A Hybrid 3D-2D Image Registration Framework for Pedicle Screw Trajectory Registration between Intraoperative X-ray Image and Preoperative CT Image

Pedicle screw insertion is considered a complex surgery among Orthopaedics surgeons. Exclusively to prevent postoperative complications associated with pedicle screw insertion, various types of image intensity registration-based navigation systems have been developed. These systems are computation-intensive, have a small capture range and have local maxima issues. On the other hand, deep learning-based techniques lack registration generalizability and have data dependency. To overcome these limitations, a patient-specific hybrid 3D-2D registration principled framework was designed to map a pedicle screw trajectory between intraoperative X-ray image and preoperative CT image. An anatomical landmark-based 3D-2D Iterative Control Point (ICP) registration was performed to register a pedicular marker pose between the X-ray images and axial preoperative CT images. The registration framework was clinically validated by generating projection images possessing an optimal match with intraoperative X-ray images at the corresponding control point registration. The effectiveness of the registered trajectory was evaluated in terms of displacement and directional errors after reprojecting its position on 2D radiographic planes. The mean Euclidean distances for the Head and Tail end of the reprojected trajectory from the actual trajectory in the AP and lateral planes were shown to be 0.6-0.8 mm and 0.5-1.6 mm, respectively. Similarly, the corresponding mean directional errors were found to be 4.90 and 20. The mean trajectory length difference between the actual and registered trajectory was shown to be 2.67 mm. The approximate time required in the intraoperative environment to axially map the marker position for a single vertebra was found to be 3 min. Utilizing the markerless registration techniques, the designed framework functions like a screw navigation tool, and assures the quality of surgery being performed by limiting the need of postoperative CT.

Medienart:	E-Artikel

Erscheinungsjahr:	2022
Erschienen:	2022

Enthalten in:	Zur Gesamtaufnahme - volume:8
Enthalten in:	Journal of imaging - 8(2022), 7 vom: 06. Juli

Sprache:	Englisch

Beteiligte Personen:	Naik, Roshan Ramakrishna [VerfasserIn] Hoblidar, Anitha [VerfasserIn] Bhat, Shyamasunder N [VerfasserIn] Ampar, Nishanth [VerfasserIn] Kundangar, Raghuraj [VerfasserIn]

Links:	Volltext

Themen:	3D-2D registration Digitally reconstructed radiographs Iterative control point registration Journal Article Optimization Pedicle screw insertion Similarity metric

Anmerkungen:	Date Revised 28.07.2022 published: Electronic Citation Status PubMed-not-MEDLINE

doi:	10.3390/jimaging8070185

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	NLM34397522X