Vector-entropy optimization-based neural-network approach to image reconstruction from projections.

In this paper we propose a multiobjective decision making based neural-network model and algorithm for image reconstruction from projections. This model combines the Hopfield's model and multiobjective decision making approach. We develop a weighted sum optimization based neural-network algorithm. The dynamical process of the net is based on minimization of a weighted sum energy function and Euler's iteration, and apply this algorithm to image reconstruction from computer-generated noisy projections and Siemens Somatson DR scanner data, respectively. Reconstructions based on this method is shown to be superior to conventional iterative reconstruction algorithms such as the multiplicate algebraic reconstruction technique (MART) and convolution from the point of view of accuracy of reconstruction. Computer simulation using the multiobjective method shows a significant improvement in image quality and convergence behavior over the conventional algorithms.