A software based framework for estimating patient displacement in magnetic induction tomography

Magnetic induction tomography (MIT) is a contactless, inexpensive and non-invasive technique for imaging the conductivity distribution inside volume conductors. Time-difference imaging can be used for the monitoring of patients in critical care. This includes monitoring of cerebral strokes and breathing, as well as continuous screening of edema. However, MIT signals are much more sensitive to body movements than to the conductivity changes inside of the body. This is because small movements during data acquisition can spoil the signals of interest and cause significant image artifacts. Thus, it is crucial to accurately estimate and factor body movements into image reconstruction. Methods: We proposed quantitative methods for identifying and estimating object movements from simulated MIT data prior to the image reconstruction step. A simulation was performed based on a 16 channel MIT system where a finite-difference based MIT software package was used to generate reference data from a homogenous tank without a target, and subsequent measurement of moved phantom with a target placed close to edge of the tank. The movement was estimated using frequency domain analysis (FFT). Results: Results show that movements of 1% of the radius of the tank cause image blurring but the artifacts can be minimized by appropriate regularization. Higher amounts of movements totally distorted the images which require artifact compensation or acquisition of new measurements. The percentage errors for FFT based movement estimation were 23% (1 mm), 0.3% (6.7 mm) and 6% (14.4 mm) for a displacement of 1% (1.3 mm), 5% (6.7 mm) and 10% (13.5 mm), respectively, where the displacements were chosen relative to the radius of the tank. It was found that the accuracy of movement estimation is related to the size of the background in real measurements.