Maximum-Entropy Co-Processor for Computed-Tomography

S CHANG,M PECKERAR, C MARRIAN

PROCEEDINGS OF THE IEEE 1994 CUSTOM INTEGRATED CIRCUITS CONFERENCE（1994）

US DEPT DEF

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摘要

In this paper, we present a neural net co-processor capable of performing computed tomographic image reconstruction. The circuit performs the Radon transformation using a cost function gradient descent method. The unique aspect of this co-processor is the incorporation of informational entropy as a regularizer in the optimization problem. A 10 pixel×10 pixel array was designed and fabricated in 2 μm CMOS technology. Convergence time of the array was less than 5 μs. Issues relating to scaling the array to larger sizes are discussed in this paper

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关键词

CMOS analogue integrated circuits,Radon transforms,VLSI,analogue processing circuits,computerised tomography,convergence,coprocessors,image processing equipment,image reconstruction,maximum entropy methods,neural chips,optimisation,real-time systems,10 pixel,100 pixel,2 micron,5 mus,CMOS technology,Radon transformation,computed tomographic image reconstruction,computerised tomography,convergence time,cost function gradient descent method,maximum entropy coprocessor,neural net co-processor,optimization problem,scaling