High-resolution magnetocardiogram restoration for cardiac electric current localization
First Claim
1. A magnetocardiogram (MCG) system comprising:
- a sensor unit including M×
M electromagnetic sensors producing an M×
M measurement output of M×
M data units, said M×
M measurement output constituting a first MCG image;
a data processing device having 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 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;
wherein the producing of said third MCG image includes;
defining the M×
M measurement output as a vector g;
defining the linear model as Σ
;
extracting from Σ
the row corresponding to the M×
M measurement output to form a sub-eigenmatrix Σ
g;
projecting g onto Σ
g;
defining the establishment of coefficients as cg=Σ
g+(gi−
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
·
cg+μ
.
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Accused Products
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.
3 Citations
18 Claims
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1. A magnetocardiogram (MCG) system comprising:
-
a sensor unit including M×
M electromagnetic sensors producing an M×
M measurement output of M×
M data units, said M×
M measurement output constituting a first MCG image;a data processing device having 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; anda high resolution MCG image synthesizer 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;wherein the producing of said third MCG image includes; defining the M×
M measurement output as a vector g;defining the linear model as Σ
;extracting from Σ
the row corresponding to the M×
M measurement output to form a sub-eigenmatrix Σ
g;projecting g onto Σ
g;defining the establishment of coefficients as cg=Σ
g+(gi−
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
; anddefining the high resolution MCG image vector h as h=Σ
·
cg+μ
. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
-
-
10. A magnetocardiogram (MCG) system comprising:
-
a sensor unit including M×
M electromagnetic sensors producing an M×
M measurement output of M×
M data units, said M×
M measurement output constituting a first MCG image;a data processing service having 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; anda high resolution MCG image synthesizer 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;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 measurement output forms 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, . . . fK┘
; and(D) training a PCA model given input data A, to obtain the eigenmatrix Σ
f. - View Dependent Claims (11)
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12. A method of creating a magnetocardiogram (MCG) image from a measurement output provided by a sensor unit including a plurality of electromagnetic sensors, each electromagnetic sensor contributing its output data to said measurement output, said method comprising;
-
providing a data processing device to implement the following steps; defining high resolution to mean a resolution substantially higher than the resolution provided by said 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 measurement output; and creating a representative high resolution MCG image by projecting said measurement output onto the subspace of the linear model, and establishing coefficients; wherein the step of creating a representative;
high resolution MCG image includes;defining the measurement output as a vector g; defining the linear model as Σ
;extracting from Σ
the row corresponding to measurement output to form a sub-eigenmatrix Σ
g;projecting g onto Σ
g;defining the establishment of coefficients as cg=Σ
g+(gi−
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
; anddefining the high resolution MCG image vector h as h=Σ
·
cg+μ
. - View Dependent Claims (13, 14, 15, 16, 17, 18)
-
Specification