Distributed network imaging and electrical impedance tomography of minimally invasive surgery

Minimally invasive surgery has become highly dependent on imaging. For instance, the effectiveness of cryosurgery in treating cancer is dependent on knowledge of freezing extent, and relies on real-time imaging techniques for monitoring. However, medical imaging is often very expensive and therefore not available to most of the world population. Here we propose the concept of distributed network imaging (DNI) which could make medical imaging and minimally invasive surgery available to all who need these advanced medical modalities. We demonstrate the concept through electrical impedance tomography (EIT) of cryosurgery. The central idea is to develop an inexpensive measurend (data collection hardware) at a remote site and then to connect the measurend apparatus to an advanced image reconstruction server, which can serve a large number of distributed measurends at remote sites, using existing communication conduits (Ethernet, telephone, satellite, etc.). These conduits transfer the raw data from the measurend to the server and the reconstructed image from the server to the measurend. Electrical impedance tomography (EIT) is an imaging modality which utilizes tissue impedance variation to construct an image. The EIT measurend which consists of electrodes, a power supply, and means to measure voltage is inexpensive, and therefore suitable for DNI. EIT is also very well-suited to imaging cryosurgery since frozen tissue impedance is much higher than that of unfrozen tissue. In this study, we first develop numerical models to illustrate the theoretical ability of EIT to image cryosurgery. We begin with a simplified two dimensional model, and then extend the study to the more appropriate three dimensional model. Our simulated finite element phantoms and pixel-based Newton-Raphson reconstruction algorithms were able to produce easily identifiable images of frozen regions within tissue. Then, we demonstrate the feasibility of the DNI concept though a case study using EIT to image an in vitro liver cryosurgery procedure through a modem. We find that the acquired raw data packets are less than 5KB per image and the images, using compression, do not exceed 50KB per image.