Eigen-vector approach for coil sensitivity maps estimation

US10914798B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-10914798-B2
Application numberUS-201314038958-A
CountryUS
Kind codeB2
Filing dateSep 27, 2013
Priority dateMar 30, 2012
Publication dateFeb 9, 2021
Grant dateFeb 9, 2021

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Abstract

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A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing a matrix A of sliding blocks of a 3D image of coil calibration data, calculating a left singular matrix V ∥ from a singular value decomposition of A corresponding to τ leading singular values, calculating P=V ∥ V ∥ H , calculating a matrix that is an inverse Fourier transform of a zero-padded matrix P, and solving M H c r =(S r ) H c r for c r , where c r is a vector of coil sensitivity maps for all coils at spatial location r, and M = ( ( 1 1 … 1 0 0 … 0 … … … 0 0 … 0 ) ⁢ ( 0 0 … 0 1 1 … 1 … … … 0 0 … 0 ) ⁢ ⁢ … ⁢ ⁢ ( 0 0 … 0 0 0 … 0 … … … 1 1 … 1 ) ) .

First claim

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What is claimed is: 1. A method for reconstructing a magnetic resonance (MR) image m, comprising the steps of: acquiring coil calibration data from a magnetic resonance imaging apparatus; constructing a matrix A={a ij } of real numbers from 3D sliding blocks of a 3D c x ×c y ×c z image of said coil calibration data, wherein c x , c y , and c z are the x, y and z dimensions, respectively, of the coil calibration data, A has [(c x −k x +1)×(c y −k y +1)×(c z −k y +1)] columns and k x k y k z n c rows, wherein k x , k y , k z are the x, y, and z dimensions, respectively, of the sliding blocks, n c is a number of coils, i and j are row and column indices of elements a of matrix A, and a i,j is a k x k y k z ×1 column vector that represents a jth sliding block of an ith coil; calculating a left singular matrix V ∥ from a singular value decomposition of A, wherein V  ⁡ [ v 1 , v 2 , … ⁢ , v τ ] = ( v 1 , 1 v 1 , 2 … v 1 , τ v 2 , 1 v 2 , 2 … v 2 , τ … … … v n c , 1 v n c , 2 … v n c , τ ) comprises left singular vectors of A corresponding to τ leading singular values wherein H denotes a complex-conjugate transpose, v i,k is a k x k y k z ×1 column vector obtained by concatenating columns that is an i-th block in vector v k wherein v k k=1, 2, . . . , τ, is a component of V ∥ ; calculating P = V  ⁢ V  H = ( ( p 1 , 1 , 1 p 1 , 1 , 2 … p 1 , 1 , k x ⁢ k y ⁢ k z p

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Classifications

  • G01R33/246Primary

    Spatial mapping of the RF magnetic field B1 · CPC title

  • Parallel magnetic resonance imaging, e.g. sensitivity encoding [SENSE], simultaneous acquisition of spatial harmonics [SMASH], unaliasing by Fourier encoding of the overlaps using the temporal dimension [UNFOLD], k-t-broad-use linear acquisition speed-up technique [k-t-BLAST], k-t-SENSE (structural details of arrays of sub-coils G01R33/3415) · CPC title

  • G01R33/243Primary

    Spatial mapping of the polarizing magnetic field · CPC title

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What does patent US10914798B2 cover?
A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing a matrix A of sliding blocks of a 3D image of coil calibration data, calculating a left singular matrix V ∥ from a singular value decomposition of A corresponding to τ leading singular values, calculating P=V ∥ V ∥ H , calculating a matrix that is an inverse Fourier transform of a zero-padde…
Who is the assignee on this patent?
Siemens Healthcare Gmbh
What technology area does this patent fall under?
Primary CPC classification G01R33/246. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Feb 09 2021 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).