@article {925, title = {A Clinically Viable Vendor-Independent and Device-Agnostic Solution for Accelerated Cardiac MRI Reconstruction}, journal = {Computer Methods and Programs in Biomedicine}, volume = {207}, year = {2021}, chapter = {106143}, issn = {0169-2607}, doi = {https://doi.org/10.1016/j.cmpb.2021.106143}, url = {https://www.sciencedirect.com/science/article/pii/S0169260721002182}, author = {Mart{\'\i}n-Gonz{\'a}lez, Elena and Elisa Moya-S{\'a}ez and Mench{\'o}n-Lara, Rosa-Mar{\'\i}a and J Royuela-del-Val and Palencia-de-Lara, C{\'e}sar and M. Rodr{\'\i}guez-Cayetano and Simmross-Wattenberg, Federico and Carlos Alberola-Lopez} } @article {807, title = {OpenCLIPER: An OpenCL-Based C++ Framework for Overhead-Reduced Medical Image Processing and Reconstruction on Heterogeneous Devices}, journal = {IEEE Journal of Biomedical and Health Informatics}, volume = {23}, year = {2019}, month = {July}, pages = {1702-1709}, abstract = {

Medical image processing is often limited by the computational cost of the involved algorithms. Whereas dedicated computing devices (GPUs in particular) exist and do provide significant efficiency boosts, they have an extra cost of use in terms of housekeeping tasks (device selection and initialization, data streaming, synchronization with the CPU, and others), which may hinder developers from using them. This paper describes an OpenCL-based framework that is capable of handling dedicated computing devices seamlessly and that allows the developer to concentrate on image processing tasks. The framework handles automatically device discovery and initialization, data transfers to and from the device and the file system and kernel loading and compiling. Data structures need to be defined only once independently of the computing device; code is unique, consequently, for every device, including the host CPU. Pinned memory/buffer mapping is used to achieve maximum performance in data transfers. Code fragments included in the paper show how the computing device is almost immediately and effortlessly available to the users algorithms, so they can focus on productive work. Code required for device selection and initialization, data loading and streaming and kernel compilation is minimal and systematic. Algorithms can be thought of as mathematical operators (called processes), with input, output and parameters, and they may be chained one after another easily and efficiently. Also for efficiency, processes can have their initialization work split from their core workload, so process chains and loops do not incur in performance penalties. Algorithm code is independent of the device type targeted.

}, keywords = {C++, C++ languages, Data structures, GPU, Graphics processing units, Image reconstruction, Informatics, Kernel, Libraries, Medical imaging, OpenCL}, issn = {2168-2194}, doi = {10.1109/JBHI.2018.2869421}, author = {Federico Simmross-Wattenberg and M. Rodr{\'\i}guez-Cayetano and J Royuela-del-Val and E. Mart{\'\i}n-Gonz{\'a}lez and E. Moya-S{\'a}ez and M. Mart{\'\i}n-Fern{\'a}ndez and C. 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} } @conference {791, title = {On the Construction of Non Linear Adjoint Operators: Application to L1-Penalty Dynamic Image Reconstruction}, booktitle = {Congreso Anual de Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica (CASEIB)}, year = {2018}, month = {11/2018}, address = {Ciudad Real, Espa{\~n}a}, abstract = {

The purpose of this work is to develop a methodology for the adjoint operators application in non linear optimization problems. The use of adjoint operators is very popular for numerical control theory; one of its main applications is devised for image reconstruction. Most of these reconstruction techniques are limited to linear L1-constraints whose adjoints are well-defined. We aim to extend these image reconstruction techniques allowing the terms involved to be non linear. For these purpose, we have generalized the concept of adjoint operator under the basis of Taylor{\textquoteright}s formula, using Gateaux derivatives in order to construct a linearised adjoint operator associated to the non linear operator. The proposed approach has been validated in a Magnetic Resonance Imaging (MRI) reconstruction framework with Cartesian subsampled k-space data using Compressed Sensing based techniques and a groupwise registration algorithm for motion compensation.
The proposed algorithm has shown to be able to effectively deal with the presence of both physiological motion and subsampling artefacts, increasing accuracy and robustness of the reconstruction as compared with its linear counterpart.

}, author = {Santiago Sanz-Est{\'e}banez and Elisa Moya-S{\'a}ez and J Royuela-del-Val and Carlos Alberola-L{\'o}pez} } @article {733, title = {Joint Groupwise Registration and ADC Estimation in the Liver using a B-Value Weighted Metric}, journal = {Magnetic Resonance Imaging}, volume = {46}, year = {2018}, month = {2018}, pages = {1-8}, type = {Original Contribution}, chapter = {1}, abstract = {

The purpose of this work is to develop a groupwise elastic multimodal registration algorithm for robust ADC estimation in the liver on multiple breath hold diffusion weighted images.

We introduce a joint formulation to simultaneously solve both the registration and the estimation problems. In order to avoid non-reliable transformations and undesirable noise amplification, we have included appropriate smoothness constraints for both problems. Our metric incorporates the ADC estimation residuals, which are inversely weighted according to the signal content in each diffusion weighted image.

Results show that the joint formulation provides a statistically significant improvement in the accuracy of the ADC estimates. Reproducibility has also been measured on real data in terms of the distribution of ADC differences obtained from different\ b-values\ subsets.\ 

The proposed algorithm is able to effectively deal with both the presence of motion and the geometric distortions, increasing accuracy and reproducibility in diffusion parameters estimation.

}, keywords = {ADC Estimation, Diffusion Weighted Imaging, Groupwise Registration, Joint Optimization, Residual Minimization Metric}, doi = {https://doi.org/10.1016/j.mri.2017.10.002}, url = {http://www.sciencedirect.com/science/article/pii/S0730725X17302187}, author = {Santiago Sanz-Est{\'e}banez and I{\~n}aki Rabanillo-Viloria and J Royuela-del-Val and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @conference {740, title = {ADC-Weighted Joint Registration-Estimation for Cardiac Diffusion Magnetic Resonance Imaging}, booktitle = {Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, year = {2017}, month = {12/2017}, abstract = {

The purpose of this work is to develop a method for the groupwise registration of diffusion weighted datasets of the heart which automatically provide smooth Apparent Diffusion Coefficient (ADC) estimations, by making use of a novel multimodal scheme. To this
end, we have introduced a joint methodology that simultaneously performs both the alignment of the images and the ADC estimation. In order to promote diffeomorphic transformations and to avoid undesirable noise amplification, we have included appropriate
smoothness constraints for both problems under the same formulation. The implemented multimodal registration metric incorporates the ADC estimation residuals, which are inversely weighted with the b-values to balance the influence of the signal level for each diffusion weighted image. Results show that the joint formulation provides more robust and precise ADC estimations and a significant improvement in the overlap of the contour
of manual delineations along the different b-values. The proposed algorithm is able to effectively deal with the presence of both physiological motion and inherent contrast variability for the different b-value images, increasing accuracy and robustness of the estimation of diffusion parameters for cardiac imaging.

}, author = {Santiago Sanz-Est{\'e}banez and J Royuela-del-Val and Jordi Broncano-Cabrero and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @article {743, title = {Libstable: Fast, parallel, and high-precision computation of α-stable distributions in R, C/C++, and MATLAB}, journal = {Journal of Statistical Software}, volume = {78}, year = {2017}, pages = {1-25}, doi = {10.18637/jss.v078.i01}, author = {J Royuela-del-Val and Federico Simmross-Wattenberg and Carlos Alberola-Lopez} } @conference {600, title = {An Automated Tensorial Classification Procedure for Left Ventricular Hypertrophic Cardiomyopathy}, booktitle = {IWBBIO 2016 (4th International Work-Conference on Bioinformatics and Biomedical Engineering)}, volume = {1}, year = {2016}, month = {2016}, pages = {1-12}, edition = {LNBI 9656}, address = {Granada, Spain}, abstract = {

Cardiovascular diseases are the leading cause of death globally. Therefore, classi cation tools play a major role in prevention and\ treatment of these diseases. Statistical learning theory applied to magnetic resonance imaging has led to the diagnosis of a variety of cardiomyopathies states.\ We propose a two-stage classi cation scheme capable of\ distinguishing between heterogeneous groups of hypertrophic cardiomyopathies and healthy patients.\ A multimodal processing pipeline is employed to estimate robust tensorial descriptors of myocardial mechanical\ properties for both short-axis and long-axis magnetic resonance tagged\ images using the least absolute deviation method. A homomorphic ltering procedure is used to align the cine segmentations to the tagged sequence and provides 3D tensor information in meaningful areas.\ Results\ have shown that the proposed pipeline provides tensorial measurements\ on which classi ers for the study of hypertrophic cardiomyopathies can\ be built with acceptable performance even for reduced samples sets.

}, keywords = {Fuzzy clustering, HARmonic Phase, Homomorphic Filtering, Hypertrophic Cardiomyopathy, Least Absolute Deviation, Magnetic Resonance Tagging, Support Vector Machines}, doi = {10.1007/978-3-319-31744-1 17}, author = {Santiago Sanz-Est{\'e}banez and J Royuela-del-Val and S. Merino-Caviedes and A. Revilla-Orodea and T. Sevilla-Ruiz and Martin-Fernandez, M and Carlos Alberola-Lopez} } @conference {663, title = {Cardio-respiratory motion estimation for compressed sensing reconstruction of free-breathing 2D cine MRI}, booktitle = {Modulated/Incomplete Data 2016, SFB Workshop.}, year = {2016}, month = {2016}, publisher = {Mathematical Optimization and Applications in Biomedical Sciences (MOBIS), SFB Research Center}, organization = {Mathematical Optimization and Applications in Biomedical Sciences (MOBIS), SFB Research Center}, address = {Graz, Austria}, abstract = {

Respiratory motion is still an issue in MRI of the heart despite the introduction of Compressed Sensing (CS) techniques, which significantly accelerate acquisition [1]. Recently [2], a double-binning scheme was introduced in which k-space data is split according both to the cardiac and respiratory phases (Fig. 1); at reconstruction, sparsity along both dimensions is exploited. Other methods introduce motion estimation and compensation in CS (MC-CS) either to correct the respiratory motion [3] or to promote sparsity for reconstruction improvement [4]. In this work, we propose a technique to jointly estimate the respiratory and cardiac motions within a double-binning scheme, enabling the MC-CS reconstruction of respiratory resolved free-breathing 2D CINE MRI. Preliminary results on synthetic, highly undersampled (x16) Cartesian setup are shown.

[1] Lustig et al. MRM 2007, [2] Feng et al. MRM 2015, [3] Usman et al. MRM 2013. [4]\ Royuela-del-Val et al. MRM 2015.

}, author = {J Royuela-del-Val and Marcos Mart{\'\i}n-Fern{\'a}ndez and Federico Simmross-Wattenberg and Carlos Alberola-Lopez} } @conference {687, title = {Harmonic Auto-Regularization for Non Rigid Groupwise Registration in Cardiac Magnetic Resonance Imaging.}, booktitle = {Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica 2016}, year = {2016}, month = {11/2016}, address = {Valencia, Spain}, abstract = {

In this paper we present a new approach for non rigid groupwise registration of cardiac magnetic resonance images by means of free-form deformations, imposing a prior harmonic deformation assumption. The procedure proposes a primal-dual framework for solving an equality constrained minimization problem, which allows an automatic estimate of the trade-off between image fidelity and the Laplacian smoothness terms for each iteration. The method has been applied to both a 4D extended cardio-torso phantom and to a set of voluntary patients. The accuracy of the method has been measured for the synthetic experiment as the difference in modulus between the estimated displacement field and the ground truth; as for the real data, we have calculated the Dice coefficient between the contour manual delineations provided by two cardiologists at end systolic phase and those provided by them at end diastolic phase and, consequently propagated by the registration algorithm to the systolic instant. The automatic procedure turns out to be competitive in motion compensation with other methods even though their parameters have been previously set for optimal performance in different scenarios.

}, author = {Santiago Sanz-Est{\'e}banez and J Royuela-del-Val and T. Sevilla-Ruiz and Revilla-Orodea, A. and Santiago Aja-Fern{\'a}ndez and Carlos Alberola-Lopez} } @article {602, title = {Jacobian weighted temporal total variation for motion compensated compressed sensing reconstruction of dynamic MRI}, journal = {Magnetic Resonance in Medicine}, year = {2016}, month = {2016}, abstract = {

Purpose:\ To eliminate the need of spatial intraframe regularization in a recently reported dynamic MRI compressed-sensing-based reconstruction method with motion compensation and to increase its performance.

Theory and Methods: We propose a new regularization metric based on the introduction of a spatial weighting measure given by the Jacobian of the estimated deformations. It shows convenient discretization properties and, as a byproduct, it also provides a theoretical support to a result reported by others based on an intuitive design. The method has been applied to the reconstruction of both short and long axis views of the heart of four healthy volunteers. Quantitative image quality metrics as well as straightforward visual assessment are reported.

Results: Short and long axis reconstructions of cardiac cine MRI sequences have shown superior results than previously reported methods both in terms of quantitative metrics and of visual assessment. Fine details are better preserved due to the lack of additional intraframe regularization, with no significant image artifacts even for an acceleration factor of 12.

Conclusions: The proposed Jacobian Weighted temporal Total Variation results in better reconstructions of highly undersampled cardiac cine MRI than previously proposed methods and sets a theoretical ground for forward and backward predictors used elsewhere.

}, keywords = {compressed sensing, dynamic MRI reconstruction, group-wise registration, motion estimation}, doi = {10.1002/mrm.26198}, url = {http://onlinelibrary.wiley.com/doi/10.1002/mrm.26198}, author = {J Royuela-del-Val and Lucilio Cordero-Grande and Federico Simmross-Wattenberg and Mart{\'\i}n-Fern{\'a}ndez, M and Carlos Alberola-Lopez} } @article {597, title = {Multi-oriented windowed harmonic phase reconstruction for robust cardiac strain imaging}, journal = {Medical Image Analysis}, volume = {29}, year = {2016}, month = {2016}, pages = {1-11}, chapter = {1}, author = {Lucilio Cordero-Grande and J Royuela-del-Val and Santiago Sanz-Est{\'e}banez and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @conference {603, title = {Multiresolution Reconstruction of Real-Time MRI with Motion Compensated Compressed Sensing: Application to 2D Free-Breathing Cardiac MRI}, booktitle = {International Symposium on Biomedical Engineering: From Nano to Macro}, year = {2016}, month = {2016}, publisher = {IEEE Signal Processing Society}, organization = {IEEE Signal Processing Society}, address = {Prague, Check Republic}, abstract = {

Real-time MRI is a novel noninvasive imaging technique that allows the visualization of physiological processes with both good spatial and temporal resolutions. However, the reconstruction of images from highly undersampled data, needed to perform real-time imaging, remains challenging. Recently, the combination of Compressed Sensing theory with motion compensation techniques has shown to achieve better results than previous methods. In this work we describe a real-time MRI algorithm based on the acquisition of the k-space data following a Golden Radial trajectory, Compressed Sensing reconstruction and a groupwise temporal registration algorithm for the estimation and compensation of the motion in the image, all this embedded within a temporal multiresolution scheme. We have applied the proposed method to the reconstruction of free-breathing acquisition of short axis views of the heart, achieving a temporal resolution of 25ms, corresponding to an acceleration factor of 28 with respect to fully sampled Cartesian acquisitions.

}, keywords = {Compressive sensing \& sampling, Image reconstruction {\textendash} analytical \& iterative methods, Magnetic resonance imaging (MRI)}, author = {J Royuela-del-Val and Muhammad Usman and Lucilio Cordero-Grande and M. Mart{\'\i}n-Fern{\'a}ndez and Federico Simmross-Wattenberg and Claudia Prieto and Carlos Alberola-Lopez} } @conference {690, title = {Whole-heart single breath-hold cardiac cine: A robust motion-compensated compressed sensing reconstruction method}, booktitle = {International Workshop on Reconstruction and Analysis of Moving Body Organs (RAMBO/MICCAI) }, year = {2016}, month = {2016}, address = {Athens, Greece}, author = {J Royuela-del-Val and Muhammad Usman and Lucilio Cordero-Grande and Marcos Mart{\'\i}n-Fern{\'a}ndez and Federico Simmross-Wattenberg and Claudia Prieto and Carlos Alberola-Lopez} } @conference {588, title = {Multiresolution Reconstruction of Real-Time MRI with Motion Compensated Compressed Sensing: Application to 2D Free-Breathing Cardiac MRI}, booktitle = {XXXIII Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica (CASEIB)}, year = {2015}, month = {11/2015}, address = {Madrid}, author = {J Royuela-del-Val and Muhammad Usman and Lucilio Cordero-Grande and Marcos Martin-Fernandez and Federico Simmross-Wattenberg and Claudia Prieto and Carlos Alberola-Lopez} } @article {533, title = {Non-Rigid Groupwise Registration for Motion Estimation and Compensation in Compressed Sensing Reconstruction of Breath-Hold Cardiac Cine MRI}, journal = {Magnetic Resonance in Medicine}, year = {2015}, doi = {10.1002/mrm.25733}, author = {J Royuela-del-Val and Lucilio Cordero-Grande and Federico Simmross-Wattenberg and Marcos Mart{\'\i}n-Fern{\'a}ndez and Carlos Alberola-Lopez} } @conference {520, title = {Single Breath Hold Whole Heart Cine MRI With Iterative Groupwise Cardiac Motion Compensation and Sparse Regularization (kt-WiSE)}, booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine 23}, year = {2015}, author = {J Royuela-del-Val and Muhammad Usman and Lucilio Cordero-Grande and Federico Simmross-Wattenberg and Marcos Martin-Fern{\'a}ndez and Claudia Prieto and Carlos Alberola-Lopez} } @conference {426, title = {MOWHARP: Multi-Oriented Windowed HARP Reconstruction for Robust Strain Imaging}, booktitle = {Proceedings of the International Society for Magnetic Resonance in Medicine 22}, year = {2014}, author = {Lucilio Cordero-Grande and J Royuela-del-Val and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @conference {434, title = {Dise{\~n}o e implementaci{\'o}n de plugins en el entorno GIMIAS para procesado y visualizaci{\'o}n de se{\~n}ales electrofisiol{\'o}gicas asociadas a problemas cardiovasculares}, booktitle = {Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica (CASEIB)}, year = {2012}, publisher = {Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, organization = {Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, address = {San Sebasti{\'a}n, Espa{\~n}a}, author = {Mart{\'\i}n-Hern{\'a}ndez, Noelia and J Royuela-del-Val and Ver{\'o}nica Garc{\'\i}a-P{\'e}rez and Federico Simmross-Wattenberg and Marcos Martin-Fernandez and Carlos Alberola-Lopez} } @conference {585, title = {Intra Heartbeat Variability as a Tool for Cardiovascular Diagnosis and Monitoring}, booktitle = {XXIX Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica (CASEIB)}, volume = {29}, year = {2011}, pages = {343-346}, address = {C{\'a}ceres, Spain}, author = {Daniel Ruiz-Aguado and Marcos Martin-Fern{\'a}ndez and J Royuela-del-Val and Pablo Casaseca-de-la-Higuera and Carlos Alberola-Lopez} }