Time-of-Flight – A New Modality for Radiotherapy
In this work, one of the first approaches utilizing so-called Time-of-Flight cameras for medical applications is presented. Using Time-of-Flight cameras it is feasible to acquire a 3-D model in real-time with a single sensor. Several systems for managing motion within radiotherapy are presented. There are five major contributions in this work: A method to verify internal tumor movement with an external respiratory signal on-line, the application of a novel technology to medical image processing and the introduction of three novel systems, one to measure respiratory motion and two other to position patients. The algorithm to correlate external and internal motion is an image-based synchronization procedure that automatically labels pre-treatment fluoroscopic images with corresponding 4-D CT phases. It is designed as an optimization process and finds the optimal mapping between both sequences by maximizing the image similarity between the corresponding pairs while preserving a temporal coherency. It is both evaluated at synthetic and patient data and an average of 93% correctly labeled frames could be achieved. The Time-of-Flight based respiratory motion system enables the simultaneously measurement of different regions. We evaluate the system using a novel body phantom. Tests showed, that the system signal and the ground truth signal of the phantom have a reliable correlation of more than 80% for amplitudes greater 5 mm. The correlation of both systems is independent (always more than 80%) of the respiratory frequency. Furthermore, the measured signals were compared with a well-established external gating system, the Anzai belt. These experiments were performed on human persons. We could show a correlation of about 88% of our system and the Anzai system. The first positioning system is able to position a C-arm like device with respect to the patient. Therefore, a Time-of-Flight camera acquires the whole body of the patient and segments it into meaningful anatomical regions, like head, thorax, abdomen, legs. The system computes 3-D bounding boxes of the anatomical regions and computes the isocenter of the boxes. Using this information, the C-arm system can automatically position itself and perform a scan. The system is evaluated using a body phantom and an accuracy within the patient table accuracy of 1 cm could be shown. The second system deals with surface-based positioning of a patient with respect to a priorly acquired surface of the same patient. Such systems are necessary, e.g. in radiotherapy or multi-modal imaging. The method uses an Iterative-Closest-Point algorithm, tailored to Time-of-Flight cameras. It is evaluated using a body phantom and obtains an overall accuracy of 0.74 mm +/ 0.37 mm for translations in all three room directions within 10 mm.