A networked tomographic data acquisition device architectured with FPGA is developed to provide remotely-reconfigurable waveform functions for emission of excitation signals. The purpose of the device is to explore online data acquisition techniques for medical applications. Dedicated electrodes for excitation and sensing were selected for the electrical perturbation of the media under study. This device precisely controls input and acquire output in synchrony. The envisaged use is particularly the exploitation of signal modulation to achieve selective response levels in live tissue. In this work we study the acquired data, simulate the measuring scheme selected for electrical perturbation using the finite element method, and estimate the impedance image of the media.
In order to benchmark on-line algorithms for electrical tomography we have designed a dynamic soft robotic phantom system. The robotic phantom will be synchronized with real-time measurements of a patient and it will support on-line tomographic algorithms during dynamic conditions. The system would allow to embody the kinematics in the tomographic inversion, for instance when using model predictive control to trigger the data acquisition at the beginning and at the end of the breathing process.
The principal objective of this research is to conceive a mobile system based on electrical tomography for subsurface imaging and monitoring in order to enable simultaneous recording of electrical potentials of cardiac and pulmonary activity. For an exploration of excitation waveforms in electrical tomography, specialized hardware is required. As the main principle of tomography is the measurement of electrical perturbations on an unknown object, it is crucial to synchronize excitation and sensing processes in a very precise way for the purpose of acquiring meaningful data. To cope with this problem, an FPGA device is used, with an architecture that allows us to trigger excitation signals and to read sensed data simultaneously via independent processes that share the same clock. In this way, waveform reconfiguration on frequency and shape can be provided and studied. The system is connected to a standard microcontroller SoC with a simple API that allows for IoT capabilities for on-line operation and tracking, given that the design is targeted for in vivo medical monitoring. As a result of the research work, a measuring device was developed, the surface data analyzed and the image was reconstructed using the selected configuration.
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