000118194 001__ 118194 000118194 005__ 20240319081020.0 000118194 0247_ $$2doi$$a10.3390/s22103735 000118194 0248_ $$2sideral$$a129582 000118194 037__ $$aART-2022-129582 000118194 041__ $$aeng 000118194 100__ $$0(orcid)0000-0003-1270-5852$$aHernández Giménez, M.$$uUniversidad de Zaragoza 000118194 245__ $$aPartial Differential Equation-Constrained Diffeomorphic Registration from Sum of Squared Differences to Normalized Cross-Correlation, Normalized Gradient Fields, and Mutual Information: A Unifying Framework; 35632143 000118194 260__ $$c2022 000118194 5060_ $$aAccess copy available to the general public$$fUnrestricted 000118194 5203_ $$aThis work proposes a unifying framework for extending PDE-constrained Large Deformation Diffeomorphic Metric Mapping (PDE-LDDMM) with the sum of squared differences (SSD) to PDE-LDDMM with different image similarity metrics. We focused on the two best-performing variants of PDE-LDDMM with the spatial and band-limited parameterizations of diffeomorphisms. We derived the equations for gradient-descent and Gauss-Newton-Krylov (GNK) optimization with Normalized Cross-Correlation (NCC), its local version (lNCC), Normalized Gradient Fields (NGFs), and Mutual Information (MI). PDE-LDDMM with GNK was successfully implemented for NCC and lNCC, substantially improving the registration results of SSD. For these metrics, GNK optimization outperformed gradient-descent. However, for NGFs, GNK optimization was not able to overpass the performance of gradient-descent. For MI, GNK optimization involved the product of huge dense matrices, requesting an unaffordable memory load. The extensive evaluation reported the band-limited version of PDE-LDDMM based on the deformation state equation with NCC and lNCC image similarities among the best performing PDE-LDDMM methods. In comparison with benchmark deep learning-based methods, our proposal reached or surpassed the accuracy of the best-performing models. In NIREP16, several configurations of PDE-LDDMM outperformed ANTS-lNCC, the best benchmark method. Although NGFs and MI usually underperformed the other metrics in our evaluation, these metrics showed potentially competitive results in a multimodal deformable experiment. We believe that our proposed image similarity extension over PDE-LDDMM will promote the use of physically meaningful diffeomorphisms in a wide variety of clinical applications depending on deformable image registration. 000118194 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000118194 590__ $$a3.9$$b2022 000118194 592__ $$a0.764$$b2022 000118194 591__ $$aCHEMISTRY, ANALYTICAL$$b26 / 86 = 0.302$$c2022$$dQ2$$eT1 000118194 593__ $$aInstrumentation$$c2022$$dQ1 000118194 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b19 / 63 = 0.302$$c2022$$dQ2$$eT1 000118194 593__ $$aAnalytical Chemistry$$c2022$$dQ1 000118194 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b100 / 274 = 0.365$$c2022$$dQ2$$eT2 000118194 593__ $$aMedicine (miscellaneous)$$c2022$$dQ2 000118194 593__ $$aInformation Systems$$c2022$$dQ2 000118194 593__ $$aBiochemistry$$c2022$$dQ2 000118194 593__ $$aAtomic and Molecular Physics, and Optics$$c2022$$dQ2 000118194 593__ $$aElectrical and Electronic Engineering$$c2022$$dQ2 000118194 594__ $$a6.8$$b2022 000118194 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000118194 700__ $$aRamón Júlvez, U.$$uUniversidad de Zaragoza 000118194 700__ $$aSierra Tomé, D. 000118194 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000118194 773__ $$g22, 10 (2022), 3735 [35 pp]$$pSensors$$tSensors$$x1424-8220 000118194 8564_ $$s3497300$$uhttps://zaguan.unizar.es/record/118194/files/texto_completo.pdf$$yVersión publicada 000118194 8564_ $$s2841815$$uhttps://zaguan.unizar.es/record/118194/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000118194 909CO $$ooai:zaguan.unizar.es:118194$$particulos$$pdriver 000118194 951__ $$a2024-03-18-16:04:11 000118194 980__ $$aARTICLE