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Comparison of Physics-Based Deformable Registration Methods for Image-Guided Neurosurgery

 

Nikos Chrisochoides, Yixun Liu, Fotios Drakopoulos, Andriy Kot, Panagiotis Foteinos, Christos Tsolakis, Emmanuel Billias, Olivier Clatz, Nicholas Ayache, Andrey Fedorov, Alexandra Golby, Peter Black and Ron Kikinis.

 

Accepted in Frontiers in Digital Health, Volume 5, November, 2023

 

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 2.5mm and meets the real-time constraints (4 min). In addition, the paper raises several questions. It 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.