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

α-stable distributions are a family of well-known probability distributions. However, the lack of closed analytical expressions hinders their application. Currently, several tools have been developed to numerically evaluate their density and distribution functions or to estimate their parameters, but available solutions either do not reach sufficient precision on their evaluations or are excessively slow for 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 α-stable parameter space. In this paper we present an R package and a C/C++ library with a MATLAB front-end that permit parallelized, fast and high precision evaluation of density, distribution and quantile functions, as well as random variable generation and parameter estimation of α-stable distributions in their whole parameter space. The described library can be easily integrated into third party developments

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

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

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

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-

Interlaboratory evaluations of the performance of elemental analytical methods for the forensic analysis and comparisons of electrical tapes

Adhesive tapes are an important type of evidence related to violent crimes such as the construction of improvised explosive devices and kidnappings as well as involved in other types of common evidence such as drug packaging. The methods of comparison of tape evidence consist of physical and microscopic examination followed by chemical analysis of the organic and inorganic components inherent to the tapes as part of their formulations. This work evaluates the performance of the conventional methods used in forensic analysis of adhesive tapes (physical and microscopic examination, FTIR, Py-GC-MS, and SEM-EDS) and the more recently developed elemental methods, LIBS and LA-ICP-MS, for the comparison of tape samples in different laboratories. Two interlaboratory exercises were designed to study the performance of the different analytical methods for the forensic analysis of electrical tapes. The exercises were developed with the objective to imitate forensic case scenarios where known and question tapes are compared following the laboratory’s analytical protocol. The participants were asked to compare the tape samples as in a regular forensic case. Seven (7) laboratories participated in the two interlaboratory exercises. All the laboratories performing SEM-EDS in both interlaboratory exercises (#1 and #2) were able to correctly associate the pairs of tapes originating from the same rolls, therefore the rate of false negatives was zero. Two of the laboratories performing SEM-EDS for the first interlaboratory exercise incorrectly associated two pairs of tapes belonging to different rolls, resulting in a 17% false positive rate. One of the laboratories performing SEM-EDS for interlaboratory exercise #2 incorrectly associated two pairs of tapes belonging to different rolls, resulting in a 13% false positive rate. Up to 7 and 8 elements were detected by SEM-EDS for interlaboratory exercise #1 and #2, respectively. The increased sensitivity and selectivity of LIBS and LA-ICP-MS methods allowed to distinguish all the pairs of tapes originating from different sources and for correctly associate the tapes originating from the same rolls, resulting in no false positives or false negatives. In addition, increased characterization of the samples was obtained by detecting up to 14 elements by LIBS and 27 elements by LA-ICP-MS for interlaboratory exercise #1, and 17 elements by LIBS and 32 elements by LA-ICP-MS for interlaboratory exercise #2. A match criterion of ±5 s allowed to numerically compare LIBS ratios and LA-ICP-MS signal areas for a more objective assessment of the differences between the tape samples

Group-Slicer: A collaborative extension of 3D-Slicer

AbstractIn this paper, we describe a first step towards a collaborative extension of the well-known 3D-Slicer; this platform is nowadays used as a standalone tool for both surgical planning and medical intervention. We show how this tool can be easily modified to make it collaborative so that it may constitute an integrated environment for expertise exchange as well as a useful tool for academic purposes