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Non-invasive modelling of ultrasound-induced temperature in tissues: a b-splines neural network solution

Publication . Ferreira, R.; Ruano, M. Graça; Ruano, Antonio

Efficient hyperthermia therapy session requires knowledge of the exact amount of heating needed at a particular tissue location and how it propagates around the area. Until now, ultrasound heating treatments are being monitored by Magnetic Resonance Imaging (MRI) which, besides raising the treatment instrumental cost, requires the presence of a team of clinicians and limits the hyperthermia ultrasound treatment area due to the space restrictions of an MRI examination procedure. This paper introduces a novel non-invasive modelling approach of ultrasound-induced temperature in tissue. This comes as a cost effective alternative to MRI techniques, capable of achieving a maximum temperature resolution of 0.26 degrees C, clearly inferior to the MRI gold standard resolution of 0.5 degrees C/cm(3). Furthermore, we propose an innovative modelling methodology, where various similar models are built and are further combined through an optimization procedure, that we call neural ensemble optimization (NEO). This combination mechanism is shown to be superior to more simple schemes such as simple averages or evolutionary strategy based techniques. (C), 2016 IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All Rights reserved.

2016Journal article

Open access

Intelligent non-invasive modeling of ultrasound-induced temperature in tissue phantoms

Publication . Ferreira, R.; Ruano, M G; Ruano, Antonio

Raising temperature of human cells (hyperthermia) is an ancient tool for tumor masses reduction and extinction, actually even before the existence of a molecular understanding of cancer cells. Hyperthermia is being increasingly used for patients' rehabilitation and oncological diseases' treatment but still constitutes a major driver for researching more efficient and reliable therapeutic usage aiming at outstanding patients wellbeing and socio-economic benefits. Efficient hyperthermia practice demands knowledge about the exact amount of heating required at a particular tissue location, as well as information concerning the spatial heating distribution. Both of these processes require accurate characterization. Until now, ultrasound heating treatments are being monitored by magnetic resonance imaging (MRI), recognized as being capable of achieving a 0.5 degrees C/cm(3) temperature resolution [1], thereby imposing a gold standard in this field. However, one can notice that MRI-based techniques, besides the inconvenient instrumental cost, obliges the presence of a team of expert clinicians and limits the hyperthermia ultrasound treatment area due to the space restrictions of an MRI examination procedure. This article introduces a novel noninvasive modelling approach of ultrasound-induced temperature propagation in tissues, to be used as a cost effective alternative to MRI monitoring of ultrasound therapeutic techniques, achieving a maximum temperature resolution of 0.26 degrees C/cm(3), clearly inferior to the MRI gold standard resolution of 0.5 degrees C/cm(3). In order to derive the model, and avoiding painful invasive in-vivo sampling, a phantom was employed, whose composition respects the human tissues' reaction to ultrasound beams. In contrast with previous works of the authors, in the present paper we study the possibility of using b-spline neural networks (BSNN) as reliable noninvasive estimator of temperature propagation in phantoms [2,3]. The proposed methodology achieves better results than previous approaches, does not require the use of an Imaging Ultrasound transducer and, as the proposed models are piecewise polynomial models, they can be easily inverted and used in closed-loop control of therapeutic ultrasound instruments. (C) 2016 Elsevier Ltd. All rights reserved.

2017-03Journal article

Restricted access

Wireless sensors and IoT platform for intelligent HVAC control

Publication . Ruano, Antonio; Silva, Sergio; Duarte, Hélder; Ferreira, Pedro M.

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.

2018-03Journal article

Open access

An intelligent support system for automatic detection of cerebral vascular accidents from brain CT images

Publication . Hajimani, Elmira; Ruano, M G; Ruano, Antonio

Objective: This paper presents a Radial Basis Functions Neural Network (RBFNN) based detection system, for automatic identification of Cerebral Vascular Accidents (CVA) through analysis of Computed Tomographic (CT) images. Methods: For the design of a neural network classifier, a Multi Objective Genetic Algorithm (MOGA) framework is used to determine the architecture of the classifier, its corresponding parameters and input features by maximizing the classification precision, while ensuring generalization. This approach considers a large number of input features, comprising first and second order pixel intensity statistics, as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line. Results: Values of specificity of 98% and sensitivity of 98% were obtained, at pixel level, by an ensemble of non-dominated models generated by MOGA, in a set of 150 CT slices (1,867,602 pixels), marked by a NeuroRadiologist. This approach also compares favorably at a lesion level with three other published solutions, in terms of specificity (86% compared with 84%), degree of coincidence of marked lesions (89% compared with 77%) and classification accuracy rate (96% compared with 88%). (C) 2017 Published by Elsevier Ireland Ltd.

2017-07Journal article

Open access

An Economic Model-Based Predictive Control to Manage the Users' Thermal Comfort in a Building

Publication . Alamin, Yaser Imad; del Mar Castilla, Maria; Domingo Alvarez, Jose; Ruano, Antonio

The goal of maintaining users' thermal comfort conditions in indoor environments may require complex regulation procedures and a proper energy management. This problem is being widely analyzed, since it has a direct effect on users' productivity. This paper presents an economic model-based predictive control (MPC) whose main strength is the use of the day-ahead price (DAP) in order to predict the energy consumption associated with the heating, ventilation and air conditioning (HVAC). In this way, the control system is able to maintain a high thermal comfort level by optimizing the use of the HVAC system and to reduce, at the same time, the energy consumption associated with it, as much as possible. Later, the performance of the proposed control system is tested through simulations with a non-linear model of a bioclimatic building room. Several simulation scenarios are considered as a test-bed. From the obtained results, it is possible to conclude that the control system has a good behavior in several situations, i.e., it can reach the users' thermal comfort for the analyzed situations, whereas the HVAC use is adjusted through the DAP; therefore, the energy savings associated with the HVAC is increased.

2017-03Journal article

Open access