SU‐C‐213CD‐01: Automated Marker Tracking for Intra‐Fractional Image Guidance in Radiotherapy

Purpose: We have developed two automated methods for real‐time detection of intra‐fractional target motion during treatment with the aid of metallic markers based on ‘in‐line’ acquired x‐ray images. Fast detection algorithms are desirable to facilitate tumor tracking applications. Methods: A Siemens linac was equipped with a 16×16″ a‐Si flat panel detector (FPD) underneath the treatment head. A diagnostic x‐ray tube was positioned opposite to the treatment head; geometrically separating MV treatment and kV imaging fields on the FPD. We analyzed x‐ray images of a lung phantom equipped with a marker for better contrast. For the automated marker detection we developed two algorithms as part of our image acquisition application. Both algorithms are based on gray‐value template matching with normalized cross‐correlation (NCC). One of the algorithms pre‐ calculates suitable candidate regions by employing a canny edge detection filter. In this study, we wanted to a) study the quality of template matching under the influence of MV induced image artifacts (e.g. stripe artifacts), and b) investigate if pre‐selection of candidate regions leads to reliable marker detection. Results: Our lung‐phantom study has demonstrated that matching a 21×21 pixel template over an 81×81 image with NCC takes about 9.9+−4.5 ms. This method is very reliable and works for a wide dose range. Using canny pre‐filtering reduces the latency to 1.1+−0.7 ms, but marker detection becomes less reliable at our lowest dose level (0.6 mAs). In the presence of an 8×8 cm̂2 MV field, the latencies remain unchanged but the correlation factors decrease sharply by up to 22%. We observed that MV induced stripe artifacts overlapping with the marker are the greatest hurdle to successful detection. Conclusions: While template‐matching over the entire image reliably finds the correct marker position, it is possible to accelerate the search for high contrast images with the help of canny filtering. Supported by Siemens Healthcare/CR.