Application of optimally-shaped phononic crystals to reduce anchor losses of MEMS resonators

This work is focused on the application of Phononic Crystals to reduce anchor losses of MEMS contour mode resonators. Anchor losses dominates the losses in these type of released resonators at low frequency and at low temperature. The use of phononic crystals, intended as finite-periodic distribution of holes in the anchor, is fully compatible with fabrication processes and moreover it is easy to implement. The numerical results obtained in this work show how the use of these crystals can significantly reduce the anchor losses: without the use of the crystal the Q-factor related to only anchor losses is 344, with the use of the crystal it can reach up to 105900

Bayesian calibration and sensitivity analysis of heat transfer models for fire insulation panels

International audienc

3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata

Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scale 3D analysis is essential. In this work, we propose a novel deep neural network using both CMR images and patient metadata to directly predict cardiac shape parameters. The proposed method uses the promising ability of statistical shape models to simplify shape complexity and variability together with the advantages of convolutional neural networks for the extraction of solid visual features. To the best of our knowledge, this is the first work that uses such an approach for 3D cardiac shape prediction. We validated our proposed CMR analytics method against a reference cohort containing 500 3D shapes of the cardiac ventricles. Our results show broadly significant agreement with the reference shapes in terms of the estimated volume of the cardiac ventricles, myocardial mass, 3D Dice, and mean and Hausdorff distance

Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose control

Modern cardiac x-ray imaging systems regulate their radiation output based on the thickness of the patient to maintain an acceptable signal at the input of the x-ray detector. This approach does not account for the context of the examination or the content of the image displayed. We have developed a machine vision algorithm that detects iodine-filled blood vessels and fits an idealized vessel model with the key parameters of contrast, diameter, and linear attenuation coefficient. The spatio-temporal distribution of the linear attenuation coefficient samples, when appropriately arranged, can be described by a simple linear relationship, despite the complexity of scene information. The algorithm was tested on static anthropomorphic chest phantom images under different radiographic factors and 60 dynamic clinical image sequences. It was found to be robust and sensitive to changes in vessel contrast resulting from variations in system parameters. The machine vision algorithm has the potential of extracting real-time context sensitive information that may be used for augmenting existing dose control strategies

Multiresolution eXtended Free-Form Deformations (XFFD) for non-rigid registration with discontinuous transforms

Image registration is an essential technique to obtain point correspondences between anatomical structures from different images. Conventional non-rigid registration methods assume a continuous and smooth deformation field throughout the image. However, the deformation field at the interface of different organs is not necessarily continuous, since the organs may slide over or separate from each other. Therefore, imposing continuity and smoothness ubiquitously would lead to artifacts and increased errors near the discontinuity interface. In computational mechanics, the eXtended Finite Element Method (XFEM) was introduced to handle discontinuities without using computational meshes that conform to the discontinuity geometry. Instead, the interpolation bases themselves were enriched with discontinuous functional terms. Borrowing this concept, we propose a multiresolution eXtented Free-Form Deformation (XFFD) framework that seamlessly integrates within and extends the standard Free-Form Deformation (FFD) approach. Discontinuities are incorporated by enriching the B-spline basis functions coupled with extra degrees of freedom, which are only introduced near the discontinuity interface. In contrast with most previous methods, restricted to sliding motion, no ad hoc penalties or constraints are introduced to reduce gaps and overlaps. This allows XFFD to describe more general discontinuous motions. In addition, we integrate XFFD into a rigorously formulated multiresolution framework by introducing an exact parameter upsampling method. The proposed method has been evaluated in two publicly available datasets: 4D pulmonary CT images from the DIR-Lab dataset and 4D CT liver datasets. The XFFD achieved a Target Registration Error (TRE) of 1.17 ± 0.85 mm in the DIR-lab dataset and 1.94 ± 1.01 mm in the liver dataset, which significantly improves on the performance of the state-of-the-art methods handling discontinuities

MULTI-X, a State-of-the-Art Cloud-Based Ecosystem for Biomedical Research

With the exponential growth of clinical data, and the fast development of AI technologies, researchers are facing unprecedented challenges in managing data storage, scalable processing, and analysis capabilities for heterogeneous multisourced datasets. Beyond the complexity of executing data-intensive workflows over large-scale distributed data, the reproducibility of computed results is of paramount importance to validate scientific discoveries. In this paper, we present MULTIX, a cross-domain research-oriented platform, designed for collaborative and reproducible science. This cloud-based framework simplifies the logistical challenges of implementing data analytics and AI solutions by providing pre-configured environments with ad-hoc scalable computing resources and secure distributed storage, to efficiently build, test, share and reproduce scientific pipelines. An exemplary use-case in the area of cardiac image analysis will be presented together with the practical application of the platform for the analysis of ~20.000 subjects of the UK-Biobank database

Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images

Purpose: The primary goal of this article is to achieve an automatic and objective method to compute the Pfirrmann’s degeneration grade of intervertebral discs (IVD) from MRI. This grading system is used in the diagnosis and management of patients with low back pain (LBP). In addition, biomechanical models, which are employed to assess the treatment on patients with LBP, require this grading value to compute proper material properties. Materials and methods: T2-weighted MR images of 48 patients were employed in this work. The 240 lumbar IVDs were divided into a training set (140) and a testing set (100). Three experts manually classified the whole set of IVDs using the Pfirrmann’s grading system and the ground truth was selected as the most voted value among them. The developed method employs active contour models to delineate the boundaries of the IVD. Subsequently, the classification is achieved using a trained Neural Network (NN) with eight designed features that contain shape and intensity information of the IVDs. Results: The classification method was evaluated using the testing set, resulting in a mean specificity (95.5 %) and sensitivity (87.3 %) comparable to those of every expert with respect to the ground truth. Conclusions: Our results show that the automatic method and humans perform equally well in terms of the classification accuracy. However, human annotations have inherent inter- and intra-observer variabilities, which lead to inconsistent assessments. In contrast, the proposed automatic method is objective, being only dependent on the input MRI

Nitrogen and phosphorous exportation in one Araucaria angustifolia (Bert) O. Ktze. harvesting chronosequence

Se estimó el contenido de nitrógeno y fósforo de tres plantaciones experimentales de Araucaria angustifolia (Bert) O. Ktze de 20, 30 y 40 años, en el norte de la provincia de Misiones, Argentina. La biomasa total y el contenido de nutrientes acumulados en el estrato arbóreo se relacionaron positivamente con el incremento de la edad. El N y P acumulado en 20, 30 y 40 años, fueron 638, 580 y 822 Kg.ha-1 y 47, 44 y 60 Kg.ha-1 respectivamente. La cosecha a diferentes edades no incrementó significativamente la exportación relativa de nutrientes, pero el aprovechamiento de los residuos podría alterar las características químicas y físicas del suelo en el largo plazo.Nitrogen and phosphorous content were estimated for three 20, 30 and 40 years-old Araucaria angustifolia (Bert) O. Ktze experimental plantations in northern Misiones province, Argentina. The total biomass and nutrient content accumulation in the stand was positively related to stand age. The amounts of N and P stored in the 20, 30 and 40 years old stands were 638, 580 y 822 Kg.ha-1 and 47, 44 and 60 Kg.ha-1 respectively. Harvest at different ages did not significantly increase the relative nutrient exportation, but the harvesting residue management could change many the soil chemical and physical properties in the long term.Laboratorio de Investigación de Sistemas Ecológicos y Ambientale