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Jaume Garcia, Francesc Carreras, Sandra Pujades, & Debora Gil. (2008). "Regional motion patterns for the Left Ventricle function assessment " In Proc. 19th Int. Conf. Pattern Recognition ICPR 2008 (pp. 1–4).
Abstract: Regional scores (e.g. strain, perfusion) of the Left Ventricle (LV) functionality are playing an increasing role in the diagnosis of cardiac diseases. A main limitation is the lack of normality models for complementary scores oriented to assessment of the LV integrity. This paper introduces an original framework based on a parametrization of the LV domain, which allows comparison across subjects of local physiological measures of different nature. We compute regional normality patterns in a feature space characterizing the LV function. We show the consistency of the model for the regional motion on healthy and hypokinetic pathological cases
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Debora Gil, Jose Maria-Carazo, & Roberto Marabini. (2006). "On the nature of 2D crystal unbending " . Journal of Structural Biology, 156(3), 546–555.
Abstract: Crystal unbending, the process that aims to recover a perfect crystal from experimental data, is one of the more important steps in electron crystallography image processing. The unbending process involves three steps: estimation of the unit cell displacements from their ideal positions, extension of the deformation field to the whole image and transformation of the image in order to recover an ideal crystal. In this work, we present a systematic analysis of the second step oriented to address two issues. First, whether the unit cells remain undistorted and only the distance between them should be changed (rigid case) or should be modified with the same deformation suffered by the whole crystal (elastic case). Second, the performance of different extension algorithms (interpolation versus approximation) is explored. Our experiments show that there is no difference between elastic and rigid cases or among the extension algorithms. This implies that the deformation fields are constant over large areas. Furthermore, our results indicate that the main source of error is the transformation of the crystal image.
Keywords: Electron microscopy
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Jaume Garcia, Joel Barajas, Francesc Carreras, Sandra Pujades, & Petia Radeva. (2005). "An intuitive validation technique to compare local versus global tagged MRI analysis " In Computers In Cardiology (Vol. 32, 29–32).
Abstract: Myocardium appears as a uniform tissue that seen in convectional Magnetic Resonance Images (MRI) shows just the contractile part of its movement. MR Tagging is a unique imaging technique that prints a grid over the tissue which moves according to the underlying movement of the myocardium revealing the true deformation of the cardiac muscle. Optical flow techniques based on spectral information estimate tissue displacement by analyzing information encoded in the phase maps which can be obtained using, local (Gabor) and global (HARP) methods. In this paper we compare both in synthetic and real Tagged MR sequences. We conclude that local method is slightly more accurate than the global one. On the other hand, global method is more efficient as it is much faster and less parameters have to be taken into account
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Debora Gil, Agnes Borras, Ruth Aris, Mariano Vazquez, Pierre Lafortune, & Guillame Houzeaux. (2012). "What a difference in biomechanics cardiac fiber makes " In Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges (Vol. 7746, pp. 253–260). Springer Berlin Heidelberg.
Abstract: Computational simulations of the heart are a powerful tool for a comprehensive understanding of cardiac function and its intrinsic relationship with its muscular architecture. Cardiac biomechanical models require a vector field representing the orientation of cardiac fibers. A wrong orientation of the fibers can lead to a
non-realistic simulation of the heart functionality. In this paper we explore the impact of the fiber information on the simulated biomechanics of cardiac muscular anatomy. We have used the John Hopkins database to perform a biomechanical simulation using both a synthetic benchmark fiber distribution and the data obtained experimentally from DTI. Results illustrate how differences in fiber orientation affect heart deformation along cardiac cycle.
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Jaume Garcia. (2009). "Statistical Models of the Architecture and Function of the Left Ventricle " (Debora Gil, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Cardiovascular Diseases, specially those affecting the Left Ventricle (LV), are the leading cause of death in developed countries with approximately a 30% of all global deaths. In order to address this public health concern, physicians focus on diagnosis and therapy planning. On one hand, early and accurate detection of Regional Wall Motion Abnormalities (RWMA) significantly contributes to a quick diagnosis and prevents the patient to reach more severe stages. On the other hand, a thouroughly knowledge of the normal gross anatomy of the LV, as well as, the distribution of its muscular fibers is crucial for designing specific interventions and therapies (such as pacemaker implanction). Statistical models obtained from the analysis of different imaging modalities allow the computation of the normal ranges of variation within a given population. Normality models are a valuable tool for the definition of objective criterions quantifying the degree of (anomalous) deviation of the LV function and anatomy for a given subject. The creation of statistical models involve addressing three main issues: extraction of data from images, definition of a common domain for comparison of data across patients and designing appropriate statistical analysis schemes. In this PhD thesis we present generic image processing tools for the creation of statistical models of the LV anatomy and function. On one hand, we use differential geometry concepts to define a computational framework (the Normalized Parametric Domain, NPD) suitable for the comparison and fusion of several clinical scores obtained over the LV. On the other hand, we present a variational approach (the Harmonic Phase Flow, HPF) for the estimation of myocardial motion that provides dense and continuous vector fields without overestimating motion at injured areas. These tools are used for the creation of statistical models. Regarding anatomy, we obtain an atlas jointly modelling, both, LV gross anatomy and fiber architecture. Regarding function, we compute normality patterns of scores characterizing the (global and local) LV function and explore, for the first time, the configuration of local scores better suited for RWMA detection.
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Jaume Garcia. (2004). "Generalized Active Shape Models Applied to Cardiac Function Analysis ". Master's thesis, , .
Abstract: Medical imaging is very useful in the assessment and treatment of many diseases. To deal with the great amount of data provided by imaging scanners and extract quantitative information that physicians can interpret, many analysis algorithms have been developed. Any process of analysis always consists of a first step of segmenting some particular structure. In medical imaging, structures are not always well defined and suffer from noise artifacts thus, ordinary segmentation methods are not well suited. The ones that seem to give better results are those based on deformable models. Nevertheless, despite their capability of mixing image features together with smoothness constraints that may compensate for image irregularities, these are naturally local methods, i. e., each node of the active contour evolve taking into account information about its neighbors and some other weak constraints about flexibility and smoothness, but not about the global shape that they should find. Due to the fact that structures to be segmented are the same for all cases but with some inter and intra-patient variation, the incorporation of a priori knowledge about shape in the segmentation method will provide robustness to it. Active Shape Models is an algorithm based on the creation of a shape model called Point Distribution Model. It performs a segmentation using only shapes similar than those previously learned from a training set that capture most of the variation presented by the structure. This algorithm works by updating shape nodes along a normal segment which often can be too restrictive. For this reason we propose a generalization of this algorithm that we call Generalized Active Shape Models and fully integrates the a priori knowledge given by the Point Distribution Model with deformable models or any other appropriate segmentation method. Two different applications to cardiac imaging of this generalized method are developed and promising results are shown.
Keywords: Cardiac Analysis; Deformable Models; Active Contour Models; Active Shape Models; Tagged MRI; HARP; Contrast Echocardiography.
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Jaume Garcia. (2002)." Propagacio de fronts per a la segmentacio en imatges IVUS" .
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Jaume Garcia, Albert Andaluz, Debora Gil, & Francesc Carreras. (2010). "Decoupled External Forces in a Predictor-Corrector Segmentation Scheme for LV Contours in Tagged MR Images " In 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 4805–4808).
Abstract: Computation of functional regional scores requires proper identification of LV contours. On one hand, manual segmentation is robust, but it is time consuming and requires high expertise. On the other hand, the tag pattern in TMR sequences is a problem for automatic segmentation of LV boundaries. We propose a segmentation method based on a predictorcorrector (Active Contours – Shape Models) scheme. Special stress is put in the definition of the AC external forces. First, we introduce a semantic description of the LV that discriminates myocardial tissue by using texture and motion descriptors. Second, in order to ensure convergence regardless of the initial contour, the external energy is decoupled according to the orientation of the edges in the image potential. We have validated the model in terms of error in segmented contours and accuracy of regional clinical scores.
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Paula Fritzsche, C.Roig, Ana Ripoll, Emilio Luque, & Aura Hernandez-Sabate. (2006). "A Performance Prediction Methodology for Data-dependent Parallel Applications " In Proceedings of the IEEE International Conference on Cluster Computing (pp. 1–8).
Abstract: The increase in the use of parallel distributed architectures in order to solve large-scale scientific problems has generated the need for performance prediction for both deterministic applications and non-deterministic applications. In particular, the performance prediction of data dependent programs is an extremely challenging problem because for a specific issue the input datasets may cause different execution times. Generally, a parallel application is characterized as a collection of tasks and their interrelations. If the application is time-critical it is not enough to work with only one value per task, and consequently knowledge of the distribution of task execution times is crucial. The development of a new prediction methodology to estimate the performance of data-dependent parallel applications is the primary target of this study. This approach makes it possible to evaluate the parallel performance of an application without the need of implementation. A real data-dependent arterial structure detection application model is used to apply the methodology proposed. The predicted times obtained using the new methodology for genuine datasets are compared with predicted times that arise from using only one execution value per task. Finally, the experimental study shows that the new methodology generates more precise predictions.
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Debora Gil, Jaume Garcia, Ruth Aris, Guillaume Houzeaux, & Manuel Vazquez. (2009). "A Riemmanian approach to cardiac fiber architecture modelling " In R. L. R. V. L. Nithiarasu (Ed.), 1st International Conference on Mathematical & Computational Biomedical Engineering (pp. 59–62). Swansea (UK).
Abstract: There is general consensus that myocardial fiber architecture should be modelled in order to fully understand the electromechanical properties of the Left Ventricle (LV). Diffusion Tensor magnetic resonance Imaging (DTI) is the reference image modality for rapid measurement of fiber orientations by means of the tensor principal eigenvectors. In this work, we present a mathematical framework for across subject comparison of the local geometry of the LV anatomy including the fiber architecture from the statistical analysis of DTI studies. We use concepts of differential geometry for defining a parametric domain suitable for statistical analysis of a low number of samples. We use Riemannian metrics to define a consistent computation of DTI principal eigenvector modes of variation. Our framework has been applied to build an atlas of the LV fiber architecture from 7 DTI normal canine hearts.
Keywords: cardiac fiber architecture; diffusion tensor magnetic resonance imaging; differential (Rie- mannian) geometry.
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