Marco Riva1,2, Christoph Hennersperger3, Fausto Milletari4, Amin Katouzian4,5, Federico Pessina2, Benjamin Gutierrez-Becker4,6, Antonella Castellano7, Nassir Navab4,8, Lorenzo Bello2,9. 1. Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Via Vanvitelli 32, Milan, Italy. 2. Unit of Surgical Neuro-Oncology, Humanitas Research Hospital, via Manzoni 56, Rozzano, Milan, Italy. 3. Computer Aided Medical Procedures (CAMP), Technische Universität München, Boltzmannstrae 3, Garching b. München, Germany. christoph.hennersperger@tum.de. 4. Computer Aided Medical Procedures (CAMP), Technische Universität München, Boltzmannstrae 3, Garching b. München, Germany. 5. IBM Almaden Research Center, 650 Harry Road, San Jose, CA, 95120, USA. 6. Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilian-University, Nußbaumstraße 5a, Munich, Germany. 7. Neuroradiology Unit and CERMAC, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Via Olgettina 58, Milan, Italy. 8. Computer Aided Medical Procedures (CAMP), Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, USA. 9. Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Via Festa del Perdono 7, Milan, Italy.
Abstract
BACKGROUND: Brainshift is still a major issue in neuronavigation. Incorporating intra-operative ultrasound (iUS) with advanced registration algorithms within the surgical workflow is regarded as a promising approach for a better understanding and management of brainshift. This work is intended to (1) provide three-dimensional (3D) ultrasound reconstructions specifically for brain imaging in order to detect brainshift observed intra-operatively, (2) evaluate a novel iterative intra-operative ultrasound-based deformation correction framework, and (3) validate the performance of the proposed image-registration-based deformation estimation in a clinical environment. METHODS: Eight patients with brain tumors undergoing surgical resection are enrolled in this study. For each patient, a 3D freehand iUS system is employed in combination with an intra-operative navigation (iNav) system, and intra-operative ultrasound data are acquired at three timepoints during surgery. On this foundation, we present a novel resolution-preserving 3D ultrasound reconstruction, as well as a framework to detect brainshift through iterative registration of iUS images. To validate the system, the target registration error (TRE) is evaluated for each patient, and both rigid and elastic registration algorithms are analyzed. RESULTS: The mean TRE based on 3D-iUS improves significantly using the proposed brainshift compensation compared to neuronavigation (iNav) before (2.7 vs. 5.9 mm; [Formula: see text]) and after dural opening (4.2 vs. 6.2 mm, [Formula: see text]), but not after resection (6.7 vs. 7.5 mm; [Formula: see text]). iUS depicts a significant ([Formula: see text]) dynamic spatial brainshift throughout the three timepoints. Accuracy of registration can be improved through rigid and elastic registrations by 29.2 and 33.3%, respectively, after dural opening, and by 5.2 and 0.4%, after resection. CONCLUSION: 3D-iUS systems can improve the detection of brainshift and significantly increase the accuracy of the navigation in a real scenario. 3D-iUS can thus be regarded as a robust, reliable, and feasible technology to enhance neuronavigation.
BACKGROUND: Brainshift is still a major issue in neuronavigation. Incorporating intra-operative ultrasound (iUS) with advanced registration algorithms within the surgical workflow is regarded as a promising approach for a better understanding and management of brainshift. This work is intended to (1) provide three-dimensional (3D) ultrasound reconstructions specifically for brain imaging in order to detect brainshift observed intra-operatively, (2) evaluate a novel iterative intra-operative ultrasound-based deformation correction framework, and (3) validate the performance of the proposed image-registration-based deformation estimation in a clinical environment. METHODS: Eight patients with brain tumors undergoing surgical resection are enrolled in this study. For each patient, a 3D freehand iUS system is employed in combination with an intra-operative navigation (iNav) system, and intra-operative ultrasound data are acquired at three timepoints during surgery. On this foundation, we present a novel resolution-preserving 3D ultrasound reconstruction, as well as a framework to detect brainshift through iterative registration of iUS images. To validate the system, the target registration error (TRE) is evaluated for each patient, and both rigid and elastic registration algorithms are analyzed. RESULTS: The mean TRE based on 3D-iUS improves significantly using the proposed brainshift compensation compared to neuronavigation (iNav) before (2.7 vs. 5.9 mm; [Formula: see text]) and after dural opening (4.2 vs. 6.2 mm, [Formula: see text]), but not after resection (6.7 vs. 7.5 mm; [Formula: see text]). iUS depicts a significant ([Formula: see text]) dynamic spatial brainshift throughout the three timepoints. Accuracy of registration can be improved through rigid and elastic registrations by 29.2 and 33.3%, respectively, after dural opening, and by 5.2 and 0.4%, after resection. CONCLUSION: 3D-iUS systems can improve the detection of brainshift and significantly increase the accuracy of the navigation in a real scenario. 3D-iUS can thus be regarded as a robust, reliable, and feasible technology to enhance neuronavigation.
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