3D intra-operative ultrasound and MR image guidance: pursuing an ultrasound-based management of brainshift to enhance neuronavigation

• Published in: International journal for computer assisted radiology and surgery 2017-10, Vol.12 (10), p.1711-1725
• Main Authors: Riva, Marco, Hennersperger, Christoph, Milletari, Fausto, Katouzian, Amin, Pessina, Federico, Gutierrez-Becker, Benjamin, Castellano, Antonella, Navab, Nassir, Bello, Lorenzo
• Format: Article
• Language: English
• Subjects: Algorithms; Brain; Computer Imaging; Computer Science; Data acquisition; Deformation; Health Informatics; Image detection; Image enhancement; Imaging; Magnetic resonance imaging; Medicine; Medicine & Public Health; Original Article; Pattern Recognition and Graphics; Radiology; Registration; Surgery; Tumors; Vision; Workflow
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-017-1578-5

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; p = 0.001 ) and after dural opening (4.2 vs. 6.2 mm, p = 0.049 ), but not after resection (6.7 vs. 7.5 mm; p = 0.426 ). iUS depicts a significant ( p = 0.001 ) 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.