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Material and Methods: Informed consent: Informed consent has been obtained from all individuals included in this study. Note the large differences in amplitude between the individual sensors.Ĭonflict of interest: Authors state no conflict of interest. Figure 6 shows the distances measured for all eleven templates. Recording motion traces of the markers worked for all four volunteers (three male, one female), although markers one and three were difficult to track due to stretching of the fabric. In general, the runtime of one template matching iteration was around 80 ms. Using our multi-threaded implementation in C#, tracking eleven markers in the color camera image – using ROIs of twice the size of the marker template – was possible in real time using multi-threading on a MacBook Pro Retina (2.3 GHz Core i7, four cores, 16 GiB RAM, SSD). Second, the distance to the patient couch was measured repeatedly for about twelve minutes to determine the amount of noise and possible drift. First, the robot as shown in Figure 5 was programmed to follow a sinusoidal motion (therefore similar to respiratory motion) along the z- axis while the distance to the patient couch was computed for each camera frame.

2.2 Accuracy measurementsįinally, the stability and accuracy of the Kinect sensor was evaluated in another experiment. Details and numbering of the markers are shown in Figure 1. Each marker consists of a black circle surrounded by a black ring. Eleven marker templates were printed onto a Lycra shirt, ensuring tight fit on the volunteers, while position of the markers correspond to areas relevant for the measurement. To acquire respiratory motion traces, a special marker shirt has been developed. In this work, we demonstrate how consumer hardware (Microsoft’s Kinect v2 depth sensor) can be used to accurately track the 3D position of multiple markers using a special marker shirt. It has been shown that the accuracy of these correlation algorithms can be improved by incorporating multiple markers.

Although this could be done (either using continuous X-ray localisation or 3D ultrasound tracking ), the current method in clinical use relies on a mathematical model linking the motion on the patient’s chest to the motion of the actual target. Especially in the second scenario, tracking one marker may not be sufficient: the actual target of the treatment beam – the tumour – is typically not observed directly. Subsequently, trajectory of the marker is then analysed and used to either dynamically activate the treatment beam (called gating ) or to guide the radiation source. As an example, image-guided radiotherapy (IGRT) of the chest and abdomen relies heavily on this principle: some kind of marker is placed on or attached to the patient’s chest and is monitored using a non-invasive localisation device. In many clinical applications, detecting and tracking of respiratory motion is required.

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