Preoperative to Intraoperative Space Registration for Management of Head Injuries

Abstract

A registration framework for image-guided robotic surgery is proposed for three emergency neurosurgical procedures, namely Intracranial Pressure (ICP) Monitoring, External Ventricular Drainage (EVD) and evacuation of a Chronic Subdural Haematoma (CSDH). The registration paradigm uses CT and white light as modalities. This paper presents two simulation studies for a preliminary evaluation of the registration protocol: (1) The loci of the Target Registration Error (TRE) in the patient's axial, coronal and sagittal views were simulated based on a Fiducial Localisation Error (FLE) of 5 mm and (2) Simulation of the actual framework using projected views from a surface rendered CT model to represent white light images of the patient. Craniofacial features were employed as the registration basis to map the CT space onto the simulated intraoperative space. Photogrammetry experiments on an artificial skull were also performed to benchmark the results obtained from the second simulation. The results of both simulations show that the proposed protocol can provide a 5mm accuracy for these neurosurgical procedures. Keywords—Image-guided Surgery, Multimodality Registration, Photogrammetry, Preoperative to Intraoperative Registration. I. INTRODUCTION HIS paper presents a registration framework designed to support image-guided solutions for three neurosurgical procedures that are routinely employed in the management of head injuries. Registration is a general term used to describe the alignment of two datasets, with respect to a reference coordinate system, with the aim of reducing the disparity between them; alternatively recovering that disparity may be the goal. A registration basis consists of features chosen that relate both datasets in terms of the disparity involved. The