Sensitivity Maps Estimation Using Eigenvalues in Sense Reconstruction

Abstract

Magnetic resonance imaging (MRI) is a non-ionizing and non-invasive imaging modality. One major limitation of MRI is its long data acquisition time.
Parallel magnetic resonance imaging (PMRl) has the potential to decrease the MRI scan time by acquiring fewer k-space lines while using numerous independent receiver coils for data acquisition. SENSE reconstruction is one of the PMRI algorithms most widely used in commercial MRI scanners these days. SENSE needs accurate estimates of the receiver coil sensitivity profiles to reconstruct fully sampled images from the acquired undersampled data. This paper presents a comparison between two methods of estimating receiver coil sensitivities: (1) eigenvalue approach, in which a series of eigenvalue decompositions at the center of the acquired k-space are performed; (2) pre-scan method which uses a low-resolution image to estimate receiver coil sensitivities. In this paper, SENSE reconstruction is performed with receiver coil sensitivities estimated using both the methods. The quality of the reconstructed image is evaluated using artifact power, mean signal-to-noise ratio and line profile. The results show that the eigenvalue method to estimate sensitivity maps can be used as an alternate method for receiver coil sensitivity estimation, as it provides good reconstruction results without any compromise on the artifact power, mean signal-to-noise ratio and the line profile of the reconstructed image. Moreover, it does not require a pre-scan image to estimate receiver coil sensitivities which is required in the pre-scan method.