High-Resolution Magnetocardiogram Restoration for Cardiac Electric Current Localization

• US 20120197145A1
• Filed: 01/31/2011
• Published: 08/02/2012
• Est. Priority Date: 01/31/2011
• Status: Active Grant

• Current Assignee: Columbia Peak Ventures, LLC (Dominion Harbor Enterprises, LLC)
• Original Assignee: Seiko Epson Corporation (Seiko Group)
• Inventors: Wu, Chenyu, Xiao, Jing
• Granted Patent: US 8,688,192 B2
• Time in Patent Office: Days
• US Class Current: 600/509
• CPC Class Codes: A61B 2562/046 in a matrix array
• CPC Class Codes: A61B 5/243 specially adapted for magnetocardiographic [MCG] signals

Abstract

Magnetocardiogram (MCG) provides temporal and spatial measurements of cardiac electric activities, which permits current localization. An MCG device usually consists of a small number of magnetic sensors in a planar array. Each sensor provides a highly low-resolution 2D MCG map. Such a low-res map is insufficient for cardiac electric current localization. To create a high resolution MCG image from the sparse measurements, an algorithm based on model learning is used. The model is constructed using a large number of randomly generated high resolution MCG images based on the Biot-Savart Law. By fitting the model with the sparse measurements, high resolution MCG image are created. Next, the 2D position of the electric current is localized by finding the peak in the tangential components of the high resolution MCG images. Finally, the 2D current localization is refined by a non-linear optimization algorithm, which simultaneously recovers the depth of the electric current from the sensor and its magnitude and orientation.

Claims

1. A magnetocardiogram (MCG) system comprising:

a sensor unit including M×

M electromagnetic sensors producing a sparse measurement output of M×

M data units, said sparse measurement output constituting a first MCG image;

a linear model defining a second MCG image of substantially higher resolution than said first MCG image, said second MCG image having a P×

P resolution where P>

M, said linear model establishing interpolation patterns between characteristics of the linear model and any data point of said M×

M measurement output; and

a high resolution MCG image synthesizer for producing a third MCG image by projecting said first MCG image onto the subspace of the linear model, and establishing coefficients for said third MCG image in accordance with the linear model and said M×

M data units.

2. The MCG system of claim 1, wherein said third MCG image has a P×

P resolution.

3. The MCG system of claim 1, further having an electric current localizer for determining a position and momentum of an electric current in accord with said third MCG image, said electric current localizer evaluating the electromagnetic output data from each electromagnetic sensor in an x-y orientation (Bxy) assuming single dipole, computing dense Bxy from dense Bz, finding the image maximum in said third MCG image, and using this determined position information as a starting point in an iterative process for identifying a three-dimensional position vector {right arrow over (p)} and momentum vector {right arrow over (J)} for said electric current.

4. The method of claim 3, wherein the identifying a three-dimensional position vector {right arrow over (p)} and momentum vector {right arrow over (J)} for said electric current further includes:

Given said third MCG image B_z(i,j)(i=1, 2, . . . , N;

j=1, 2, . . . , N), the maximal point of the tangential components B′

_xy(i,j) of B_z(i,j) refers to the 2D position (x_p,y_p) of the electric current, and the tangential components of B_z(i,j) is computed as

5. The MCG system of claim 1, wherein said linear model is defined as creating a plurality of synthesized magnetocardiogram images having the same resolution as said second MCG image, said synthesized magnetocardiogram images being based on simulated electrical impulses within a three-dimensional spatial heart volume, as it would be perceived in an expected magnetocardiogram system.

6. The MCG system of claim 5, wherein said plurality of synthesized magnetocardiogram images includes at least one thousand synthesized images simulating perceived electrical impulses per predefined depth level within said heart volume.

7. The MCG system of claim 5, wherein said synthesized MCG images are synthesized using the Biot-Savart Law.

8. The MCG system of claim 5, wherein said synthesized MCG images are based on randomly generated currents within said heart volume.

9. The MCG system of claim 5, wherein said linear model is created by using principal component analysis (PCA).

10. The MCG system of claim 1, wherein said interpolation patterns are established by the following steps:

(A) defining the following notation;

N×

N dense Bz magnetic field map to form a vector;

M×

M sparse measurement to form a vector;

K randomly generated single current dipoles Q;

(B) for each randomly generated current Q, compute N×

N magnetic field map using Biot-Savart equation and stack the image to a vector f₁;

(C) repeating step (B) to obtain K samples and get a data matrix A=└

f₁, f₂, . . . f_K┘

; and

(D) training a PCA model given input data A, to obtain the eigenmatrix Σ

_f.

11. The MCG system of claim 10, wherein said third MCG image is created by:

given a new dipole and M×

M sparse measurements g₁, finding the corresponding rows in the eigenmatrix, and denoting a resultant submatrix as Σ

_g;

projecting the sparse measurement to the PCA subspace and computing the coefficients as c_g=Σ

_g⁺(g_j−

g_mean), where Σ

_g⁺ is an estimation of the inverse of Σ

_g; and

using the computed coefficients and original PCA space to reconstruct the dense magnetic field map Bz, as f_j=Σ

_fc_g+f_mean.

12. The MCG system of claim 1, wherein the producing of said third MCG image includes:

defining the sparse measurement output as a vector g;

defining the linear model as Σ

;

extracting from Σ

the row corresponding to sparse measurement output to form a sub-eigenmatrix Σ

_g;

projecting g onto Σ

_g;

defining the establishment of coefficients as c_g=Σ

_g⁺(g_i−

μ

_g), where Σ

_g⁺ is the pseudo inverse of Σ

_g, μ

_g are extracted coefficients from a mean vector μ

of linear model Σ

; and

defining the high resolution MCG image vector h as h=Σ

·

c_g+μ

.

13. A method of creating a magnetocardiogram (MCG) image from a sparse measurement output provided by a sensor unit including a plurality of electromagnetic sensors, each electromagnetic sensor contributing its output data to said sparse measurement output, said method comprising;

defining high resolution to mean a resolution substantially higher than the resolution provided by said sparse measurement output;

creating a plurality of synthesized high resolution magnetocardiogram images based on simulated electrical impulses within a three-dimensional spatial heart volume, as it would be perceived in an expected magnetocardiogram system;

creating a linear model of the synthesized high resolution magnetocardiogram images to establish interpolation patterns between characteristics of the linear model and any sparse measurement output; and

creating a representative high resolution MCG image by projecting said sparse measurement output onto the subspace of the linear model, and establishing coefficients.

14. The method of claim 13, wherein said plurality of synthesized high resolution magnetocardiogram images includes more than one thousand image simulating perceived electrical impulses at different depths within said heart volume.

15. The method of claim 13, wherein said synthesized high resolution MCG images are synthesized using the Biot-Savart Law.

16. The method of claim 13, wherein said synthesized high resolution MCG images are based on randomly generated currents within said heart volume.

17. The method of claim 13, wherein said linear model is created using by principal component analysis (PCA).

18. The method of claim 13, wherein said interpolation patterns are established by the following steps:

(A) defining the following notation;

N×

N dense Bz magnetic field map to form a vector;

M×

M sparse measurement to form a vector;

K randomly generated single current dipoles Q;

(B) for each randomly generated current Q, compute N×

N magnetic field map using Biot-Savart equation and stack the image to a vector f₁;

(C) repeating step (B) to obtain K samples and get a data matrix A=└

f₁, f₂, . . . f_K┘

; and

(D) train a PCA model given input data A, to obtain the eigenmatrix Σ

_f.

19. The method of claim 18, wherein said representative high resolution MCG image is created by:

given a new dipole and M×

M sparse measurements g_j, finding the corresponding rows in the eigenmatrix, and denoting a resultant submatrix as Σ

_g;

projecting the sparse measurement to the PCA subspace and computing the coefficients as c_g=Σ

_g^+(g_j−

g_mean), where Σ

_g⁺ is an estimation of the inverse of Σ

_g; and

using the computed coefficients and original PCA space to reconstruct the dense magnetic field map Bz, as f_j=Σ

_fc_g+f_mean.

20. The method of claim 13, wherein the step of creating a representative high resolution MCG image includes:

defining the sparse measurement output as a vector g;

defining the linear model as Σ

;

extracting from Σ

the row corresponding to sparse measurement output to form a sub-eigenmatrix Σ

_g;

projecting g onto Σ

_g;

defining the establishment of coefficients as c_g=Σ

_g⁺(g_i−

μ

_g), where Σ

_g⁺ is the pseudo inverse of Σ

_g, μ

_g are extracted coefficients from a mean vector μ

of linear model Σ

; and

defining the high resolution MCG image vector h as h=Σ

·

c_g+μ

.