Marco Riva1,2, Patrick Hiepe3, Mona Frommert3, Ignazio Divenuto4, Lorenzo G Gay2, Tommaso Sciortino2, Marco Conti Nibali2, Marco Rossi2, Federico Pessina2,5, Lorenzo Bello2,6. 1. Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy. 2. Unit of Oncological Neurosurgery, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy. 3. Brainlab A.G., München, Germany. 4. Unit of Neuroradiology, Humanitas Clinical and Research Center - IRCCS, Rozzano, Italy. 5. Department of Biomedical Sciences, Humanitas University, Rozzano, Italy. 6. Department of Oncology and Hemato-oncology, Università degli Studi di Milano, Milan, Italy.
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.
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.
Authors: Andrej Šteňo; Ján Buvala; Veronika Babková; Adrián Kiss; David Toma; Alexander Lysak Journal: Front Oncol Date: 2021-03-22 Impact factor: 6.244
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Authors: Marco Riva; Tommaso Sciortino; Riccardo Secoli; Ester D'Amico; Sara Moccia; Bethania Fernandes; Marco Conti Nibali; Lorenzo Gay; Marco Rossi; Elena De Momi; Lorenzo Bello Journal: Cancers (Basel) Date: 2021-03-03 Impact factor: 6.639