AUTHOR=Chrisochoides Nikos , Liu Yixun , Drakopoulos Fotis , Kot Andriy , Foteinos Panos , Tsolakis Christos , Billias Emmanuel , Clatz Olivier , Ayache Nicholas , Fedorov Andrey , Golby Alex , Black Peter , Kikinis Ron TITLE=Comparison of physics-based deformable registration methods for image-guided neurosurgery JOURNAL=Frontiers in Digital Health VOLUME=5 YEAR=2023 URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2023.1283726 DOI=10.3389/fdgth.2023.1283726 ISSN=2673-253X ABSTRACT=

This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete data. It concludes with preliminary results on leveraging Quantum Computing, a promising new technology for computationally intensive problems like Feature Detection and Block Matching in addition to finite element solver; all three account for 75% of computing time in deformable registration.