A quadratic energy minimization framework for signal loss estimation from arbitrarily sampled ultrasound data
Hennersperger C, Mateus D, Baust M, Navab N (2014)
Publication Type: Conference contribution
Publication year: 2014
Publisher: Springer Verlag
Book Volume: 17
Pages Range: 373-380
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: USA
ISBN: 9783319104690
DOI: 10.1007/978-3-319-10470-6_47
Abstract
We present a flexible and general framework to iteratively solve quadratic energy problems on a non uniform grid, targeted at ultrasound imaging. Therefore, we model input samples as the nodes of an irregular directed graph, and define energies according to the application by setting weights to the edges. To solve the energy, we derive an effective optimization scheme, which avoids both the explicit computation of a linear system, as well as the compounding of the input data on a regular grid. The framework is validated in the context of 3D ultrasound signal loss estimation with the goal of providing an uncertainty estimate for each 3D data sample. Qualitative and quantitative results for 5 subjects and two target regions, namely US of the bone and the carotid artery, show the benefits of our approach, yielding continuous loss estimates. © 2014 Springer International Publishing.
Involved external institutions
Germany (DE)How to cite
APA:
Hennersperger, C., Mateus, D., Baust, M., & Navab, N. (2014). A quadratic energy minimization framework for signal loss estimation from arbitrarily sampled ultrasound data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 373-380). USA: Springer Verlag.
MLA:
Hennersperger, Christoph, et al. "A quadratic energy minimization framework for signal loss estimation from arbitrarily sampled ultrasound data." Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, USA Springer Verlag, 2014. 373-380.
BibTeX: Download