Background:
In magnetic resonance electrical impedance tomography (MREIT), we reconstruct conductivityimages using magnetic flux density data induced by externally injected currents. Since we extractmagnetic flux density data from acquired MR phase images, the amount of measurement noiseincreases in regions of weak MR signals. Especially for local regions of MR signal void, there mayoccur excessive amounts of noise to deteriorate the quality of reconstructed conductivity images. Inthis paper, we propose a new conductivity image enhancement method as a postprocessing techniqueto improve the image quality.
Methods:
Within a magnetic flux density image, the amount of noise varies depending on the position-dependentMR signal intensity. Using theMR magnitude image which is always available inMREIT, we estimatenoise levels of measured magnetic flux density data in local regions. Based on the noise estimates,we adjust the window size and weights of a spatial averaging filter, which is applied to reconstructedconductivity images. Without relying on a partial differential equation, the new method is fast andcan be easily implemented.
Results:
Applying the novel conductivity image enhancement method to experimental data, we could improvethe image quality to better distinguish local regions with different conductivity contrasts. Fromphantom experiments, the estimated conductivity values had 80% less variations inside regions ofhomogeneous objects. Reconstructed conductivity images from upper and lower abdominal regionsof animals showed much less artifacts in local regions of weak MR signals.
Conclusion:
We developed the fast and simple method to enhance the conductivity image quality by adaptivelyadjusting the weights and window size of the spatial averaging filter using MR magnitude images.Since the new method is implemented as a postprocessing step, we suggest adopting it without orwith other preprocessing methods for application studies where conductivity contrast is of primaryconcern.
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