Libstable: Fast, Parallel and High-Precision Computation of -Stable Distributions in C/C++ and MATLAB

-stable distributions are a wide family of probability distributions used in many elds where probabilistic approaches are taken. However, the lack of closed analytical expressions is a major drawback for their application. Currently, several tools have been developed to numerically evaluate their density and distribution functions or estimate their parameters, but available solutions either do not reach su cient precision on their evaluations or are too slow for several practical purposes. Moreover, they do not take full advantage of the parallel processing capabilities of current multi-core machines. Other solutions work only on a subset of the -stable parameter space. In this paper we present a C/C++ library and a MATLAB front-end that allows fully parallelized, fast and high precision evaluation of density, distribution and quantile functions (PDF, CDF and CDF1 respectively), random variable generation and parameter estimation of -stable distributions in their whole parameter space. The library provided can be easily integrated on third party developments

Non-Rigid Groupwise Registration for Motion Estimation and Compensation in Compressed Sensing Reconstruc- tion of Breath-Hold Cardiac Cine MRI

Purpose: Compressed sensing methods with motion estimation and compensation techniques have been proposed for the reconstruction of accelerated dynamic MRI. However, artifacts that naturally arise in compressed sensing reconstruction procedures hinder the estimation of motion from reconstructed images, especially at high acceleration factors. This work introduces a robust groupwise non-rigid motion estimation technique applied to the compressed sensing reconstruction of dynamic cardiac cine MRI sequences. Theory and Methods: A spatio-temporal regularized, groupwise, non-rigid registration method based on a B-splines deformation model and a least squares metric is used to estimate and to compensate the movement of the heart in breath-hold cine acquisitions and to obtain a quasi-static sequence with highly sparse representation in temporally transformed domains. Results: Short axis in vivo datasets are used for validation, both original multi-coil as well as DICOM data. Fully sampled data were retrospectively undersampled with various acceleration factors and reconstructions were compared with the two well-known methods k-t FOCUSS and MASTeR. The proposed method achieves higher signal to error ratio and structure similarity index for medium to high acceleration factors. Conclusions: Reconstruction methods based on groupwise registration show higher quality recon- structions for cardiac cine images than the pairwise counterparts tested

Convolution-based free-form deformation for multimodal groupwise registration

Producción CientíficaRecently, an efficient implementation of convolution-based free form deformations (FFD) has been proposed for both groupwise 3D monomodal and 2D pairwise multimodal registrations. However, there is still an unmet need in the field for groupwise -D multimodal registration with L > 2. In this correspondence, we address this need and present a solution for achieving accurate registration using two popular metrics: Renyi entropy and PCA2.Ministerio de Economía, Industria y Competitividad (TEC2017-82408-R and PID2020-115339RB-I00)ESAOTE Ltd. (18IQBM

Efficient convolution-based pairwise elastic image registration on three multimodal similarity metrics

Producción CientíficaThis 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.Ministerio de Economía, Industria y Competitividad (grants TEC2017-82408-R and PID2020-115339RB-I00)ESAOTE Ltd (grant 18IQBM

Fast 4D elastic group-wise image registration. Convolutional interpolation revisited

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.MEC-TEC2017-82408-

A clinically viable vendor-independent and device-agnostic solution for accelerated cardiac MRI reconstruction

Producción CientíficaBackground and objective: Recent research has reported methods that reconstruct cardiac MR images acquired with acceleration factors as high as 15 in Cartesian coordinates. However, the computational cost of these techniques is quite high, taking about 40 min of CPU time in a typical current machine. This delay between acquisition and final result can completely rule out the use of MRI in clinical environments in favor of other techniques, such as CT. In spite of this, reconstruction methods reported elsewhere can be parallelized to a high degree, a fact that makes them suitable for GPU-type computing devices. This paper contributes a vendor-independent, device-agnostic implementation of such a method to reconstruct 2D motion-compensated, compressed-sensing MRI sequences in clinically viable times. Methods: By leveraging our OpenCLIPER framework, the proposed system works in any computing device (CPU, GPU, DSP, FPGA, etc.), as long as an OpenCL implementation is available, and development is significantly simplified versus a pure OpenCL implementation. In OpenCLIPER, the problem is partitioned in independent black boxes which may be connected as needed, while device initialization and maintenance is handled automatically. Parallel implementations of both a groupwise FFD-based registration method, as well as a multicoil extension of the NESTA algorithm have been carried out as processes of OpenCLIPER. Our platform also includes significant development and debugging aids. HIP code and precompiled libraries can be integrated seamlessly as well since OpenCLIPER makes data objects shareable between OpenCL and HIP. This also opens an opportunity to include CUDA source code (via HIP) in prospective developments. Results: The proposed solution can reconstruct a whole 12–14 slice CINE volume acquired in 19–32 coils and 20 phases, with an acceleration factor of ranging 4–8, in a few seconds, with results comparable to another popular platform (BART). If motion compensation is included, reconstruction time is in the order of one minute. Conclusions: We have obtained clinically-viable times in GPUs from different vendors, with delays in some platforms that do not have correspondence with its price in the market. We also contribute a parallel groupwise registration subsystem for motion estimation/compensation and a parallel multicoil NESTA subsystem for -norm problem solving.Ministerio de Economía, Industria y Competitividad (grant TEC2017-82408-R)Asociación Española Contra el Cáncer (grant PRDVL19001MOYA

Miccai 2017, workshop RAMBO

In this work we propose a novel approach for the reconstruc- tion of 3D isotropic, free-breathing cardiac cine MRI with 100% data e - ciency. The main components are a continuous 3D Golden radial k-space data acquisition, a robust groupwise cardio-respiratory motion estima- tion technique and a multiresolution strategy introduced in a previously proposed compressed sensing reconstruction scheme. Initial results on simulated data show better reconstruction quality than the non-motion compensated counterpart and reduced reconstruction times with respect a single-resolution procedure for equivalent acceleration factors ranging 24.38 to 34.8

ISMRM-2018

Metodología de reconstrucción de imagen de resonancia cardiaca a partir de una única plantilla de imagen que se deforma, según la información adquirida en el k-espacio, para dar lugar a las diferentes fases cardiacas.Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA082U16)Ministerio de Ciencia, Innovación y Universidades (TEC 2014-57428