Proposta de Solução para a Mensuração de Peso por Superfície de Contato com Objetivo de Prevenir Lesões por Pressão em Pacientes Acamados

Pressure injuries (LPP) are one of the biggest adverse events foundin health services and consist of damage to the body tissues ofbedridden patients, resulting from prolonged pressure on the skin.This situation impacts on the quality of life of people who developthe condition, causing physical and emotional damage to the bedridden,in addition to increasing the time and costs of hospitalization.Based on this problem, software was developed that shows thepoints of greatest pressure between the body of a bedridden patientand the bed in which he is. This software receives information fromhardware, under development, built specifically for this project.The points of greatest pressure are made available on the screenof a monitoring application, in an organized and intuitive manner.For each person, a pressure map image is generated with the valuesread and decubitus change times are suggested through alarms. Inaddition, this image can be analyzed by a health professional whocan take steps to relieve pressure points and prevent the appearanceof LPP. As a result, in tests carried out during the research, the systembuilt showed the information successfully and the objectiveswere achieved

Ultrasonic image reconstruction using sparsity: fast iterative methods

This study contributes to the search for fast iterative methods for ultrasonic sparse image reconstruction. The goal is achieved in three steps: the validation of a discrete acquisition model, a comparative evaluation of algorithms suitable to the problem and an acceleration proposal for one of the best performing methods. The model validation strategy consists of image reconstructions from synthetic data with previously known results, and subsequent validation with real data, collected by an ultrasound research platform with a professional phantom. The reconstructions are performed by a selected set of iterative algorithms of convex optimization, which have their parameters, results and performances analyzed. This study proposes the acceleration of the ADMM (Alternating Direction Method of Multipliers), which is among the best performing methods in terms of computational cost, and which can have its initial convergence speed doubled by the proposed modification. Since the acceleration can also be used in other applications of ADMM, the proposed modification is validated in four cases of study: two in ultrasonography and two in magnetic resonance imaging.Este trabalho contribui para a busca de métodos rápidos para reconstrução esparsa em ultrassonografia. O objetivo é alcançado em três etapas: a validação de um modelo discreto de aquisição, uma avaliação comparativa de algoritmos adequados ao problema e uma proposição de aceleração para um dos métodos de melhor desempenho. A estratégia de validação do modelo consiste em reconstruções a partir de dados sintéticos de resultado conhecido e subsequente validação com dados reais, coletados por uma plataforma de pesquisa em ultrassom com um phantom de uso profissional. As reconstruções são realizadas por um conjunto selecionado de algoritmos iterativos de otimização convexa, que têm seus parâmetros, resultados e desempenhos analisados. O trabalho propõe a aceleração do método ADMM (Alternating Direction Method of Multipliers) que está entre os de melhor desempenho em termos de custo computacional, e que pode dobrar sua velocidade inicial de convergência com a modificação proposta. Como a aceleração também pode ser utilizada em outras aplicações do ADMM, a modificação proposta é validada em quatro casos de estudo, sendo dois em ultrassonografia e dois em imageamento por ressonância magnética

An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging

Ultrasonic image reconstruction using inverse problems has recently appeared as an alternative to enhance ultrasound imaging over beamforming methods. This approach depends on the accuracy of the acquisition model used to represent transducers, reflectivity, and medium physics. Iterative methods, well known in general sparse signal reconstruction, are also suited for imaging. In this paper, a discrete acquisition model is assessed by solving a linear system of equations by an ℓ 1 -regularized least-squares minimization, where the solution sparsity may be adjusted as desired. The paper surveys 11 variants of four well-known algorithms for sparse reconstruction, and assesses their optimization parameters with the goal of finding the best approach for iterative ultrasound imaging. The strategy for the model evaluation consists of using two distinct datasets. We first generate data from a synthetic phantom that mimics real targets inside a professional ultrasound phantom device. This dataset is contaminated with Gaussian noise with an estimated SNR, and all methods are assessed by their resulting images and performances. The model and methods are then assessed with real data collected by a research ultrasound platform when scanning the same phantom device, and results are compared with beamforming. A distinct real dataset is finally used to further validate the proposed modeling. Although high computational effort is required by iterative methods, results show that the discrete model may lead to images closer to ground-truth than traditional beamforming. However, computing capabilities of current platforms need to evolve before frame rates currently delivered by ultrasound equipments are achievable