Fast-MICP for frameless image-guided surgery.
OBJECTIVE
In image-guided surgery (IGS) systems, image-to-physical registration is critical for reliable anatomical information mapping and spatial guidance. Conventional stereotactic frame-based or fiducial-based approaches provide accurate registration but are not patient-friendly. This study proposes a frameless cranial IGS system that uses computer vision techniques to replace the frame or fiducials with the natural features of the patient.


METHODS
To perform a cranial surgery with the proposed system, the facial surface of the patient is first reconstructed by stereo vision. Accuracy is ensured by capturing parallel-line patterns projected from a calibrated LCD projector. Meanwhile, another facial surface is reconstructed from preoperative computed tomography (CT) images of the patient. The proposed iterative closest point (ICP)-based algorithm [fast marker-added ICP (Fast-MICP)] is then used to register the two facial data sets, which transfers the anatomical information from the CT images to the physical space.


RESULTS
Experimental results reveal that the Fast-MICP algorithm reduces the computational cost of marker-added ICP (J.-D. Lee et al., "A coarse-to-fine surface registration algorithm for frameless brain surgery," in Proceedings of International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 836-839) to 10% and achieves comparable registration accuracy, which is under 3 mm target registration error (TRE). Moreover, two types of optical-based spatial digitizing devices can be integrated for further surgical navigation. Anatomical information or image-guided surgical landmarks can be projected onto the patient to obtain an immersive augmented reality environment.


CONCLUSIONS
The proposed frameless IGS system with stereo vision obtains TRE of less than 3 mm. The proposed Fast-MICP registration algorithm reduces registration time by 90% without compromising accuracy.
Lee
Jiann-Der
JD
Department of Electrical Engineering, Chang Gung University, Tao-Yuan 333, Taiwan.
Huang
Chung-Hsien
CH
Wang
Sheng-Ta
ST
Lin
Chung-Wei
CW
Lee
Shin-Tseng
ST
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D019047:Phantoms, Imaging; D012015:Reference Standards; D015203:Reproducibility of Results; D012867:Skin / Q000000981:diagnostic imaging; D025321:Surgery, Computer-Assisted / Q000295:instrumentation / Q000379:methods* / Q000592:standards; D014057:Tomography, X-Ray Computed