Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/55594
Título
Efficient convolution-based pairwise elastic image registration on three multimodal similarity metrics
Autor
Año del Documento
2023
Editorial
Elsevier
Descripción
Producción Científica
Documento Fuente
Signal Processing, 2023, vol. 202, 108771
Abstract
This paper proposes a complete convolutional formulation for 2D multimodal pairwise image registration problems based on free-form deformations. We have reformulated in terms of discrete 1D convolutions the evaluation of spatial transformations, the regularization term, and their gradients for three different multimodal registration metrics, namely, normalized cross correlation, mutual information, and normalized mutual information. A sufficient condition on the metric gradient is provided for further extension to other metrics. The proposed approach has been tested, as a proof of concept, on contrast-enhanced first-pass perfusion cardiac magnetic resonance images. Execution times have been compared with the corresponding execution times of the classical tensor product formulation, both on CPU and GPU. The speed-up achieved by using convolutions instead of tensor products depends on the image size and the number of control points considered, the larger those magnitudes, the greater the execution time reduction. Furthermore, the speed-up will be more significant when gradient operations constitute the major bottleneck in the optimization process.
Materias (normalizadas)
Multimodal registration
Registro multimodal
Convolution
Convolución
Non-rigid registration
Registro no rígido
Materias Unesco
3325 Tecnología de las Telecomunicaciones
ISSN
0165-1684
Revisión por pares
SI
Patrocinador
Ministerio de Economía, Industria y Competitividad (grants TEC2017-82408-R and PID2020-115339RB-I00)
ESAOTE Ltd (grant 18IQBM)
ESAOTE Ltd (grant 18IQBM)
Propietario de los Derechos
© 2022 The Authors
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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