Kinect based real-time position calibration for nasal endoscopic surgical navigation system

Jingfan Fan; Jian Yang; Yakui Chu; Shaodong Ma; Yongtian Wang

doi:10.1117/12.2216599

18 March 2016 Kinect based real-time position calibration for nasal endoscopic surgical navigation system

Jingfan Fan, Jian Yang, Yakui Chu, Shaodong Ma, Yongtian Wang

Proceedings Volume 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling; 97861O (2016) https://doi.org/10.1117/12.2216599
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Unanticipated, reactive motion of the patient during skull based tumor resective surgery is the source of the consequence that the nasal endoscopic tracking system is compelled to be recalibrated. To accommodate the calibration process with patient's movement, this paper developed a Kinect based Real-time positional calibration method for nasal endoscopic surgical navigation system. In this method, a Kinect scanner was employed as the acquisition part of the point cloud volumetric reconstruction of the patient's head during surgery. Then, a convex hull based registration algorithm aligned the real-time image of the patient head with a model built upon the CT scans performed in the preoperative preparation to dynamically calibrate the tracking system if a movement was detected. Experimental results confirmed the robustness of the proposed method, presenting a total tracking error within 1 mm under the circumstance of relatively violent motions. These results point out the tracking accuracy can be retained stably and the potential to expedite the calibration of the tracking system against strong interfering conditions, demonstrating high suitability for a wide range of surgical applications.

Citation Download Citation

Jingfan Fan, Jian Yang, Yakui Chu, Shaodong Ma, and Yongtian Wang "Kinect based real-time position calibration for nasal endoscopic surgical navigation system", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97861O (18 March 2016); https://doi.org/10.1117/12.2216599

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