An enhanced electrical impedance imaging algorithm for hyperthermia applications.
Electrical impedance imaging is a technique which is under investigation as a noninvasive method of tracking subsurface temperature distributions and/or associated cellular response during hyperthermia. In previous work, a finite element image reconstruction algorithm for converting surface potential distributions recorded at discrete electrode positions into spatial maps of conductivity values was developed. This paper reports on a series of significant improvements in the basic image reconstruction approach. Specifically, the ability to recover both the resistive and capacitive components of tissue electrical impedance have been incorporated. In addition, the image enhancement schemes of (1) total variation minimization, (2) dual meshing, and (3) spatial low-pass filtering, have been added. Through a series of simulation studies involving both phantom-like and clinically-relevant geometries having discrete regions and continuously-varying electrical property profiles, a significantly improved ability to recover spatial images of electrical properties in the impedance imaging context is demonstrated. The results show that the new algorithm is much more tolerant of measurement noise with levels up to 1% causing relatively modest degradations in image quality (compared to 0.1% which was needed previously in order to produce high quality images). The recovered electrical properties, themselves, both resistive and capacitive, are also found to be quantitative in value with errors in the 10-20% range occurring in the majority of cases, although deviations can reach 40% or more when noise levels as high as 10% are used. Temperature estimation simulations show that maximum temperature errors are significantly reduced (to approximately 2 degrees C relative to more than 10 degrees C in previous thermal simulations) with the new algorithm; however, temperature accuracies of better than 0.5 degree C on average are still found to be difficult to achieve with electrical impedance imaging even when the enhanced image reconstruction approach is used.
Paulsen
K D
KD
Thayer School of Engineering, Dartmouth College, Hanover, N. H. 03755, USA.
Jiang
H
H
D000465:Algorithms; D000818:Animals; D017097:Electric Impedance; D005334:Fever; D006801:Humans; D007091:Image Processing, Computer-Assisted