Automatic rigid image Fusion of preoperative MR and intraoperative US acquired after craniotomy

Abstract

Background Neuronavigation of preoperative MRI is limited by several errors. Intraoperative ultrasound (iUS) with navigated probes that provide automatic superposition of pre-operative MRI and iUS and three-dimensional iUS reconstruction may overcome some of these limitations. Aim of the present study is to verify the accuracy of an automatic MRI – iUS fusion algorithm to improve MR-based neuronavigation accuracy. Methods An algorithm using Linear Correlation of Linear Combination (LC2)-based similarity metric has been retrospectively evaluated for twelve datasets acquired in patients with brain tumor. A series of landmarks were defined both in MRI and iUS scans. The Target Registration Error (TRE) was determined for each pair of landmarks before and after the automatic Rigid Image Fusion (RIF). The algorithm has been tested on two conditions of the initial image alignment: registration-based fusion (RBF), as given by the navigated ultrasound probe, and different simulated course alignments during convergence test. Results Except for one case RIF was successfully applied in all patients considering the RBF as initial alignment. Here, mean TRE after RBF was significantly reduced from 4.03 (± 1.40) mm to (2.08 ± 0.96 mm) (p = 0.002), after RIF. For convergence test, the mean TRE value after initial perturbations was 8.82 (± 0.23) mm which has been reduced to a mean TRE of 2.64 (± 1.20) mm after RIF (p < 0.001). Conclusions The integration of an automatic image fusion method for co-registration of pre-operative MRI and iUS data may improve the accuracy in MR-based neuronavigation.

Enabling Navigation and Augmented Reality in the Sitting Position in Posterior Fossa Surgery Using Intraoperative Ultrasound

“…Another approach implemented rigid co-registration for CT and iUS data [ 27 ] and was later adapted to MRI and iUS data implementing the Linear Correlation of Linear Combination (LC2) similarity metric, allowing for rigid image co-registration over a couple of seconds [ 28 ], which has been successfully technically evaluated within the CuRIOUS2018 Challenge [ 69 ]. Based on this approach, rigid image-based MRI-iUS co-registration has been implemented (Brainlab, Munich, Germany), and a prior release has been applied in a retrospective case series including patients with intracranial lesions in a comparable setup as that used in this study [ 70 ] and released as a fully integrated part of the navigational system, easing the intraoperative workflow and usage. The results of the CuRIOUS2018 Challenge and the prerelease evaluation of a prototype showed a significant decrease in the navigation inaccuracy by iUS-based navigation updates relying on rigid image-based co-registration.…”

“…Besides the spatial overlap of lesions seen in the MRI and iUS data, the quality of co-registration was also assessed in a landmark-based manner, comparing the distances between the corresponding landmarks in both modalities, as previously performed in a recent pilot study with supratentorial lesions [ 70 ]. In this study on infratentorial lesions surgically treated in the sitting position, initial reference array-based registration led to a mean Euclidean distance of 8.69 ± 6.23 mm (ranging from 2.85 mm to 37.2 mm), which was also significantly improved by rigid image-based co-registration with a mean Euclidean distance of 3.19 ± 2.73 mm (ranging from 1.77 mm to 6.40 mm).…”