000109593 001__ 109593
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000109593 0247_ $$2doi$$a10.1007/s10851-021-01016-4
000109593 0248_ $$2sideral$$a123320
000109593 037__ $$aART-2021-123320
000109593 041__ $$aeng
000109593 100__ $$0(orcid)0000-0003-1270-5852$$aHernandez, M.$$uUniversidad de Zaragoza
000109593 245__ $$aCombining the Band-Limited Parameterization and Semi-Lagrangian Runge–Kutta Integration for Efficient PDE-Constrained LDDMM
000109593 260__ $$c2021
000109593 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109593 5203_ $$aThe 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.
000109593 536__ $$9info:eu-repo/grantAgreement/ES/DGA/COS2MOS research group$$9info:eu-repo/grantAgreement/ES/MINECO/PID2019-104358RB-I00$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2016-80347-R
000109593 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000109593 590__ $$a1.627$$b2021
000109593 592__ $$a0.752$$b2021
000109593 594__ $$a3.8$$b2021
000109593 591__ $$aMATHEMATICS, APPLIED$$b107 / 267 = 0.401$$c2021$$dQ2$$eT2
000109593 593__ $$aApplied Mathematics$$c2021$$dQ2
000109593 591__ $$aCOMPUTER SCIENCE, SOFTWARE ENGINEERING$$b80 / 110 = 0.727$$c2021$$dQ3$$eT3
000109593 593__ $$aComputer Vision and Pattern Recognition$$c2021$$dQ2
000109593 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b117 / 146 = 0.801$$c2021$$dQ4$$eT3
000109593 593__ $$aStatistics and Probability$$c2021$$dQ2
000109593 593__ $$aModeling and Simulation$$c2021$$dQ2
000109593 593__ $$aGeometry and Topology$$c2021$$dQ2
000109593 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000109593 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000109593 773__ $$g(2021), [25 pp]$$pJ. math. imaging vis.$$tJOURNAL OF MATHEMATICAL IMAGING AND VISION$$x0924-9907
000109593 8564_ $$s1130358$$uhttps://zaguan.unizar.es/record/109593/files/texto_completo.pdf$$yPostprint
000109593 8564_ $$s2249101$$uhttps://zaguan.unizar.es/record/109593/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000109593 909CO $$ooai:zaguan.unizar.es:109593$$particulos$$pdriver
000109593 951__ $$a2023-05-18-13:52:49
000109593 980__ $$aARTICLE