Intraoperative Computed Tomography and Finite Element Modelling for Multimodal Image Fusion in Brain Surgery.
Biomechanical simulation
Brainshift
Computed tomography
Elastic image fusion
Finite element modeling
Intraoperative imaging
Neuronavigation
Journal
Operative neurosurgery (Hagerstown, Md.)
ISSN: 2332-4260
Titre abrégé: Oper Neurosurg (Hagerstown)
Pays: United States
ID NLM: 101635417
Informations de publication
Date de publication:
01 May 2020
01 May 2020
Historique:
received:
03
01
2019
accepted:
16
04
2019
pubmed:
26
7
2019
medline:
22
6
2021
entrez:
26
7
2019
Statut:
ppublish
Résumé
intraoperative computer tomography (iCT) and advanced image fusion algorithms could improve the management of brainshift and the navigation accuracy. To evaluate the performance of an iCT-based fusion algorithm using clinical data. 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. 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. 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.
Sections du résumé
BACKGROUND
BACKGROUND
intraoperative computer tomography (iCT) and advanced image fusion algorithms could improve the management of brainshift and the navigation accuracy.
OBJECTIVE
OBJECTIVE
To evaluate the performance of an iCT-based fusion algorithm using clinical data.
METHODS
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
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
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.
Identifiants
pubmed: 31342073
pii: 5538306
doi: 10.1093/ons/opz196
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
531-541Informations de copyright
Copyright © 2019 by the Congress of Neurological Surgeons.