Parameter Analysis on Sensitivity Encoding (SENSE) Algorithm for Parallel Imaging of Magnetic Resonance Imaging

Magnetic Resonance Imaging (MRI) is one of the most prominent medical imaging technologies for examining human bones and soft tissues. However, it has a shortcoming of long scan time. To overcome this problem, parallel imaging is used to reduce scan time by using multiple coil receivers and under-sampled k-space data which lead to aliasing and artifact images. Sensitivity Encoding (SENSE) is one of the parallel imaging algorithms that work in the image domain to unfolding the aliased images. However, the use of SENSE algorithm is limited by its parameter. This study aims to investigate how parameters in the SENSE algorithm influence MRI image reconstruction via simulation. During the simulation, we used a variation of number of coils (n
c
) and acceleration factor (R). In order to analyze the data, we use structural similarity index measure (SSIM) and mean squared error (MSE) as the image quality assessment (IQA) methods. According to the IQA parameter, the results showed that number of coils and acceleration factor are correlated. The best IQA result in SENSE reconstructed image comes from the variation of n
c
= 16 and R = 2, which has the lowest MSE value (0.01405) and the highest SSIM value (0.8384). A higher acceleration factor number causes more aliasing and noise while more number of coils can make the reconstruction image better. Nevertheless, number of coils with the right number of acceleration factor will result in a good reconstructed image. This study showed that SENSE is capable of retrieving good reconstruction images by using the right parameters.