An MRI image reconstruction approach based on the minimization of a complex norm

In this paper, a technique to reconstruct MRI images is proposed and presented. It is based on the decomposition of the collected raw data and the model into real and imaginary components. Then, a corresponding cost (error) function is formed to estimate the image intensities by an optimization procedure based on the conjugate gradient approach. The proposed approach is evaluated and compared with other conventional approaches such as SVD, conjugate synthesis, zero-filling and projection onto convex set (POCS) approaches. The quantitative evaluation is based on three different criteria: the root mean square, the performance test and the peak signal to noise ratio. The results show that the performance of the proposed approach offers a certain improvement over the other conventional techniques.