Intraoperative Computed Tomography and Finite Element Modelling for Multimodal Image Fusion in Brain Surgery

Abstract

BACKGROUND intraoperative computer tomography (iCT) and advanced image fusion algorithms could improve the management of brainshift and the navigation accuracy. OBJECTIVE To evaluate the performance of an iCT-based fusion algorithm using clinical data. METHODS Ten patients with brain tumors were enrolled; preoperative MRI was acquired. The iCT was applied at the end of microsurgical resection. Elastic image fusion of the preoperative MRI to iCT data was performed by deformable fusion employing a biomechanical simulation based on a finite element model. Fusion accuracy was evaluated: the target registration error (TRE, mm) was measured for rigid and elastic fusion (Rf and Ef) and anatomical landmark pairs were divided into test and control structures according to distinct involvement by the brainshift. Intraoperative points describing the stereotactic position of the brain were also acquired and a qualitative evaluation of the adaptive morphing of the preoperative MRI was performed by 5 observers. RESULTS The mean TRE for control and test structures with Rf was 1.81 ± 1.52 and 5.53 ± 2.46 mm, respectively. No significant change was observed applying Ef to control structures; the test structures showed reduced TRE values of 3.34 ± 2.10 mm after Ef (P < .001). A 32% average gain (range 9%-54%) in accuracy of image registration was recorded. The morphed MRI showed robust matching with iCT scans and intraoperative stereotactic points. CONCLUSIONS The evaluated method increased the registration accuracy of preoperative MRI and iCT data. The iCT-based non-linear morphing of the preoperative MRI can potentially enhance the consistency of neuronavigation intraoperatively.

Raman Spectroscopy and Machine Learning for IDH Genotyping of Unprocessed Glioma Biopsies

“…Thirty-eight (38) tumor samples were collected from adult subjects undergoing surgery for presumptive glioma tumors. All procedures were performed with imaging and neurophysiological guidance [12,13] in order to achieve, when feasible, a safe supra-marginal resection [3]. We collected 21 samples from IDH-mutated tumors and 17 samples from IDH-WT tumors.…”

Current Limitations of Intraoperative Ultrasound in Brain Tumor Surgery

“…While the rigid registration improved the alignment of the MRI and IOUS image volumes, considering the fact that brain-shift is a nonlinear process, deformable registration has the potential to further improve the results ( 88 ). Future sophisticated fusion algorithms might use IOUS image as anatomical reference, similarly to intraoperative CT, and enable deformation of preoperative 3D MRI image into “virtual intraoperative MRI” ( 89 ).…”

<p>Estimation of Risk Factors for Head Slippage Using a Head Clamp System. A Retrospective Study</p>

“…The thickness of each slice increased from 1 to 2.5 mm using this modification and they were automatically fused using an algorithm. 9,10 After fusion, we measured the distance between the corresponding head pins and the height of the pins from the NI-plane. We referred to the maximal distance of pins that had slipped >2.5 mm among the three pins because distances <2.4 mm could not be measured due to the thickness of the CT slices.…”