Fibular registration using surface matching in navigation-guided osteotomies: a proof of concept study on 3D-printed models

Robot-guided osteotomy in fibula free flap mandibular reconstruction: a preclinical study

Various methods currently exist to guide fibular osteotomy positioning in fibula free flap mandibular reconstruction, but patient-specific navigation methods and cutting guides require experience, and may be time-consuming and/or expensive. This study describes a robot-guided osteotomy technique for mandible reconstruction using a fibula free flap according to virtual preoperative planning. The method was assessed on five 3D-printed models and a cadaveric model. The precision of the robot-guided osteotomy was evaluated by measuring the deviations between the lengths and angles of the fragments obtained and those of the virtual planning. The average deviation of the anterior and posterior crest lengths was 0.42 ± 0.29 mm for the 3D-printed models and 1.00 ± 0.53 mm for the cadaveric model. The average angle deviation was 1.90 ± 1.22° and 1.94 ± 0.69° for the 3D-printed and cadaveric models, respectively. The results of this preclinical study revealed that fibular osteotomy positioning guidance using a robot-positioned cutting guide may be a precise, easy-to-use technique that could be tailored for fibula free flap mandibular reconstruction.

Contactless surface registration of featureless anatomy using structured light camera: application to fibula navigation in mandible reconstruction

PURPOSE

Mandibular reconstruction using fibula free flap is a challenging surgical procedure. To assist osteotomies, computer-assisted surgery (CAS) can be used. Nevertheless, precise registration is required and often necessitates anchored markers that disturb the patient and clinical flow. This work proposes a new contactless surface-based method adapted to featureless anatomies such as fibula to achieve a fast, precise, and reproducible registration.

METHODS

Preoperatively, a CT-scan of the patient is realized and osteotomies are virtually planned. During surgery, a structured light camera digitizes the fibula. The obtained intraoperative point cloud is coarsely registered with the preoperative model using 3 points defined in the CT-scan and located on the patient's bone with a laser beam. Then, a fine registration is performed using an ICP algorithm. The registration accuracy was evaluated comparing the position of points engraved in a 3D-printed fibula with their position in the registered model and evaluating resulting osteotomies. Accuracy and execution time were compared to a conventional stylus-based registration method. The work was validated in vivo.

RESULTS

The experiment performed on a 3D-printed model showed that execution time is equivalent to surface-based registration using a stylus, with a better accuracy (mean TRE of 0.9 mm vs 1.3 mm using stylus) and guarantee good osteotomies. The preliminary in vivo study proved the feasibility of the method.

CONCLUSION

The proposed contactless surface-based registration method using structured light camera gave promising results in terms of accuracy and execution speed and should be useful to implement CAS for mandibular reconstruction.

"Image to patient" equal-resolution surface registration supported by a surface scanner: analysis of algorithm efficiency for computer-aided surgery

PURPOSE

The "image to patient" registration procedure is crucial for the accuracy of surgical instrument tracking relative to the medical image while computer-aided surgery. The main aim of this work was to create an equal-resolution surface registration algorithm (ERSR) and analyze its efficiency.

METHODS

The ERSR algorithm provides two datasets with equal, high resolution and approximately corresponding points. The registered sets are obtained by projection of a user-designed rectangle(s)-shaped uniform clouds of points on DICOM and surface scanner datasets. The tests of the algorithm were performed on a phantom with titanium microscrews. We analyzed the influence of DICOM resolution on the effect of the ERSR algorithm and compared the ERSR to standard paired-points landmark transform registration. The methods of analysis were Target Registration Error, distance maps, and their histogram evaluation.

RESULTS

The mean TRE in case of ERSR equaled 0.8 ± 0.3 mm (resolution A), 0.8 ± 0.5 mm (resolution B), and 1.0 ± 0.7 mm (resolution C). The mean values were at least 0.4 mm lower than in the case of landmark transform registration. The distance maps between the model achieved from the scanner and the CT-based model were analyzed by histogram. The frequency of the first bin in a histogram of the distance map for ERSR was about 0.6 for all three resolutions of DICOM dataset and three times higher than in the case of landmark transform registration. The results were statistically analyzed using the Wilcoxon signed-rank test (alpha = 0.05).

CONCLUSION

The tests proved a statistically significant higher efficiency of equal resolution surface registration related to the landmark transform algorithm. It was proven that the lower resolution of the CT DICOM dataset did not degrade the efficiency of the ERSR algorithm. We observed a significantly lower response to decreased resolution than in the case of paired-points landmark transform registration.