Fluoroscopic tracking of multiple implanted fiducial markers using multiple object tracking.
When treating mobile tumors using techniques such as beam gating or beam tracking, precise localization of tumor position is required, which is often realized by fluoroscopically tracking implanted fiducial markers. Multiple markers placed inside or near a tumor are often preferred to a single marker for the sake of accuracy. In this work, we propose a marker tracking system that can track multiple markers simultaneously, without confusing them, and that is also robust enough to continue tracking even when the markers are moving behind bony anatomy. The integrated radiotherapy imaging system (IRIS), developed at the Massachusetts General Hospital (MGH), was used to take fluoroscopy videos for marker tracking. The tracking system integrates marker detection with a multiple object tracking process, inspired by the multiple hypothesis marker tracking (MHT) process. It also utilizes breathing pattern information to help tracking. Four criteria are used to identify tracking failure, and when tracking failure occurs, the system can immediately inform the user. (In the clinical environment, the system would immediately disable the treatment beam.) In this paper, two liver patients with implanted fiducial markers were studied, and the studies were performed retrospectively to assess the effectiveness of the new tracking system. For both patients, LAT and AP fluoroscopic videos were studied. In order to better test the proposed tracking system, artificial markers were added around the real markers to disturb the tracking of the real markers. The performance of the proposed system was compared to that of a conventional tracking system (one that did not use multiple object tracking). The performance of the new system was also investigated with and without consideration of the breathing pattern information. We found that the conventional tracking system can easily miss tracking markers in the presence of artificial markers, and it cannot detect the tracking failures. On the other hand, our proposed system can track markers well and can also successfully detect tracking failures. Failure rate was calculated on a per-frame-per-marker basis for the proposed tracking system. When the system considered breathing pattern information, it had a 0% failure rate 75% of the time and 0.4% failure rate 25% of the time. However, when the system did not consider breathing patterns, it had a much higher failure rate, in the range of 1.2%-12%. Both examples of the proposed system yielded low e(95) (the maximum marker tracking error at 95% confidence level)-less than 1.5 mm.
Tang
Xiaoli
X
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
Sharp
Greg C
GC
Jiang
Steve B
SB
D000465:Algorithms; D001185:Artificial Intelligence; D005471:Fluoroscopy / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000295:instrumentation / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity