Tissue discrimination in magnetic resonance imaging of the rator cuff

Evaluation and diagnosis of diseases of the muscles within the rotator cuff can be done using different modalities, being the Magnetic Resonance the method more widely used. There are criteria to evaluate the degree of fat infiltration and muscle atrophy, but these have low accuracy and show great variability inter and intra observer. In this paper, an analysis of the texture features of the rotator cuff muscles is performed to classify them and other tissues. A general supervised classification approach was used, combining forward-search as feature selection method with kNN as classification rule. Sections of Magnetic Resonance Images of the tissues of interest were selected by specialist doctors and they were considered as Gold Standard. Accuracies obtained were of 93% for T1-weighted images and 92% for T2-weighted images. As an immediate future work, the combination of both sequences of images will be considered, expecting to improve the results, as well as the use of other sequences of Magnetic Resonance Images. This work represents an initial point for the classification and quantification of fat infiltration and muscle atrophy degree. From this initial point, it is expected to make an accurate and objective system which will result in benefits for future research and for patients' health

Construction of computational models for the stress analysis of the bones using CT imaging: application in the gleno-humeral joint

Se presenta en este trabajo una metodología para el procesamiento de imágenes de estudios de TC para la construcción de modelos computacionales de piezas óseas. Los modelos computacionales son utilizados para el análisis de esfuerzos utilizando el Método de los Elementos Finitos. Las constantes elásticas del tejido óseo son calculadas a partir de los datos de densidad de las TC. La metodología propuesta es aplicada en la construcción de un modelo para el análisis de la articulación gleno-humeral.A methodology for the construction of computational models from CT images is presented in this work. Computational models serve for the stress analysis of the bones using the Finite Element Method. The elastic constants of the bone tissue are calculated using the density data obtained in from the CTs. The proposed methodology is demonstrated in the construction of a model for the gleno-humeral joint.Fil: Cisilino, Adrian Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: D'amico, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Buroni, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Commisso, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Sammartino, Mario. Clínica de Fracturas y Ortopedia; ArgentinaFil: Capiel, Carlos. Instituto Radiológico; Argentin

Exploraciones de la densidad mineral ósea y osteopenia en poblaciones humanas antiguas de Patagonia Austral. Intersecciones en Antropología

El análisis de la densidad mineral ósea (DMO) en restos humanos arqueológicos es una de las herramientas que generan información acerca de la salud metabólica de las poblaciones del pasado. Hasta el momento no se dispone de este tipo de investigaciones en Patagonia Austral. Considerando esto, el objetivo de este trabajo es explorar la DMO en restos humanos de individuos de poblaciones antiguas de Patagonia Austral y su posible relación con la edad y el impacto del contacto aborigen-europeo. Se estudió la DMO en el cuello de fémur (DMOc) y en el triángulo de Ward (DMOt) de 15 esqueletos humanos adultos mediante DEXA, en el Instituto Radiológico Mar del Plata. Los resultados muestran una reducción de la DMO mayor al 25% en mujeres a partir de los 30 años en relación a las mujeres adultas de menor edad, que podría estar asociada a la maternidad y a deficiencias nutricionales. Por el contrario, en los hombres no se observan reducciones de la DMO, por lo que las condiciones nutricionales habrían sido las adecuadas para los requerimientos fisiológicos. En ambos casos no se observan diferencias importantes en la DMO entre individuos de momentos previos y posteriores al contacto.Fil: Suby, Jorge Alejandro. Universidad Nacional del Centro de la Provincia de Bs.as. Facultad de Ciencias Sociales. Departamento de Arqueología. Laboratorio de Ecología Evolutiva Humana (Sede Quequén); Argentina;Fil: Costantino, Sebastián.Fil: Capiel, Carlos.Fil: Lucarini, María Marta.Fil: Etchepare, Ezequiel

Multiscale design of artificial bones with biomimetic elastic microstructures

Cancellous bone is a highly porous, heterogeneous, and anisotropic material which can be found at the epiphyses of long bones and in the vertebral bodies. The hierarchical architecture makes cancellous bone a prime example of a lightweight natural material that combines strength with toughness. Better understanding the mechanics of cancellous bone is of interest for the diagnosis of bone diseases, the evaluation of the risk of fracture, and for the design of artificial bones and bone scaffolds for tissue engineering. A multiscale optimization method to maximize the stiffness of artificial bones using biomimetic cellular microstructures described by a finite set of geometrical micro-parameters is presented here. The most outstanding characteristics of its implementation are the use of: an interior point optimization algorithm, a precalculated response surface methodology for the evaluation of the elastic tensor of the microstructure as an analytical function of the micro-parameters, and the adjoint method for the computation of the sensitivity of the macroscopic mechanical response to the variation of the micro-parameters. The performance and effectiveness of the tool are evaluated by solving a problem that consists in finding the optimal distribution of the microstructures for a proximal end of a femur subjected to physiological loads. Two strategies for the specification of the solid volume fraction constraints are assessed. The results are compared with data of a computed tomography study of an actual human bone. The model successfully predicts the main features of the spatial arrangement of the trabecular and cortical microstructures of the natural bone.Fil: Colabella, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Cisilino, Adrian Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Fachinotti, Victor Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Capiel, Carlos Alfredo. Instituto Radiologico Mar del Plata S.r.l..; ArgentinaFil: Kowalczyk, Piotr. Polish Academy of Sciences; Argentin

Interval type-2 fuzzy predicates for brain magnetic resonance image segmentation

The analysis of structural changes in the brain through Magnetic Resonance Imaging (MRI) provides useful information for diagnosis and clinical treatment of patients with pathologies like Alzheimer disease and dementia. While complexity achieved by the MRI equipment is high, quantification of structures and tissues has not been entirely solved. In the present paper, MRI segmentation is discussed using a new classification method called Type-2 Label-based Fuzzy Predicate Classification (T2-LFPC). From labeled data (pixels of different tissues selected by medical experts) a random partition is defined and the obtained subsets are analyzed discovering groups with similar properties called class prototypes. Using theses prototypes, interval type-2 membership functions and fuzzy predicates are defined. Parameters regarding the fuzzy predicates are optimized. Fuzzy predicates are applied on unlabeled pixels performing the segmentation and volumes occupied for the tissues into the intracranial cavity are computed. Results are compared to those of known methods. A method of measuring the progressive atrophy and possible changes compared to a therapeutic effect should be essentially automatic and therefore independent of the radiologist. Results show that the performance of the proposed method is highly acceptable as a contribution for this requirement. Advantages of this approach are presented throughout this paper.The analysis of structural changes in the brain through Magnetic Resonance Imaging (MRI) provides useful information for diagnosis and clinical treatment of patients with pathologies like Alzheimer disease and dementia. While complexity achieved by the MRI equipment is high, quantification of structures and tissues has not been entirely solved. In the present paper, MRI segmentation is discussed using a new classification method called Type-2 Label-based Fuzzy Predicate Classification (T2-LFPC). From labeled data (pixels of different tissues selected by medical experts) a random partition is defined and the obtained subsets are analyzed discovering groups with similar properties called class prototypes. Using theses prototypes, interval type-2 membership functions and fuzzy predicates are defined. Parameters regarding the fuzzy predicates are optimized. Fuzzy predicates are applied on unlabeled pixels performing the segmentation and volumes occupied for the tissues into the intracranial cavity are computed. Results are compared to those of known methods. A method of measuring the progressive atrophy and possible changes compared to a therapeutic effect should be essentially automatic and therefore independent of the radiologist. Results show that the performance of the proposed method is highly acceptable as a contribution for this requirement. Advantages of this approach are presented throughout this paper.Fil: Comas, Diego Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas En Electronica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas En Electronica.; ArgentinaFil: Meschino, Gustavo Javier. Universidad FASTA ; ArgentinaFil: Costantino, Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas En Electronica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas En Electronica.; ArgentinaFil: Capiel, Carlos. Instituto Radiologico Mar del Plata; Argentina. Universidad FASTA ; ArgentinaFil: Ballarin, Virginia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas En Electronica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas En Electronica.; Argentina. Universidad FASTA ; Argentin

Tissue discrimination in magnetic resonance imaging of the rotator cuff

Evaluation and diagnosis of diseases of the muscles within the rotator cuff can be done using different modalities, being the Magnetic Resonance the method more widely used. There are criteria to evaluate the degree of fat infiltration and muscle atrophy, but these have low accuracy and show great variability inter and intra observer. In this paper, an analysis of the texture features of the rotator cuff muscles is performed to classify them and other tissues. A general supervised classification approach was used, combining forward-search as feature selection method with kNN as classification rule. Sections of Magnetic Resonance Images of the tissues of interest were selected by specialist doctors and they were considered as Gold Standard. Accuracies obtained were of 93% for T1-weighted images and 92% for T2-weighted images. As an immediate future work, the combination of both sequences of images will be considered, expecting to improve the results, as well as the use of other sequences of Magnetic Resonance Images. This work represents an initial point for the classification and quantification of fat infiltration and muscle atrophy degree. From this initial point, it is expected to make an accurate and objective system which will result in benefits for future research and for patients' health.Fil: Meschino, Gustavo Javier. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Departamento de Ingeniería Eléctrica. Laboratorio de Bioingeniería; Argentina. Universidad FASTA "Santo Tomas de Aquino"; ArgentinaFil: Comas, Diego Sebastián. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Gonzalez, Mariela Azul. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Departamento de Ingeniería Eléctrica. Laboratorio de Bioingeniería; Argentina. Universidad FASTA "Santo Tomas de Aquino"; ArgentinaFil: Capiel, Carlos Alfredo. Universidad FASTA "Santo Tomas de Aquino"; ArgentinaFil: Ballarin, Virginia Laura. Universidad Nacional de Mar del Plata. Facultad de Ingeniería; Argentina. Universidad FASTA "Santo Tomas de Aquino"; Argentin