A data-parallelism approach for PSO-ANN based medical image reconstruction on a multi-core system

This paper presents the sequential and parallel data decomposition strategies implemented on a Particle Swarm Optimization (PSO) algorithm based Artificial Neural Network (PSO-ANN) weights optimization for image reconstruction. The application system is developed for the reconstruction of two-dimensional spatial standard Computed Tomography (CT) phantom images. It is running on a multi-core computer by varying the number of cores. The feed forward ANN initializes the weight between the 'ideal' images that are reconstructed using filtered back projection (FBP) technique and the corresponding projection data of CT phantom. In an earlier work, ANN training time is too long. Hence, we propose that the ANN exemplar datasets are decomposed into subsets. Using these subsets, artificial sub neural nets (subnets) are initialized and each subnet initial weights are optimized using PSO. Consequently, it was observed that the sequential approach of the proposed method consumes more training time. Hence the parallel strategy is attempted to reduce the computational training time. The parallel approach is further explored for image reconstruction from 'noisy' and 'limited-angle' datasets also.