| Respiratory diseases have a significant socio-economic impact. For example, in 2008, the global mortality rate caused by malignant neoplasms, infections, and respiratory pathologies was 15%. These include pathologies characterized by a low ventilation/perfusion ratio, such as asthma, pulmonary edema, acute respiratory distress syndrome, and chronic bronchitis; and those characterized by a high ventilation/perfusion ratio such as emphysema, pulmonary embolism and chronic obstructive pulmonary disease.;Because of the wide spectrum of respiratory pathologies, the development of new methods and instruments for monitoring the respiratory system and assist clinicians in the diagnosis and treatment of these diseases remains highly relevant. An example of these new instruments is the electrical impedance tomography (EIT) system developed by the Institut de Génie Biomédical (IGB) of École Polytechnique de Montréal..;EIT is a technique that allows imaging the spatio-temporal changes in electrical properties of biological tissues involved in cardiac and respiratory activity. Acquisition of EIT data is done by means of electrodes positioned on the periphery of the thorax. These electrodes are used to apply a small sinusoidal current and to measure potential differences arising from the flow of the applied current through the thoracic tissues. A reconstruction algorithm processes the data to produce images of the electrical conductivity distribution. EIT is therefore a non-invasive technique that can be used for long term patient monitoring. EIT systems are safe, compact and involve low initial investment and operating costs. By contrast, other medical imaging techniques such as scintigraphy and tomodensitography require systems that are very expensive, bulky and involve the administration of radioactive compounds or exposure to X-rays.;The interpretation of EIT images is however difficult, due to the low spatial resolution and the superposition of effects from different physiological processes. The objective of the project described in this dissertation is to overcome these limitations by implementing functional imaging techniques that allow extracting clinically significant information.;Our work is based on functional imaging techniques described in the scientific literature that have been validated by off-line data processing. We believe that these techniques will achieve a significant impact only if they can be executed in real time, so that the information they provide can be used immediately by the clinicians to adjust the treatment of patients monitored by EIT. For this reason, we have stressed in our work the importance of optimizing the speed and efficiency of the data processing algorithms.;Three functional imaging techniques have been selected among the many variants described in the literature; they rely either on measurements of the variance, spectral analysis by Fourier transforms, or polynomial regression of the values of each pixel in a sequence of images. The techniques were implemented by developing modules based on the MEMTA programming architecture developed by the IGB. The modules were written in C++ complemented by standard libraries of mathematical and graphical functions, in order to make optimal use of the CPU and the multiple processors present in video boards. The effectiveness of this implementation may be judged by the fact that all three functional imaging techniques can be executed simultaneously using less than 60% of the CPU and memory resources of a mid-range personal computer.;A pilot study was conducted to validate the developed techniques. This was done with EIT data acquired from a healthy adult male subject mechanically ventilated using the ventilator's assisted pressure controlled mode. Test results show that the developed techniques separate ventilation- and perfusion-based EIT image components and provide images that should be easier to understand by eventual clinical users.;In conclusion, results presented in this dissertation open up optimistic prospects for an effective use of EIT in various clinical settings. For example, future EIT systems could include feedback mechanisms to automatically control ventilator settings in order to optimize alveolar recruitment. This could also be applied to improve mechanical ventilation of patients undergoing anesthesia and consequently reduce the incidence of post-operative pulmonary complications. |
