Inverse computational feedback optimization imaging applied to time varying changes in a homogeneous structure.
The technique of inverse computational feedback optimization imaging allows for the imaging of varying tissue without the continuous need of a complex imaging systems such as an MRI or CT. Our method trades complex imaging equipment for computing power. The objective is to use a baseline scan from an imaging system along with finite element method computational software to calculate the physically measurable parameters (such as voltage or temperature). As the physically measurable parameters change the computational model is iteratively run until it matches the measured values. Optimization routines are implemented to accelerate the process of finding the new values. Presented is a computational model demonstrating how the inverse imaging technique would work with a simple homogeneous sample with a circular structure. It demonstrates the ability to locate an object with only a few point measurements. The presented computational model uses swarm optimization techniques to help find the object location from the measured data (which in this case is voltage).
Evans
Daniel J
DJ
Computer Science, University of Idaho, Moscow, ID 83844, USA. photonthunder@gmail.com
Manwaring
Mark L
ML
Soule
Terence
T
D000465:Algorithms; D003198:Computer Simulation; D003201:Computers; D004868:Equipment Failure; D005246:Feedback; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000295:instrumentation*; D009369:Neoplasms / Q000628:therapy; D016571:Neural Networks, Computer; D012307:Risk Factors; D012984:Software; D013696:Temperature; D013997:Time Factors; D014057:Tomography, X-Ray Computed / Q000295:instrumentation*