109593 20230519145409.0 doi 10.1007/s10851-021-01016-4 sideral 123320 ART-2021-123320 eng Hernandez, M. Universidad de Zaragoza (orcid)0000-0003-1270-5852 Combining the Band-Limited Parameterization and Semi-Lagrangian Runge–Kutta Integration for Efficient PDE-Constrained LDDMM 2021 Access copy available to the general public Unrestricted The family of PDE-constrained Large Deformation Diffeomorphic Metric Mapping (LDDMM) methods is emerging as a particularly interesting approach for physically meaningful diffeomorphic transformations. The original combination of Gauss–Newton–Krylov optimization and Runge–Kutta integration shows excellent numerical accuracy and fast convergence rate. However, its most significant limitation is the huge computational complexity, hindering its extensive use in Computational Anatomy applied studies. This limitation has been treated independently by the problem formulation in the space of band-limited vector fields and semi-Lagrangian integration. The purpose of this work is to combine both in three variants of band-limited PDE-constrained LDDMM for further increasing their computational efficiency. The accuracy of the resulting methods is evaluated extensively. For all the variants, the proposed combined approach shows a significant increment of the computational efficiency. In addition, the variant based on the deformation state equation is positioned consistently as the best performing method across all the evaluation frameworks in terms of accuracy and efficiency. info:eu-repo/grantAgreement/ES/DGA/COS2MOS research group info:eu-repo/grantAgreement/ES/MINECO/PID2019-104358RB-I00 info:eu-repo/grantAgreement/ES/MINECO/TIN2016-80347-R info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 1.627 2021 MATHEMATICS, APPLIED 107 / 267 = 0.401 2021 Q2 T2 COMPUTER SCIENCE, SOFTWARE ENGINEERING 80 / 110 = 0.727 2021 Q3 T3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 117 / 146 = 0.801 2021 Q4 T3 0.752 2021 Applied Mathematics 2021 Q2 Computer Vision and Pattern Recognition 2021 Q2 Statistics and Probability 2021 Q2 Modeling and Simulation 2021 Q2 Geometry and Topology 2021 Q2 3.8 2021 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. (2021), [25 pp] J. math. imaging vis. JOURNAL OF MATHEMATICAL IMAGING AND VISION 0924-9907 1130358 http://zaguan.unizar.es/record/109593/files/texto_completo.pdf Postprint 2249101 http://zaguan.unizar.es/record/109593/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:109593 articulos driver 2023-05-18-13:52:49 ARTICLE