Hybrid simulated annealing image reconstruction for transmission tomography

Abstract

For parallel-ray transmission tomographic image reconstruction, a hybrid simulated annealing (HSA)-based statistical algorithm has been developed which utilizes the preprocessing template system. The proposed methodology has been applied for the reconstruction of 8 × 8, 16 × 16, 32 × 32, 64 × 64 and 128 × 128 head and lung phantoms to enhance the resolution. The effect of generic parameters including the initial temperature, final temperature, annealing profile has been analysed. The sensitivity of reconstruction quality has also been evaluated for problem specific parameters including the number of projections and size of reconstruction. Image quality is measured using root-mean-squared error, Euclidean error and peak signal-to-noise ratio (PSNR). The results of computer simulations have been compared to the standard deterministic techniques including filtered back-projection (FBP) and algebraic reconstruction technique. Hybrid simulated annealing has been found to be superior yielding the minimum error and the maximum SNR value, as compared to the Matlab® functions, ‘radon’ and ‘iradon’, with PSNR for HSA and FBP as 44.92, 33.21 and 13.61, 14.59 dB for the 8 × 8 head and lung phantoms. The parametric optimization of transmission tomography-based image reconstruction provides a scope for achieving greater dose reduction even for much larger images with parallel platforms.