: This research program will address two major weaknesses in current radiotherapy by using the treatment beam to correct for tumour motion in real-time during treatment, and to accurately calculate the actual delivered dose to the patient in real-time. Together, these advances will improve real-time tumour tracking applications and increase patient safety via real-time error detection. The innovative methods developed here have the potential to improve the treatment of cancer on radiotherapy units worldwide.