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

Authors

Nikos Chrisochoides, Old Dominion UniversityFollow
Yixun Liu, Old Dominion University
Fotis Drakopoulos, Old Dominion UniversityFollow
Andriy Kot, Old Dominion University
Panos Foteinos, Old Dominion University
Christos Tsolakis, Old Dominion UniversityFollow
Emmanuel Billias, Old Dominion UniversityFollow
Olivier Clatz, Inria, French Research Institute for Digital Science
Nicholas Ayache, Inria, French Research Institute for Digital Science
Andrey Fedorov, Old Dominion University
Alex Golby, Harvard Medical School
Peter Black, Harvard Medical School
Ron Kikinis, Harvard Medical School

Document Type

Article

Publication Date

2023

DOI

10.3389/fdgth.2023.1283726

Publication Title

Frontiers in Digital Health

Volume

5

Pages

1283726 (1-16)

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.

Rights

© 2023 Chrisochoides, Liu, Drakopoulos, Kot, Foteinos, Tsolakis, Billias, Clatz, Ayache, Fedorov, Golby, Black and Kikinis.

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). The use, distribution or reproduction in other forums is permitted, provided the original authors and the copyright owners are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Original Publication Citation

Chrisochoides, N. P., Fedorov, A., Liu, Y., Kot, A., Foteinos, P., Drakopoulos, F., Tsolakis, C., Billias, E., Clatz, O., Ayache, N., Golby, A., & Black, P. (2023). Comparison of physics-based deformable registration methods for image-guided neurosurgery. Frontiers in Digital Health, 5, 1-16, Article 1283726. https://doi.org/10.3389/fdgth.2023.1283726

Repository Citation

Chrisochoides, N. P., Fedorov, A., Liu, Y., Kot, A., Foteinos, P., Drakopoulos, F., Tsolakis, C., Billias, E., Clatz, O., Ayache, N., Golby, A., & Black, P. (2023). Comparison of physics-based deformable registration methods for image-guided neurosurgery. Frontiers in Digital Health, 5, 1-16, Article 1283726. https://doi.org/10.3389/fdgth.2023.1283726

ORCID

0000-0003-3088-0187 (Chrisochoides), 0000-0002-0656-9631 (Fotis), 0000-0002-0656-9631 (Tsolakis)