Tag: image-guided

Robotic drill guide positioning in spine surgery using known-component 3D–2D image registration

Robotic drill guide positioning using known-component 3D–2D image registration, by Yi, Ramchandran, Siewerdsen, and Uneri, J. of Medical Imaging (2018), 5(2):021212.

Abstract:

A method for x-ray image-guided robotic instrument positioning is reported and evaluated in preclinical studies of spinal pedicle screw placement with the aim of improving delivery of transpedicle K-wires and screws. The known-component (KC) registration algorithm was used to register the three-dimensional patient CT and drill guide surface model to intraoperative two-dimensional radiographs. Resulting transformations, combined with offline hand–eye calibration, drive the robotically held drill guide to target trajectories defined in the preoperative CT. The method was assessed in comparison with a more conventional tracker-based approach, and robustness to clinically realistic errors was tested in phantom and cadaver. Deviations from planned trajectories were analyzed in terms of target registration error (TRE) at the tooltip (mm) and approach angle (deg). In phantom studies, the KC approach resulted in TRE = 1.51 ± 0.51 mm and 1.01 deg ± 0.92 deg, comparable with accuracy in tracker-based approach. In cadaver studies with realistic anatomical deformation, the KC approach yielded TRE = 2.31 ± 1.05 mm and 0.66 deg ± 0.62 deg, with statistically significant improvement versus tracker (TRE = 6.09 ± 1.22 mm and 1.06 deg ± 0.90 deg). Robustness to deformation is attributed to relatively local rigidity of anatomy in radiographic views. X-ray guidance offered accurate robotic positioning and could fit naturally within clinical workflow of fluoroscopically guided procedures.

Combining intraoperative ultrasound brain shift correction and augmented reality visualizations

brain-AR

Combining intraoperative ultrasound brain shift correction and augmented reality visualizations: a pilot study of eight cases by Gerard et al., J. of Medical Imaging, 5(2), 021210 (2018).

NOTE: You can read or download the paper at ResearchGate.

Abstract:

We present our work investigating the feasibility of combining intraoperative ultrasound for brain shift correction and augmented reality (AR) visualization for intraoperative interpretation of patient-specific models in image-guided neurosurgery (IGNS) of brain tumors. We combine two imaging technologies for image-guided brain tumor neurosurgery. Throughout surgical interventions, AR was used to assess different surgical strategies using three-dimensional (3-D) patient-specific models of the patient’s cortex, vasculature, and lesion. Ultrasound imaging was acquired intraoperatively, and preoperative images and models were registered to the intraoperative data. The quality and reliability of the AR views were evaluated with both qualitative and quantitative metrics. A pilot study of eight patients demonstrates the feasible combination of these two technologies and their complementary features. In each case, the AR visualizations enabled the surgeon to accurately visualize the anatomy and pathology of interest for an extended period of the intervention. Inaccuracies associated with misregistration, brain shift, and AR were improved in all cases. These results demonstrate the potential of combining ultrasound-based registration with AR to become a useful tool for neurosurgeons to improve intraoperative patient-specific planning by improving the understanding of complex 3-D medical imaging data and prolonging the reliable use of IGNS.

brain-AR-flowchart
Flowchart of the intraoperative workflow and how surgical tasks are related to IGNS tasks. A-Patient-to-image registration. After the patient’s head is immobilized a tracking reference is attached to the clamp and 8 facial landmarks are chosen that correspond to identical landmarks on the preoperative images to create a mapping between the two spaces. B Augmented reality visualization on the skull is being qualitatively assessed by comparing the tumor contour as defined by the preoperative guidance images and the overlay of the augmented image. C– A series of US images are acquired once the craniotomy has been performed on the dura and then reconstructed and registered with the preoperative MRI images using the gradient orientation alignment algorithm. D-Augmented reality visualization on the cortex showing the location of the tumor (green) and a vessel of interest (blue). E– The AR accuracy is quantitatively evaluated by having the surgeon choose an identifiable landmark on the physical patient, recording the coordinates, and then choosing the corresponding landmark on the augmented image, recording the coordinates and measuring the two-dimensional distance between the coordinates.