Image analysis-based framework for adaptive and focal radiotherapy
It is estimated that more than 60% of cancer patients will receive radiotherapy (RT). Medical images acquired from different imaging modalities are used to guide the entire RT process from the initial treatment plan to fractionated radiation delivery. Accurate identification of the gross tumor volume (GTV) on computed tomography (CT), acquired at different time points, is crucial for the success of RT. In addition, complementary information from magnetic resonance imaging (MRI), positron emission tomography (PET), cone-beam computed tomography (CBCT) and electronic portal imaging device (EPID) is often used to obtain better definition of the target, track disease progression and update the radiotherapy plan. However, identifying tumor volumes on medical image data requires significant clinical experience and is extremely time consuming. Computer-based methods have the potential to assist with this task and improve radiotherapy. In this thesis a method was developed for automatically identifying the tumor volume on medical images. The method consists of three main parts: (1) a novel rigid image registration method based on scale invariant feature transform (SIFT) and mutual information (MI); (2) a non-rigid registration (deformable registration) method based on the cubic B-spline and a novel similarity function; (3) a gradient-based level set method that used the registered information as prior knowledge for further segmentation to detect changes in the patient from disease progression or regression and to account for the time difference between image acquisition. Validation was carried out by a clinician and by using objective methods that measure the similarity between the anatomy defined by a clinician and by the method proposed. With this automatic approach it was possible to identify the tumor volume on different images acquired at different time points in the radiotherapy workflow. Specifically, for lung cancer a mean error of 3.9% was found; clinically acceptable results were found for 12 of the 14 prostate cancer cases; and a similarity of 84.44% was achieved for the nasal cancer data. This framework has the potential ability to track the shape variation of tumor volumes over time, and in response to radiotherapy, and could therefore, with more validation, be used for adaptive radiotherapy.