Abstract
External Beam Radiotherapy is a method that is widely used in treating tumours in cancer patients. Image acquisition methods such as 4 - dimensional computed tomography (4DCT) and cone-beam computed tomography(CBCT) are used frequently when planning and delivering treatment strategies. However, patients involuntary movements such as respiratory motion cause ambiguities (blurriness, etc ) when locating tumours in these scanned images. These aforementioned ambiguities in tumour location could lead to higher dosed treatment plans. This could be highly undesirable for the patients, as it will easily damage healthy tissues around the tumour as well. However, two main problems occur in external beam radiotherapy and need to be taken into account when estimating the motion of a tumour. First, the tumour movement as the patient breathes and second, the delay in the time needed for the treatment delivery system (usually a linear accelerator) to move and produce a beam profile that conforms to the treatment plan at a particular angle. This means that ideally the patients internal organ configuration should be dynamically predicted in order to minimize treating normal tissue at the tumour margin. We propose that a dynamic CT volume (one breathing cycle) be used, to determine a patient specific motion model by using iterative registration methods to determine the transformation parameters to locate the tumour and its movements. A correlation model was built to determine the motion between the lung tumour and the internal surrogate organ. Therefore, when the treatment beams impinge on the patient during radiotherapy the tumour position can then be determined and targeted within ±3mm from the CTV-PTV margin in any direction which accounts for geometric errors in treatment planning. As a consequence, reduces the overall PTV margin of error in tumour motion considering imperceptible penetration, which leads to reduced patient dose in the treatment plan.