HASAN: Highly accurate sensitivity for auto-contrast-corrected pMRI reconstruction

Abstract

A novel method for highly accurate coil sensitivity-map estimation, based on a constrained image-domain multi-channel LMS (c-iMCLMS) algorithm, is proposed for image reconstruction using self-calibrating SENSE. The sensitivity information is extracted by developing an image-domain cross-relation equation using the low-resolution images constructed from the fully sampled central region of the variable density MR data. Then this formulation is solved in an iterative way using a novel sum-of-squares (SOS) constraint. The improvement of the convergence speed of the c-iMCLMS algorithm is accomplished by SOS normalization of the low resolution image data and using a variable step-size in the update equation. The salient feature of the proposed technique is that it does not require any prior selection of the basis function and/or simultaneous estimation of the object image and the coil sensitivity-map. Only the low resolution images are re-filtered for the compensation of the data truncation effect to improve the consistency of the estimated coil maps. Besides, the application of the novel SOS-constraint, estimated using the pixel position-wise variance of the coil maps, gives closest to the true sensitivity-map. As a result, true object image with auto-corrected contrast is reconstructed without adopting any traditional post-contrast correction techniques. For minimization of the process noise, regularized conjugate gradient (CG) based SENSE reconstruction algorithm is used for image reconstruction using the estimated coil sensitivity-map. The proposed technique is tested on various simulation, synthetic and in-vivo datasets and significant signal-to-artifact-noise-ratio (SANR) improvement closest to the theoretical limit set by coil geometric factor is obtained as compared to some noted techniques in the literature both visually and numerically.