Eigen-vector approach for coil sensitivity maps estimation

Abstract

A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing (61) a matrix A of sliding blocks of a 2D image of coil calibration data, calculating (62) a left singular matrix V∥ from a singular value decomposition of A corresponding to τ leading singular values, calculating (63) P=V∥V∥H, calculating (64) a matrix S that is an inverse Fourier transform of a zero-padded matrix P, and solving (65) MHcr=(Sr)Hcr for cr, where cr is a vector of coil sensitivity maps for all coils at spatial location r, andM((11…100…0………00…0)(00…011…1………00…0)…(00…000…0………11…1)).

Description

CROSS REFERENCE TO RELATED UNITED STATES APPLICATIONS[0001]This application claims priority from “On the Eigen-Vector Approach for Coil Sensitivity Maps Estimation”, U.S. Provisional Application No. 61 / 618,002 of Liu, et al., filed Mar. 30, 2012, “Coil Sensitivity maps Estimation in Dynamic CMRI: Square Could be Better than Rectangular”, U.S. Provisional Application No. 61 / 724,023 of Liu, et al., filed Nov. 8, 2012, and “An Eigen-Vector Approach for Coil Sensitivity Estimation in the 3D Scenario”, U.S. Provisional Application No. 61 / 724,001 of Liu, et al., filed Nov. 8, 2012, the contents of all of which are herein incorporated by reference in their entireties.TECHNICAL FIELD[0002]This disclosure is directed to methods for estimating coil sensitivity maps (CSM) of magnetic resonance imaging (MRI) apparatuses.DISCUSSION OF THE RELATED ART[0003]Parallel imaging makes use of multiple receiver coils to acquire the image in parallel. It can be used to accelerate the image acquisition by ...

AI Technical Summary

Benefits of technology

The invention describes a method for estimating coil sensitivity maps for magnetic resonance images. This method reduces computational and storage costs and avoids the computation of large matrices by using equivalent representations. The method involves determining a unit eigenvector and an optimizer cr that maximizes the correlation between the unit eigenvector and another matrix. The technical effect is to improve the accuracy and quality of the reconstructed images while reducing the computational load and storage requirements.

Problems solved by technology

However, when the object is not static, the sensitivity functions are different between pre-scan and under-sampled scans, and this could lead to reconstruction errors.

To compensate for this, joint estimation approaches have been proposed, however, these approaches usually have high computation cost and are restricted to explicit reconstructions.

However, to the best of the inventor's knowledge: (1) the detailed mathematical derivations for the eigenvector approach are not well understood; (2) the optimization criterion for computing the CSM is not very clear; and (3) there lacks an efficient approach.

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