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Review
. 2023 Aug 19;12(16):5398.
doi: 10.3390/jcm12165398.

Image-to-Patient Registration in Computer-Assisted Surgery of Head and Neck: State-of-the-Art, Perspectives, and Challenges

Ali Taleb  1 , Caroline Guigou  1   2 , Sarah Leclerc  1 , Alain Lalande  1   3 , Alexis Bozorg Grayeli  1   2
Affiliations
Review

Image-to-Patient Registration in Computer-Assisted Surgery of Head and Neck: State-of-the-Art, Perspectives, and Challenges

Ali Taleb et al. J Clin Med. .

Abstract

Today, image-guided systems play a significant role in improving the outcome of diagnostic and therapeutic interventions. They provide crucial anatomical information during the procedure to decrease the size and the extent of the approach, to reduce intraoperative complications, and to increase accuracy, repeatability, and safety. Image-to-patient registration is the first step in image-guided procedures. It establishes a correspondence between the patient's preoperative imaging and the intraoperative data. When it comes to the head-and-neck region, the presence of many sensitive structures such as the central nervous system or the neurosensory organs requires a millimetric precision. This review allows evaluating the characteristics and the performances of different registration methods in the head-and-neck region used in the operation room from the perspectives of accuracy, invasiveness, and processing times. Our work led to the conclusion that invasive marker-based methods are still considered as the gold standard of image-to-patient registration. The surface-based methods are recommended for faster procedures and applied on the surface tissues especially around the eyes. In the near future, computer vision technology is expected to enhance these systems by reducing human errors and cognitive load in the operating room.

Keywords: computer-assisted surgery; head-and-neck surgery; image navigation; image-guided; image-to-patient; registration.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A classification of the registration methods. The upper row represents the intraoperative data (patient’s body) and the lower row the preoperative data (imaging). The dotted lines represent the pairing between the 2 modalities. In the anatomy-based method (A), anatomical landmarks are identified and selected by the operator. In the marker-based method (B), fiducial markers are fixed to the patient’s head. In the surface-based method (C), the red surface on the patient’s face is scanned by a specific instrument. In computer-vision-based method (D), the registration zone is captured by a camera.
Figure 2
Figure 2
Landmarks used in anatomy-based methods for registration.
Figure 3
Figure 3
Metrics and factors influencing the target registration error (TRE) in anatomy- and marker-based methods. the sign − indicates that the corresponding factor decreases the error. The sign + indicates that the corresponding factor increases the error.
Figure 4
Figure 4
Target registration error between 2D and 3D modalities. B is the target in the 3D modality. A is the registered target in the 2D modality. A ray (R) is issued from the center of the camera and passing through A. Error is measured by the perpendicular distance from B to R.
Figure 5
Figure 5
Frame used in marker-based methods fixed on a dental splint.
See this image and copyright information in PMC

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