Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery

Abstract

Objective-The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the preoperative MR data and the intraoperative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI.Materials and Methods-Eleven consecutive patients with supratentorial gliomas were included in our study. All underwent surgery at our intra-operative MR imaging-guided therapy facility and have tumors in eloquent brain areas (e.g. precentral gyrus and cortico-spinal tract). Functional MRI and DT-MRI, together with MPRAGE and T2w structural MRI were acquired at 3T prior to surgery. SPGR and T2w images were acquired with a 0.5T magnet during each procedure. Quantitative assessment of the alignment accuracy was carried out and compared with current state-of the-art systems based only on rigid-registration.Results-Alignment between preoperative and intra-operative datasets was successfully carried out during surgery for all patients. Overall, the mean residual displacement remaining after nonrigid registration was 1.82 mm. There is a statistically significant improvement in alignment accuracy utilizing our non-rigid registration in comparison to the currently used technology (p<0.001).Conclusions-We were able to achieve intra-operative rigid and non-rigid registration of (1) pre-operative structural MRI with intra-operative T1w MRI; (2) pre-operative FMRI with intraoperative T1w MRI, and (3) pre-operative DT-MRI with intra-operative T1w MRI. The registration algorithms as implemented were sufficiently robust and rapid to meet the hard realtime constraints of intra-operative surgical decision making. The validation experiments demonstrate that we can accurately compensate for the deformation of the brain and thus can construct an augmented reality visualization to aid the surgeon.

Editorial: Ultrasound-guided neurosurgery: experiences from 20 years of cross-disciplinary research in Trondheim, Norway

“…2,4,6,14,18 The use of ultrasound for imaging in neurosurgery has been promoted by several groups that have explored the use of B-mode and Doppler mode as well as elastography mode and contrastenhanced ultrasound. 7,13,16,21 Cross-disciplinary research within ultrasound imaging has quite a long history in Trondheim.…”

Grid-Enabled Software Environment for Enhanced Dynamic Data-Driven Visualization and Navigation During Image-Guided Neurosurgery

“…The registration method was first presented in [1], and subsequently evaluated in [4]. The computation consists of preoperative and intraoperative components.…”

“…the patient-specific tetrahedral mesh model is generated from the segmented intra-cranial cavity (ICC). The ICC segmentation [4] is prepared based on pre-operative imaging data. As the first intraoperative scan is available, preoperative data is rigidly (i.e., using translation and rotation) aligned with the patient's head position; 2. with the acquisition of the intraoperative image showing brain deformation, sparse displacement field is estimated from the intra-operative scan using blockmatching [1].…”

Automated 2D Segmentation of Prostate in T2-weighted MRI Scans

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