Partial Differential Equation-Constrained Diffeomorphic Registration from Sum of Squared Differences to Normalized Cross-Correlation, Normalized Gradient Fields, and Mutual Information: A Unifying Framework; 35632143

Hernández Giménez, M. (Universidad de Zaragoza) ; Ramón Júlvez, U. (Universidad de Zaragoza) ; Sierra Tomé, D.

Resumen: This 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.

Idioma: Inglés

DOI: 10.3390/s22103735

Año: 2022

Publicado en: Sensors 22, 10 (2022), 3735 [35 pp]
ISSN: 1424-8220

Factor impacto JCR: 3.9 (2022)

Categ. JCR: CHEMISTRY, ANALYTICAL rank: 26 / 86 = 0.302 (2022) - Q2 - T1
Categ. JCR: INSTRUMENTS & INSTRUMENTATION rank: 19 / 63 = 0.302 (2022) - Q2 - T1
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 100 / 274 = 0.365 (2022) - Q2 - T2

Factor impacto CITESCORE: 6.8 - Engineering (Q1) - Chemistry (Q1) - Biochemistry, Genetics and Molecular Biology (Q2) - Physics and Astronomy (Q1)

Factor impacto SCIMAGO: 0.764 - Instrumentation (Q1) - Analytical Chemistry (Q1) - Medicine (miscellaneous) (Q2) - Information Systems (Q2) - Biochemistry (Q2) - Atomic and Molecular Physics, and Optics (Q2) - Electrical and Electronic Engineering (Q2)

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)