@article {960, title = {Fast 4D elastic group-wise image registration. Convolutional interpolation revisited}, journal = {Computer Methods and Programs in Biomedicine}, volume = {200}, year = {2021}, pages = {105812}, abstract = {

Background and Objective:This paper proposes a new and highly efficient implementation of 3D+t groupwise registration based on the free-form deformation paradigm. Methods:Deformation is posed as a cascade of 1D convolutions, achieving great reduction in execution time for evaluation of transformations and gradients. Results:The proposed method has been applied to 4D cardiac MRI and 4D thoracic CT monomodal datasets. Results show an average runtime reduction above 90\%, both in CPU and GPU executions, compared with the classical tensor product formulation. Conclusions:Our implementation, although fully developed for the metric sum of squared differences, can be extended to other metrics and its adaptation to multiresolution strategies is straightforward. Therefore, it can be extremely useful to speed up image registration procedures in different applications where high dimensional data are involved.

}, keywords = {B-splines, Convolution, Efficient implementation, Free-form deformation, Groupwise Registration, Non-rigid registration}, issn = {0169-2607}, doi = {https://doi.org/10.1016/j.cmpb.2020.105812}, url = {https://www.sciencedirect.com/science/article/pii/S016926072031645X}, author = {Rosa-Mar{\'\i}a Mench{\'o}n-Lara and Javier Royuela-del-Val and Federico Simmross-Wattenberg and Pablo Casaseca-de-la-Higuera and Marcos Mart{\'\i}n-Fern{\'a}ndez and Carlos Alberola-L{\'o}pez} } @article {795, title = {Space-time variant weighted regularization in compressed sensing cardiac cine MRI}, journal = {Magnetic Resonance Imaging}, volume = {58}, year = {2019}, pages = {44 - 55}, abstract = {

Purpose: To analyze the impact on image quality and motion fidelity of a motion-weighted space-time variant regularization term in compressed sensing cardiac cine MRI.
Methods: k-t SPARSE-SENSE with temporal total variation (tTV) is used as the base reconstruction algorithm. Motion in the dynamic image is estimated by means of a robust registration technique for non-rigid motion. The resulting deformation fields are used to leverage the regularization term. The results are compared with standard k-t SPARSE-SENSE with tTV regularization as well as with an improved version of this algorithm that makes use of tTV and temporal Fast Fourier Transform regularization in x-f domain.
Results: The proposed method with space-time variant regularization provides higher motion fidelity and image quality than the two previously reported methods. Difference images between undersampled reconstruction and fully sampled reference images show less systematic errors with the proposed approach.
Conclusions: Usage of a space-time variant regularization offers reconstructions with better image quality than the state of the art approaches used for comparison.

}, keywords = {Cine cardiac MRI, Space-time variant regularization, compressed sensing, k-t SPARSE-SENSE}, issn = {0730-725X}, doi = {https://doi.org/10.1016/j.mri.2019.01.005}, url = {http://www.sciencedirect.com/science/article/pii/S0730725X18301978}, author = {Alejandro Godino-Moya and J Royuela-del-Val and Muhammad Usman and Rosa-Mar{\'\i}a Mench{\'o}n-Lara and Marcos Mart{\'\i}n-Fern{\'a}ndez and Claudia Prieto and Carlos Alberola-L{\'o}pez} }