Method and system for displaying confidence intervals for source reconstruction

The present invention involves the creation of an appropriate model and the use of that model to generate a best fit dipole represented by a vector (x, y, z). Once the best fit dipole is generated, a best fit field distribution and a field distribution of a modified dipole is created. Using a difference between the field distributions, a Singular Value Decomposition is used to compute the main axes of the confidence ellipsoids.

FIELD OF THE INVENTION

Generally, the invention relates to the field of source imaging. More specifically, the invention relates to the calculation and display of a confidence interval for a dipole fit in a source reconstruction.

BACKGROUND OF THE INVENTION

Physicians and researchers often need to identify patches of electrically active cortical or myocardial tissue in order to identify a source of illness or to map brain activity. While known monitoring equipment are capable of determining that electrical or magnetic activity has occurred, the determination of a source of that activity must often be calculated or estimated. The process of calculating or estimating the source of electromagnetic activity in tissue is generally referred to as source reconstruction.

There are a number of different methods known in the art for performing source reconstruction. Many of these methods involve creating a model which attempts to determine the source of activity through the use of mathematical formulas which describe electromagnetic field distributions. These formulas typically depend on the position and orientation of the source, the position and orientation of the sensors which pick up the electromagnetic signals, and the geometry and conductivity properties of the volume conductor (head or chest) tissue.

One known method of source reconstruction involves the determination of equivalent current dipoles. This method makes the basic assumption that the source of electromagnetic activity is focal and small in number. However, measured data exhibits a limited Signal-to-Noise Ratio (SNR) due to background activity, environmental and amplifier noise. The noise distribution of the data leads to scattered dipole positions in the source space around the most probable source position. As such, the reconstructed dipoles only represent the most probable source positions.

There is a need for an apparatus and a method to determine and to display an area surrounding the reconstructed dipoles which represent a most probable solution to the source reconstruction model given the noise level in the data. This area is generally known as a confidence interval and it represents a probability distribution which corresponds to the noise level.

SUMMARY OF THE INVENTION

The present invention is a method and an apparatus for displaying the confidence interval of a source reconstruction result. In one embodiment, an Equivalent Current Dipole (ECD) model is used to perform the source reconstruction. The ECD model defines a current dipole in terms of its location, strength and orientation, along with an estimate of its reliability (confidence volume). When using the ECD model, one of the vectors generated represents the source dipole location (the more important one since it represents the result of the non-linear least squares fit procedure). Another vector that is reconstructed represents the dipole orientation (solution of a linear inverse problem), so both result vectors should be distinguished. In order to compute the best fit field, both result vectors are needed.

Once the best fit dipole is generated, it is used to create a best fit field distribution. The best fit dipole position is also modified by a small amount (generally less than 1 mm) and a field distribution of the modified dipole is created. A difference between the best fit field distribution generated and the modified field distribution is computed and a Singular Value Decomposition is used to determine the main axes of confidence ellipsoid. An analysis of the signal noise is performed, and an estimate of the SNR is generated. A confidence interval is calculated from the estimated noise level and the difference of field strength. The confidence interval is then overlaid onto an anatomical image of the source tissue.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

A. General Overview

FIG. 1is a flowchart providing a general overview of the present invention. Box10represents the generation of a dipole fit at selected latencies. This step involves the creation of an appropriate model and the use of that model to generate a best fit dipole represented by a vector (x, y, z). Once the best fit dipole is generated, the vector is used to create a best fit field distribution as shown in box12. The best fit dipole is also modified by a small amount (generally less than 1 mm) as shown in box14and a field distribution of the modified dipole is created box16.

A difference between the field distribution generated in box12and the modified field distribution of box16is computed (box18). A Singular Value Decomposition is used to compute the main axes of the confidence ellipsoids (box20).

An analysis of the signal noise is performed, and an estimate of the SNR is generated (box22). A confidence ellipsoid is calculated (box24) from the estimated noise level of box22and the difference of field strength from box20. The confidence interval is then overlaid onto an anatomical image of the source tissue (box26).

Reference points are determined with using a Cartesian coordinate system anchored on (at least) three fiducial points on the subject's head. In one embodiment, the fiducial points include two external ear canal points and the nasion. The two ear canal points define the y-axis. The line perpendicular to the y-axis and passing through the nasion defines the x-axis, and the line perpendicular to the x-y plane passing at the intersection of the x-y axis defines the z axis.

Once the frame of reference has been established, appropriate models have to be used to reconstruct the source of the measured electromagnetic data. Neural activity can often be represented as a primary source with a specific current density in a closed volume. The current density is comprised of primary (intracellular) and secondary (extracellular) components.

Localizing the primary current sources is known as solving the inverse problem. However, there is typically no unique solution to the inverse problem because there may be an infinite number of current distributions which could be used to explain the externally measured magnetic field or electrical potential. As such, it is necessary to make assumptions regarding the location or the geometry of the source.

Given a particular data set, an appropriate model is selected based on a particular model criterion. There are many model criterion that are known in the art. These formulas depend on the number, position and orientation of the current source, the position (and orientation in the magnetic case) of the sensors, and the geometry and conductivity properties of the head or heart tissue. For the purposes of explanation only, the embodiment of the present invention is described as using an ECD or elementary dipole model. The ECD model defines the current dipole in terms of its location, strength and orientation, along with an estimate of its reliability (confidence volume). One skilled in the art can readily appreciate that the present invention is easily adapted to support other known models.

In order to determine a best fit dipole, the ECD model is used because analytical or numerical expressions exist that describe their electromagnetic field distributions. For example, assuming an infinite homogeneous volume (used for the purpose of simplifying the mathematical explanation only, typically, spherical shell models, three or four shells representing skin, skull, and brain, or a Boundary/Finite Element Method model are used) conductor (conductivity σo, permeability u0) a dipole at positionrjcurrentj, sensor at positionrwill have the following electric potential V0and magnetic field B0:

Due to the linearity in the dipole components of all volume conductor models the so-called lead-field fofprmulation provides a more compact notation comprising all sensor signals in column vectors:
V=LVjandB=LBj(3)

The lead-field matrices:
LV(3* Se) andLB(3Sm)
contain all geometric information, such as dipole and sensor positions, and volume conductor properties, whereas the linear dipole componentsiand thereby the dipole orientations are separated.

In a spatio-temporal formulation, the vectorMcontaining the measured data has to be extended to a matrix M, where each column vector represents one sample. Accordingly, the current component vectorjhas to be extended. For keeping the expressions better readable, the vector and matrix underlines are omitted in the following equations:
(j→j,L→L,M→M).

The best fit solution of the inverse problem is determined by minimizing the residual variance (squared deviation) between the measured data and the forward calculated fields using the Frobenius nonn of a matrix A:

M is the spatio-temporal measured data matrix (s sensors * t samples), the lead-field matrix L (s·c current dipole components) comprises the dipole positions, the volume conductor characteristics, and the sensor geometry, and j contains the (c*t) temporal loadings or strengths of the (c=3·d dipoles) dipole components. The best fit currents ĵ, that minimize Eq. 5 in the overdetermined case (more knowns than unknowns: s>c) are given by [Lawson and Hanson, 1974; Ben-Israel and Greville, 1976]:
{circumflex over (j)}=(LTL)−1LTM(6)

The best fit dipole positions can then be found by nonlinear mininization algorithms (e.g. Nelder-Mead-simplex [Nelder and Mead, 1965]) (Box10). For each dipole position or configuration the lead-field matrix L has to be set up and the best fit deviation (Eq. 5 with j=ĵ) is calculated by solving the linear problem for the dipole strengths (Eq. 6). The minimizer changes the nonlinear parameters (the dipole positions) and looks for the global minimum of the error hypersurface.

Once a best fit coordinate is found, a field distribution is calculated based on the best fit coordinate (Box12). The best fit coordinates are also modified (in the order of 1 mm) (Box14) and a field distribution is calculated based on the modified best fit coordinates (Box16).

From Eq. 5 and 6, the best fit field distribution F is calculated as follows:
F=Lĵ=L(LTL)−1LTM(7)

The difference between best fit field distribution and the modified best fit coordinates is then calculated (Box18). By modifying the best fit dipole coordinates xi=x, y, z by little increments dxi, modified lead field matrices L1, field distributions F1, and the difference between the field distributions dF (normalized to the position changes) corresponding to these changes can be calculated as:
dFi=(Fi−F)/dxi(8)

A main axes of the difference in field distributions is then computed by Singular Value Decomposition (SVD) (box20). For each dipole position k three difference field vectors dFk(i=x, y, z) are obtained, that-can be written as columns of a difference field matrix dFk(three columns, s rows). In a linear approximation, the main axes of the corresponding error ellipsoid can then be computed by a SVD of this matrix:
dFk=UkΣkVkT(9)

The axis orientations are contained in rows of the three by three rotation matrix VkTand the lengths of the axes Σkiare obtained from the number of sensors s (due the normalization of the SVD), the mean noise level N, and the three singular values Σki
lki=N√{square root over (s)}/Σki(10)

In this linear approximation the lengths of the confidence ellipsoid axes are proportional to the noise level (Eq. 10) and thus the confidence volume vkproportional to the third power of the noise level N:
vk=lkxlkylkz·4π/3=4πN3s3/2/(3ΣkxΣkyΣkz)  (11)

The confidence interval is then overlaid onto an anatomical map, using the coordinates of the best dipole fit and their circumference as shown inFIG. 2. In one embodiment, after registration of the two modalities (functional [EEG/MEG] and anatomical [MR/CT] coordinate system) which is done by matching at least three landmarks that can be identified in both modalitites, the confidence ellipsoids can be transformed into the anatomical coordinate system like the dipole positions and orientations using the same transformation algorithm (vector—matrix multiplication representing a rigid transformation [rotation and shift operation]).

FIG. 3is an illustration for the ability of the confidence ellipsoids to be useful to test the dipole model used. In this case a two dipole model was applied to a data-set that could already be explained by a single dipole at the selected latency (FIG. 2). The meaningful dipole stays at the left temporal lobe, whereas the second dipole, that is not really needed to explain the measured data, can be everywhere within the left temporal frontal region. Without displaying the confidence ellipsoids, just two dipole symbols would be displayed, only the fit quality is slightly improved, due to larger degrees of freedom of the two dipole solution (six non-linear (position) parameters and six linear component parameters compared to three non-linear and three linear parameters in the one dipole case). The point cloud in bothFIG. 2andFIG. 3is the same (points on the segmented cortical surface just for visualization purposes, this could also be a semi-transparent rendering of the cortical surface).

As shown inFIG. 4, the confidence ellipsoid can also be overlaid over anatomical data in an orthogonal slice display. The ellipsoids are projected onto the corresponding planes of the anatomical data.

C. System Configuration

As shown inFIG. 5, in one embodiment, the present invention includes a processor50in communication with a detector52, an imaging source54, and a display56. For the purposes of this disclosure, the term in communication shall include communication by hardwire means, by telecommunications means, or by the transfer of data using a memory device. The system components can be fully or partially integrated with each other, or they may be stand alone components.

The processor50can be a single or multiple computers, or can be any processor, integrator, or any hardwired circuit configured to perform the steps described in the subject invention.

The detector52can be any known physiological monitoring device or a combination thereof. Preferably, the detector is a combination of an Electroencephalogram (EEG) and a Magnetoencephalogram (MEG).

The imaging source54can be any known imaging device, but preferably, the imaging source is a Magnetic Resonance Imaging Unit (MRI) or a Computerized Tomography Unit (CT).

The display can be any that is known in the art, but preferably, the display is of a high resolution.

The matter set forth in the foregoing description and accompanying drawings is offered by way of illustration only and not as a limitation. While a particular embodiment has been shown and described, it will be obvious to those skilled in the art that changes and modifications may be made without departing from the broader aspects of applicant's contribution. The actual scope of the protection sought is intended to be defined in the following claims when viewed in their proper perspective based on the prior art.