JPH10323335A - Device and method for diagnosis for electrical phenomenon in heart - Google Patents

Abstract

PROBLEM TO BE SOLVED: To estimate and display distributional information of excitement accessing time and amplitudes of action potential in a heart more speedily, assuredly and accurately than conventionals. SOLUTION: A diagnostic device, to estimate and display electrical phenomenon in the heart of a subject based on at least either one of the electric potential and the magnetic field measured at the subject, is installed with a means (steps S2 to S11) to simultaneously estimate both the distributional information of excitement accessing time and amplitudes of action potential in the heart and a means (step 12) to display the above said distributions. Also, the device is installed with a SQUID fluxmeter to measure the magnetic field of the measuring points on the body surface of the subject.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus and method for diagnosing electrical phenomena in a heart of a subject, and more particularly, to measuring an electric potential or a magnetic field generated from the heart to measure the electrical phenomena in the heart. The present invention relates to a diagnostic device and a diagnostic method for implementing a novel solution to an estimated electrocardiogram inverse problem or magnetocardiogram inverse problem. The electrical phenomena in the heart, specifically, in the present invention, the arrival time of myocardial excitation, the action potential amplitude as myocardial electrical activity,
It can be considered as a cardiac excitement propagation process represented by the conductivity distribution in the ventricle, the current dipole density, and the like.

[0002]

2. Description of the Related Art As one of prior arts of this kind, for example, a diagnostic apparatus described in Japanese Patent Application Laid-Open No. 8-289877, which has been already proposed by the present inventors, is known. This diagnostic apparatus constructs a heart model by software in a computer as shown in FIG. 10 and shows the excitement propagation of the heart using the die crust method or the Warshall Freud method based on this model. We propose a method to simulate efficiently and to derive an electrocardiogram and a magnetocardiogram from the simulation results.

When this method is used, the shape of the ventricle, the position of the ventricle, the orientation of the ventricle, one or more initial excitation positions, the excitation time at those positions, and the excitation propagation speed are calculated in order to calculate the electrocardiogram or the magnetocardiogram. It is necessary to give values such as the distribution in the ventricle and the distribution of the conductivity in the ventricle as parameters to the ventricle model.

Further, in Japanese Patent Application Laid-Open No. 8-289877, the present inventor has proposed an inverse problem solving method based on the above-described excitation propagation simulation method. Specifically, some of the parameters (for example, the initial excitation position) given to the ventricle model are set so that the difference between the measured electrocardiogram or electrocardiogram and the electrocardiogram or electrocardiogram calculated by the above-described simulation method becomes as small as possible. And the values of the excitement times at those positions) are corrected, and the value when the difference becomes the smallest (for example, the initial excitement position and the excitement times at those positions) is output as the estimation result. At this time, appropriate values are given to parameters other than the selected parameters (for example, parameters other than the initial excitation position and the excitation time, the excitation propagation velocity distribution and the action potential amplitude distribution), and these parameters are fixed at the time of estimation. It is treated as a value.

Prior Art 2 As another prior art, a method of estimating the time of arrival at the ventricular surface from the QRS waveform of a multi-channel electrocardiogram on the body surface is described in the document "H. Roozen et al .: Compu-ting". the
activation sequence at the ventricular heart surf
ace from bodysurface potentials, Medical & Biologi
cal Engineering & Computing, May, 1987 ". Specifically, this paper describes the excitation arrival time distribution in the myocardium and each channel under the assumption that the conductivity in the myocardium is uniform and isotropic, and that the action potential amplitude is constant. The volume integral equation (integration area is in the ventricular muscle) describing the relationship with the induced electrocardiogram QRS waveform is converted into a boundary integral equation, and this equation is solved to obtain the Q of the electrocardiogram.
A method of analyzing the distribution of the excitation arrival time at the myocardial boundary surface from the measurement result of the RS waveform is shown.

[0006]

However, the above-mentioned prior art has the following various problems which must be solved.

In the case of the prior art 1 In the case of the diagnostic device for the electrical phenomena in the heart according to the prior art 1, since the magnetocardiogram and the parameter to be estimated have a non-linear relationship,
To estimate this parameter, it is necessary to perform a large number of iterative operations, and it is necessary to perform an excitation propagation simulation for each of the iterative operations. Excitation propagation simulation requires a lot of calculation time, and as a result, there is a first problem that the time required for one derivation becomes enormous.

In the method for estimating the electrical phenomena in the heart according to the prior art 1, the analysis results when the initial excitation site and the initial excitation time are estimated as variables and the excitation propagation time and the action potential amplitude distribution are fixed values are obtained. IEICE Transactions A, Vol.
116-, No. 8, 1996, pages 698-704.

[0009] In this report, the excitation propagation time and the action potential amplitude distribution are treated as fixed values, so that they may not actually conform. For example, assuming that the method for estimating intracardiac electrical phenomena according to Prior Art 1 is actually applied to a patient such as myocardial infarction, there is a delay in excitation conduction at the infarct site, and the amplitude of the action potential is reduced. Conceivable. In addition, since the initial excitement site and the excitement time vary greatly between individuals, it is impossible to treat the excitement propagation velocity distribution, the action potential amplitude distribution, the conductivity distribution, the initial excitement site, and the initial excitement time with some fixed values. That is, it is necessary to estimate all these parameters as variables.

However, on the other hand, if all these parameters are variables, the number of estimated parameters becomes very large, the size of the search space for parameters becomes enormous, and it becomes extremely difficult to perform stable estimation. Therefore, there is a second problem that it is extremely difficult to apply the prior art 1 to a patient having myocardial infarction or the like and diagnose the abnormality.

On the other hand, when the method for estimating an intracardiac electrical phenomenon according to the prior art 1 is applied to a patient such as an arrhythmia who does not have a basic disease such as myocardial infarction, an excitation propagation velocity distribution, an action potential amplitude distribution, and a conductivity distribution. Is given as a default value,
IEICE Transactions II, Vol. 116-, No. 8,1
As described on pages 996, 698-704, it is conceivable to estimate only the initial excitation site and the initial excitation time.

Also in this case, it is desirable to provide in detail the distribution of the excitation propagation velocity, action potential amplitude, and electrical conductivity in the heart muscle in order to perform an accurate analysis. But,
At this time, such detailed information is not known,
It has to give rough information and cannot simulate excitement propagation with high accuracy. As a result, there is a third problem that the estimation result is greatly influenced by the previously given vague values of the excitation propagation speed, the action potential amplitude, and the conductivity, and the analysis result becomes unstable.

[0013] In addition, patients with arrhythmia often have underlying diseases such as myocardial infarction and ischemia. For this reason, in the case where the device for estimating an intracardiac electrical phenomenon can be applied only to an arrhythmic patient who does not have an underlying disease, there is a fourth problem that versatility is low because applicable patients are limited. .

On the other hand, in the prior art 2, since it is not necessary to perform the excitation propagation simulation, the problem that the analysis takes an enormous amount of time is reduced, but the action potential amplitude distribution and the conductivity distribution are uniform. When converting the volume integral equation, which is the basic equation of this method, to the boundary integral equation, this assumption is essential, so it can be applied to patients with diseases such as myocardial infarction. However, similarly to the prior art 1, there is a fifth problem that the applicable patients are limited and the versatility is reduced.

Further, there is a problem related to the ventricle model chest conductivity distribution model common to the prior arts 1 and 2.

In detail, in the prior arts 1 and 2, in order to improve the accuracy of estimating the distribution of the time of arrival of excitement, it is necessary to perform an accurate analysis using a ventricle model adapted to the shape of the ventricle of each subject. is required. In the conventional method, the size and the position of the heart are manually measured from the MR の image of the subject, and a ventricle model having a typical ventricle shape is installed by enlarging or reducing according to the measured value.

That is, it is difficult to automatically detect the size of the heart.
It is difficult to completely and automatically extract the shape of the heart from the three-dimensional images of X-CT and X-CT, and it is practically impossible to create a model that is individually adapted for each of many patients on a daily basis. It is possible.

In the magnetocardiogram inverse problem, it is generally pointed out that it is necessary to use a chest conductivity distribution model adapted to the chest shape and the conductivity distribution of each subject even when calculating the magnetic field. However, conventionally, since the boundary element method or the finite element method is used in order to consider the conductivity distribution of each subject, the tissue boundary of the ventricle and the chest is constituted by a triangular mesh, or the chest region is a tetrahedron. It must be composed of meshes. At this time, it is necessary to fit the tissue boundary to the mesh, but it is difficult to fully automate such processing. Therefore, the ventricle shape and the conductivity distribution of the chest of each subject can be obtained from three-dimensional MRI and X-CT images. It cannot be extracted completely automatically, and as a result, it is virtually impossible to routinely create a chest conductivity distribution model for a large number of patients.

[0019] Under these circumstances, an estimation method taking into account the shape of the ventricle and the distribution of the thoracic conductivity suitable for the patient has not been implemented, and the estimation accuracy of the excitation arrival time distribution is low, making it difficult to provide practical clinical practice. There were 6 problems.

One object of the present invention has been made in view of the various unresolved problems of the prior art described above.
The excitement propagation process in the heart (heart muscle excitation arrival time, action potential amplitude, etc.) is faster and more stable than before, without needing detailed information on the distribution of the excitation propagation velocity in the heart in the heart in advance. And it is possible to estimate with high accuracy.

Another object of the present invention is to make the excitation propagation process in the heart faster and more stable than before, without the need for detailed information on the distribution of the excitation propagation velocity in the heart in the heart in advance. In addition, it is possible to perform estimation with high accuracy, to apply the method to patients having abnormalities such as myocardial infarction, and to provide versatility.

Still another object of the present invention is to enable easy and automatic extraction of the patient's ventricular shape and chest conductivity distribution for each patient, and to easily diagnose many patients. To improve practicality.

Still another object of the present invention is to make the excitation propagation process in the heart faster and more stable than in the past without requiring detailed information on the distribution of the excitation propagation velocity in the heart in the heart in advance. , And with high accuracy, and also applicable to patients with abnormalities such as myocardial infarction, to provide versatility, furthermore, the ventricular shape and thoracic conductivity distribution of each patient can be easily determined for each patient. , And automatically extract, and improve the practicality by easily diagnosing many patients.

[0024]

Here, the myocardial electrical activity amount is, specifically, the amplitude value of the action potential in the rising phase of the action potential of the myocardium, the distribution of the electrical conductivity of the myocardium, and the product of both. Is the absolute value of the current dipole density, or a quantity related to those quantities, and represents the magnitude of the electrical activity of each heartbeat in each part of the myocardium, not the value at each time such as the instantaneous value of the action potential Quantity.

In order to achieve the above various objects, a diagnostic apparatus for an intracardiac electrical phenomenon according to the present invention estimates and displays an electrical phenomenon in the heart of a subject based on at least one of a potential and a magnetic field measured from the subject. Distribution information estimating means for simultaneously estimating the distribution information of the excitation arrival time in the heart and the distribution information of the myocardial electric activity amount representing the magnitude of the electrical activity of the myocardium, and the distribution information of the excitation arrival time. And display means for displaying the distribution information of the myocardial electrical activity.

[0026] The present invention is characterized in that a measuring means for measuring at least one of a potential and a magnetic field from a measurement point on the body surface of the subject is provided.

Preferably, the distribution information estimating means includes an expression means for expressing the distribution of the time of arrival of excitation and the distribution of the amount of myocardial electrical activity in the heart with a finite number of parameters, and Waveform calculation means for calculating a time waveform at at least one of each measurement point of the potential and the magnetic field based on the distribution of the excitation arrival time and the distribution of the myocardial electric activity amount represented by: And a parameter estimating means for estimating the plurality of parameters by using a nonlinear optimization method so that at least one of the waveforms and the time waveform of at least one of the calculated potential and the magnetic field coincide with each other. In this case, for example, the distribution information estimating unit includes a heart model setting unit that sets the heart model from three-dimensional image data of the inside of the subject. Further, the expression means is means for expressing the distribution of the time of arrival of excitation in the heart and the distribution of the amount of myocardial electrical activity in a number smaller than the number of representative points of the heart model. It is also preferable that the estimating means includes a calculating means for calculating the distribution of the excitation arrival time and the distribution of the myocardial electric activity based on the small number of parameters.

[0028] More preferably, said expression means comprises: the excitation arrival time at each representative point of the heart model; a distribution of the excitation arrival time represented by the myocardial electrical activity in the heart; Means for expressing the distribution of the amount of myocardial electrical activity by a linear function of a smaller number of parameters than the representative points of the heart model, wherein the distribution information estimating means is based on the small number of parameters represented by the linear function. Calculation means is provided for calculating the distribution of the excitation arrival time and the distribution of the myocardial electrical activity. At this time, for example, the linear function is a function based on Fourier transform or inverse Fourier transform. Further, for example, the linear function is a function based on a matrix operation. In one embodiment, the matrix of the matrix operation is a matrix obtained from a covariance matrix of the measured or virtual excitation arrival times.

[0029] More preferably, said heart model setting means is means for setting a shape of a ventricle as said heart model by a combination of a large number of polyhedrons. This polyhedron is
For example, it is characterized in that it is a hexahedron, a pentahedron, or a tetrahedron, or a combination of at least two of these polyhedrons.

[0030] As a further preferred embodiment, there is provided image transfer means for inputting and transferring the three-dimensional image data of the inside of the subject obtained from a medical modality, and the distribution information estimating means transfers the image data from the image transfer means. A heart model setting unit configured to set a model of the subject's heart from the obtained three-dimensional image data.

As still another preferred embodiment, the heart model setting means moves, rotates, and / or deforms a ventricle model having a predetermined shape, and uses the three-dimensional image data by a non-linear optimization method. It is a means for setting the position, orientation and shape of the ventricle of the ventricle model of the subject by fitting the ventricle surface. Preferably, the heart model setting means includes means for interactively moving, rotating, and / or deforming the ventricle model having the predetermined shape.

Preferably, the heart model setting means includes means for displaying the three-dimensional image data and the ventricle model of the subject in a superimposed manner.

As still another preferred embodiment, the heart model setting means includes means for expressing a ventricle shape as the heart model by arranging units constituting a plurality of types of images having predetermined shapes. The predetermined shape is, for example, a hexahedron.

As still another preferred embodiment, the distribution information estimating means is configured to generate a biological model from one or more types of three-dimensional image data obtained from one or more types of medical modalities. The biological model creating means includes a tissue type classification means for classifying a tissue type of each region of a living body in a three-dimensional image region from the three-dimensional image data. In this case, the biological model creating means may have conductivity setting means for setting the conductivity for each of the regions based on the classification result of the tissue type separating means. Further, in this case, the biological model creating means includes voxel configuring means for configuring a living body in the three-dimensional image area by a large number of voxels, and the distribution information estimating means uses the difference method to calculate the large number of voxels. Calculation means for calculating at least one of the potential waveform and the magnetic field waveform.

A method for diagnosing an intracardiac electrical phenomenon according to the present invention is a method for estimating and displaying an electrical phenomenon in the heart of a subject based on at least one of a potential and a magnetic field measured from the subject. Simultaneously estimating the distribution information of the excitement arrival time within and the distribution information of the myocardial electric activity amount representing the magnitude of the electrical activity of the myocardium of the heart; and Displaying distribution information.

Thus, in the present invention, for example, the excitation arrival time distribution in the heart and the myocardial electric activity amount distribution are simultaneously estimated. Also, preferably, a relatively small number of parameters for generating the excitation arrival time distribution and the myocardial electric activity amount distribution are to be estimated. Instead of myocardial electrical activity distribution, conductivity distribution,
The distribution of the product of the myocardial electrical activity and the electrical conductivity (current dipole density) may be estimated. More preferably, the ventricle shape and the chest shape are composed of a large number of voxels, and the potential calculation and the magnetic field calculation are executed using the difference method.

Other features and effects of the present invention will become apparent from the embodiments of the present invention described below and the accompanying drawings.

[0038]

Embodiments of the present invention will be described below with reference to the accompanying drawings.

First Embodiment A first embodiment of the present invention will be described with reference to FIGS.

FIG. 1 shows an outline of the configuration of a diagnostic apparatus for an intracardiac electrical phenomenon according to this embodiment. This diagnostic device is S
It comprises a QUID (superconducting quantum interference device) magnetometer 11, an image collection device 12, an image transfer device 13, and a computer device 14.

The SQUID magnetometer 11 includes a SQUID sensor 11a, a driving circuit 11b, and a signal collecting circuit 11c.
Multi-channel SQ which is a highly sensitive magnetic sensor
It is configured as a UID magnetometer. The SQUID magnetometer 11 measures a cardiac magnetic field waveform at several points (measurement points) on the chest using a SQUID sensor, and amplifies / amplifies this waveform signal.
Filter processing and conversion into digital data by Α / D conversion. The digital amount of magnetic field waveform data is supplied to the computer device 14.

The image acquisition device 12 is composed of one or more modalities for acquiring three-dimensional image data, such as an MRI (magnetic resonance imaging) device, an X-ray CT (X-CT), an ultrasonic diagnostic device, etc. The image data (raw image data) inside the body of the user is collected and supplied to the image transfer device 13. The image transfer device 13 includes, for example, a CPU and a memory, stores the sent image data, and supplies the image data to the computer device 14 in response to a command from the computer device 14. The image transfer device 13 is preferably configured to supply image data to the computer device 14 via a network or a storage medium, and also performs image format conversion and reconstruction into three-dimensional image data as necessary. Is configured.

The computer device 14 performs an analysis and an operation for estimating an excitement propagation process in the heart.
An arithmetic processing unit 14a having a CPU as a main part, a memory 14b,
An input device 14c and a display device 14d are provided. Memory 1
The analysis / arithmetic program is built in 4b in advance, and the arithmetic processor 14a executes this program. An example of this execution is shown in the flowchart of FIG.

This flowchart will be described. First, the arithmetic processing unit 14a receives the magnetic field waveform data supplied from the SQUID magnetometer 11 (step S1), and executes signal processing of the waveform data (step S2). In this signal processing, a digital filter, an averaging process for each heartbeat, and the like are performed for noise removal and the like. Then, a time corresponding to the QRS complex of the magnetocardiogram is detected, and a magnetic field waveform corresponding to the QRS complex of each channel is extracted. The reference point for the time may be set arbitrarily. For example, the time near the start point of the QRS complex may be set to 0, or the time at the maximum amplitude may be set to 0.

Thereafter, the arithmetic processing unit 14a inputs a plurality of tomographic image data (three-dimensional data) from the image transfer device 13 (step S3), and performs measurement and setting of the heart shape based on the image data. (Step S4). Thus, for example, a region of a ventricular muscle is detected from three-dimensional image data obtained by acquisition of an MRI apparatus, and a ventricular shape model is constructed.

FIG. 3 shows a model serving as a basis of a ventricular model to be constructed. In this example, the shape of the ventricle is represented by a combination of many hexahedral elements (units). This ventricle model has parameters such as length in the long axis direction, outer diameter of the left ventricle in each direction orthogonal to the short axis, left ventricle wall thickness, right ventricle wall thickness, and length of the gap between the septum and the right ventricle wall. It can be operated and deformed via the input device 14c according to each subject. Parameters other than the length in the long axis direction can be specified for each of the apex, the base, and any level therebetween.

The fitting process for fitting the ventricle model to the shape of the ventricle of each subject is automatically performed here, for example, by an interactive method by manual operation or by using a nonlinear optimization method. In each method, each parameter for deforming the ventricle model so that the surface of the ventricle and the surface of the ventricle model of the three-dimensional image collected from the subject's MRI, X-CT, ultrasonic diagnostic apparatus, etc. match as closely as possible. And the parameters representing the position and orientation of the ventricle model in the subject are changed.

When the shape of the ventricle model is fitted to the shape of the ventricle of the three-dimensional image by using a manual interactive method, the shape of the ventricle model and the projected view of the three-dimensional image in any direction or a cross-sectional view at any position. Is displayed on the display 14d at the same time, and the parameter is changed while the operator checks the display screen. Further, the position, orientation, and shape of the ventricle model are changed according to the changed parameters, and the state of the change is displayed on the display 14 in conjunction with the display screen.
It is also desirable to have a configuration in which d is displayed.

In the case of automatically fitting using the non-linear optimization method, the ventricle position, orientation,
It is desirable that the shape, the final fitted ventricular position, orientation, and shape be displayed on the display 14d together with the ventricular shape of the three-dimensional image. Further, it is desirable that the shape of the ventricle be manually changed as required, and the result be used as an initial value so that the fitting operation can be restarted by automatic processing.

Arithmetic processor 14a of computer device 14
After that, set heart shape data and measurement (input)
Based on the subject's magnetocardiogram data during the QRS period, the analysis of the excitation arrival time distribution and the action potential amplitude distribution as the estimated value of the excitation propagation process in the ventricle of the heart is performed (steps S5 to S12).

As an algorithm for analyzing the excitation arrival time distribution and the action potential amplitude distribution, various nonlinear optimization algorithms can be used. These algorithms include various methods such as a quasi-Newton method, a conjugate gradient method, a conjugate direction method, a simulated annealing method, and a genetic algorithm method. Of these, typical algorithms include initial value setting processing of the excitation arrival time distribution and action potential amplitude distribution (FIG. 2, step S
5), calculation processing of the current source distribution in the heart (FIG.
6), magnetic field calculation processing (FIG. 2, step S7), error calculation processing (FIG. 2, step S10), and change processing of excitation arrival time distribution / action potential amplitude distribution (FIG. 2, step S)
9) is included. The order in which each of these processing steps is performed is well known in each non-linear optimization algorithm, and the order is determined according to the algorithm employed. FIG. 2 schematically shows the order applied to many algorithms such as the quasi-Newton method.

The contents of the processing steps S5 to S10 will be described below.

In the initial value setting processing of the excitation arrival time distribution and the action potential amplitude distribution in step S5, the initial values of the endocardium and epicardium excitation arrival times and the initial value of the action potential amplitude are set.

In the case of setting the initial value of the arrival time of the endocardium and epicardium excitation, the initial value of the excitation arrival time is set for all the vertices on the endocardium side or the epicardium side of the ventricle model in FIG. I do. This may be randomly determined within a predetermined range, for example, from 0 to 80 ms based on the QRS start time, or a typical excitation start time of a normal heart may be set.

Similarly, for the action potential amplitude, an initial value of the action potential amplitude is set for all apexes on the endocardium side or epicardium side of the ventricle model. The range may be random, or when a specific action potential amplitude is used as the amount of myocardial electrical activity, a normal action potential amplitude of about −90 mV may be applied to all vertices. . further,
In the ventricle model of FIG. 3, representative points are also provided at points other than the epicardium and the endocardium, and the excitation arrival time and action potential amplitude of all representative points including the endocardium, the apex of the epicardium and other points are included. May be set as the initial value.

In the calculation process of the original cardiac current distribution in step S6, the excitation arrival time and the action potential amplitude inside each hexahedron of the heart model are interpolated, and the excitation arrival time and the action potential amplitude obtained by the interpolation are calculated. Is used to calculate the shape of the excitation wavefront at several times during the QRS period and the current source distribution on the excitation wavefront. As an interpolation method, for example, an interpolation polynomial frequently used in the finite element method and represented by the following equation may be used. Although this interpolation method will be described using the excitation arrival time distribution as an example, the same method is used for the action potential amplitude.

[0057]

(Equation 1)

[Outside 1]

[0058]

(Equation 2)

[Outside 2]

Further, in the magnetic field calculation processing in step S7, the positions, orientations,
Based on the magnitude, a magnetic field to be measured by each SQUID sensor installed on the chest is calculated for each time during the QRS period. This calculation may be performed based on Biosavar's law assuming that the conductivity distribution of the fuselage is an infinite uniform medium or a semi-infinite flat plate, or may be performed using Servas's formula assuming a concentric spherical conductivity sphere. You may. Alternatively, a magnetic field calculation taking into account the actual shape of the myocardium, blood, lung, fat layer, skin, and the like can be performed using the boundary element method or the finite element method.

The processes in steps S6 and S7 are repeated a required number of times (step S8).

Furthermore, the distribution of the arrival time of the excitement in step S9
In the process of changing the action potential amplitude distribution, the excitation arrival time distribution and the action potential amplitude distribution set in the heart model are finally determined by the magnetocardiogram measured in steps S1 and S2 and the step S2.
The value is changed based on the values repeatedly calculated in step S8 so that the difference between the magnetocardiograms calculated in step 7 is reduced. How to change specifically depends on various nonlinear optimization algorithms.

Further, in the error calculation process in step S10, an error e indicating how much the magnetocardiogram in the QRS period obtained through the processes in steps S1 and S2 differs from the magnetocardiogram in the QRS period calculated in step S7. Is calculated. For example, the magnetocardiogram at the j-th time measured by channel i is B
_ij , and similarly, when the magnetocardiogram obtained by calculation is C _ij ,

(Equation 3) The error e is obtained by the following equation. b is the root mean square of B _ij , and c is the root mean square of C _ij .

In the analysis processing of the excitation arrival time distribution and the action potential amplitude distribution, the above-described processing steps S6 to S10 are repeatedly executed as required by the adopted nonlinear optimization algorithm ( Step S1
1). As a result, a distribution in the ventricle of the excitation arrival time and the action potential amplitude that minimizes the error e is obtained.

The data of the excitation arrival time distribution and the action potential amplitude distribution estimated in this way are then displayed on the display 1.
4d is displayed (step S12).

FIG. 4 shows an example of the display result. In this example, an isochronogram of the arrival time of the excitement is displayed on the developed views of the endocardium and the epicardium. The isochronous diagram is a display method in which points at which excitement starts at the same time are connected by a line. Further, as shown in the figure, the distribution of the action potential amplitude is displayed in a superimposed manner depending on the shade of the color or the difference in the hue.

In this example, the ventricle is cut along the longitudinal direction and is shown in a developed view in which the epicardium, the left ventricle, and the right ventricle are developed in a circular shape. An isochronogram or an action potential amplitude distribution may be displayed above. Alternatively, the excitation arrival time distribution and the action potential amplitude distribution may be displayed on the cross section of the ventricle. Further, the heart model may be superimposed and displayed on the perspective projection view. Furthermore, the isochronogram and the action potential amplitude may not be superimposed but displayed separately. Further, numerical values of the excitation start time and the action potential amplitude may be shown in the figure.

Further, FIGS. 11 to 14 show display examples of data of the estimated excitation arrival time distribution and the action potential amplitude distribution. The processing for performing these displays is executed by the arithmetic processing unit 14a.

FIG. 11 shows an example in which the isochronous diagram of the excitation arrival time distribution and the gray-scale image of the action potential amplitude distribution are individually superimposed on each of two identical long-axis sectional views of the myocardium, and they are simultaneously displayed. is there. As shown in the figure, since there is a region where the action potential amplitude is reduced in the apex of the left ventricle, it is possible to easily determine that there is some kind of myocardial abnormality in this region. In other words, since both the excitation arrival time distribution and the action potential amplitude distribution are displayed at the same time, various arrhythmias with abnormal conduction conduction, myocardial ischemia and myocardial infarction with abnormal action potentials, and various heart diseases are shown. Excellent diagnostic display. Further, the number of images required for discriminating various heart diseases can be greatly reduced as compared with the conventional current source density distribution display, and the labor required for image interpretation can be reduced.

FIG. 12 shows an example in which the isochronous diagram of the excitation arrival time distribution and the grayscale image of the action potential amplitude distribution are superimposed and displayed on the same long-axis sectional view of the myocardium. As a result, in addition to the advantages described with reference to FIG. 11, only one image is required, and the display space on the screen can be saved. Therefore, analysis results (excitation arrival time distribution and action potential amplitude distribution) on a plurality of different ventricular cross sections can be arranged and presented on one display screen or paper. On the other hand, since the excitation arrival time distribution and the action potential amplitude are superimposed and displayed, for example, it is easy to read the correspondence between the excitation arrival time in the abnormal region of the action potential amplitude and the excitation wavefront shape in the abnormal region. It is possible to contribute to improvement of diagnostic performance and diagnostic efficiency. FIG. 13 shows an example in which the same display as in FIG. 12 is performed on the short-axis cross section of the ventricle.

FIGS. 14 (a) and 14 (b) relate to an example in which an excitation arrival time distribution on the surface of the ventricle is superimposed and displayed on the perspective projection view of the ventricular shape model. FIG. 2B is a diagram for explaining a method of designating the viewing direction of the perspective projection view. The lower left and upper cross sections of the screen in FIG. 11B are displayed in the same manner as described above (see FIGS. 11 to 13), and the right side of the screen is excited on a perspective projection view or a parallel projection view from an arbitrary direction. The time distribution, the action potential amplitude distribution, or both are displayed. The arrow at the lower left of the screen indicates the line of sight. This arrow can be interactively moved by the operator on the cross section from the input device 14c. This arrow can be further moved on the cross section at the upper left of the screen. The operator can set the viewing direction to an arbitrary direction by rotating the ventricle and moving the arrow. FIG. 7A specifically shows an example in which the excitation arrival time distribution is superimposed and displayed on the projection view, which is an example displayed on the right side of the screen in FIG. By displaying in this manner, in addition to the various advantages described above, when observing myocardial ischemia, there is an advantage that an ischemic region on the epicardium can be easily found.

In the above-described processing of the first embodiment, the processing in steps S1 and S2 is the signal processing means, the processing in steps S3 and S4 is the heart shape setting means, and the processing in step S5 is the initial value setting means. In steps S6 and S
The processing of steps S8 and S11 executes the current source distribution calculation means in step S11.
Steps S7, S8 and S11 execute the magnetic field calculation means.
Steps S9 and S1 correspond to the changing means.
The process 1 corresponds to the error calculation unit, and the process of step S12 corresponds to the display command unit. FIG. 9 shows the functional connection of these units.

As described above, according to the present embodiment and its modification, the action potential amplitude distribution and the excitation arrival time distribution can be simultaneously estimated. The ventricular electrical activity can be approximately represented by an action potential amplitude distribution and an excitation arrival time distribution for any normal and abnormal ventricles whose heart rate is below a certain rate. For example, myocardial ischemia or myocardial infarction can be expressed as if the ischemic and infarcted sites have a slower propagation of excitation than a normal site and have a smaller action potential amplitude. Furthermore, an arrhythmia such as ventricular tachycardia can be expressed as an excitation arrival time distribution different from normal if the heart rate does not become extremely low, such as during ventricular fibrillation. Therefore, the diagnostic device for intracardiac electrical phenomena according to the present embodiment can be applied to patients having various diseases such as myocardial infarction and arrhythmia, and has an advantage that versatility in a clinical setting is extremely high.

Further, the diagnostic apparatus of the present embodiment is characterized in that the process of the excitation propagation simulation, which requires a long calculation time as in the conventional method, is not included in the repetition. Of the operations included in the iterative processing of the analysis of the diagnostic device described above, the magnetic field calculation processing has the largest amount of calculation, but since this is a linear operation with many repetitions of the four arithmetic operations, it is vectorized and parallelized. The use of computerization, bi-line processing, and / or the use of a cache memory makes it possible to perform high-speed operations with a computer currently used. Therefore, the time required for analysis can be significantly reduced as compared with the conventional method.

Further, unlike the conventional method, since the configuration does not require detailed information on the excitation propagation velocity in the myocardium in advance during the estimation calculation, an incorrect excitation propagation velocity distribution is used for estimation. It is possible to avoid the instability of the estimation result due to this.

Second Embodiment A second embodiment of the present invention will be described with reference to FIG. The configuration of the diagnostic device for intracardiac electrical phenomena according to this embodiment is the same as or similar to that described above.

This embodiment describes another example of a method of expressing a heart shape.

FIG. 5 shows a ventricle model used in this embodiment. The difference between FIG. 3 of the first embodiment and FIG.
While the shape of the heart is expressed using a facepiece, the shape shown in FIG. 5 is that the shape of the ventricle is expressed using a pentahedron (triangular prism shape).

This comparison will be described in detail. In the first embodiment, since the heart is divided by a hexahedron, the size of the hexahedron at the base of the heart is smaller than the size of the hexahedron at the apex. Therefore, when the calculation amount is the same, the accuracy of the magnetic field calculation is inferior to the case where a hexahedron having a uniform size is used. However, the second embodiment can improve that point, and the entire heart can be divided into a uniform size by dividing the heart into pentahedra. Therefore, even in the case of the same calculation amount, high magnetic field calculation accuracy can be maintained.

More specifically, step S in FIG.
3. In the processing of S4, the ventricle model using the pentahedron is used, and a model that matches the shape of the ventricle model to the heart of each subject is set. Using the set pentahedron ventricle model, the current source distribution is calculated in the process of step S6 in FIG. 2 in the same manner as in the first embodiment.

Therefore, as described above, the pentahedron ventricle model can be compared with the hexahedron model with the same amount of calculation, and the accuracy of the magnetic field calculation, that is, the accuracy of the estimated analysis result can be further improved. This has the effect.

In FIG. 3 described above, the shape of the ventricle is expressed by a hexahedron, and in FIG. 5 described above, the shape of the ventricle is expressed by a pentahedron. The software according to step S4 may be configured. Further, the ventricle shape may be configured by appropriately combining these several types of polyhedrons.

Third Embodiment A third embodiment of the present invention will be described with reference to FIG.

In the present embodiment, the distribution of the arrival time of the excitation and the amplitude of the action potential are expressed not by using the values at all the apexes on the endocardium and epicardium but by a smaller number of parameters. With the goal. In the following description, the excitation arrival time is exemplified, but the same method can be applied to the action potential amplitude.

[0084]

[Outside 3]

(Equation 4) It is expressed as

[0085]

[Outside 4]

(Equation 5) It is represented as P ₁ and P ₂ are sub-matrices of P, and P ₁
Is N × M, and P ₂ is N × (NM). M is N
Less than. Using this matrix P ₁

(Equation 6)

[Outside 5]

(Equation 7) Can obtain the approximate value.

The diagnostic device for electrical phenomena in the heart according to the present embodiment is based on the principle described above, and its hardware configuration is the same as or equivalent to that of the first embodiment. In this configuration, the arithmetic processing unit 14a of the computer device 14 performs a series of processes shown in FIG.

In the process of FIG. 6, steps S5a and S5b are newly inserted between steps S5 and S6, and step S9 is replaced with step S9 in comparison with the process of FIG.
9a, and the positions on the return flow when the repetitive determination is made in step S11 are steps S5a and S5b.
It is between.

[0088]

[Outside 6] In step S5a, the arithmetic processor 14a sets initial values of the variables x and y. More specifically, the initial values of the variables x and y are set using Expression (7) based on the excitation arrival time and the initial value of the action potential amplitude set in step S5 described above.

Next, in step S5b, the excitation arrival time and the action potential amplitude are calculated. That is, the excitation arrival time and the action potential amplitude at each representative point in the ventricle are calculated from the variables x and y using the equation (6).

Next, through the processing of steps S6 and S7 (including the repetition processing of steps S5b, S6 and S7), the processing of changing the variables x and y of step S9a is performed. In this changing process, the variables x and x are changed in the same manner as in the changing process of the cardiac excitement arrival time distribution and the action potential amplitude distribution in the first embodiment.
y is changed.

Thereafter, the processes of steps S10 and S11 are similarly executed.

Therefore, according to the analysis method of this embodiment,
Since it is sufficient to estimate a smaller number of variables than in the first and second embodiments, the estimation is stable, and the estimation can be performed in a shorter time.

However, in the analysis method of the third embodiment, if an excessively small value is used for M, the degree of freedom of the arrival time distribution of the endocardium / adventitia excitation becomes small, so that the estimated endocardium / The spatial resolution of the membrane excitation arrival time distribution may decrease. Further, it may be difficult to match the magnetocardiogram calculated based on the estimated arrival times of the endocardium and epicardium or the measured electrocardiogram, and the estimation error may increase.

To avoid this, it is desirable to use a small value for M at the beginning of the iteration during the execution of the non-linear optimization algorithm, and increase M as the convergence progresses to approach N. At this time, an initial value of 0 may be given to a variable newly increasing as M is increased. With this configuration, it is possible to avoid the problem that the estimation error of the estimated value of the excitation arrival time distribution becomes large and the spatial resolution is reduced due to the small M.

Also, the number M of parameters of the excitation arrival time distribution
Different values may be used for the parameter numbers of the and the action potential amplitude distribution. Thus, for example, when the spatial resolution of the excitation arrival time distribution can be lower than the spatial resolution of the action potential amplitude distribution, the number of excitation arrival time distribution parameters can be reduced, and the stability of estimation can be further improved. it can.

In the processing of FIG. 6 described above, if processing limited to variables x and y is performed from the beginning, step S
5 can be omitted.

Fourth Embodiment A fourth embodiment of the present invention will be described with reference to FIGS. The diagnostic device for intracardiac electrical phenomena according to this embodiment includes:
It uses a ventricular model composed of a large number of voxels. The hardware configuration is the same as or equivalent to that of the first embodiment.

In the present embodiment, the distribution of the arrival time of the ventricle excitation is expressed as follows. The action potential amplitude distribution can also be expressed in the same manner, and a description thereof will be omitted.

First, each voxel is represented by the order i, j, k of the arrangement of each voxel in the x, y, z directions.
Let the numbers of voxels in the i, j, and k directions be N _i , N _j , and N _k . The excitement arrival time of voxel (i, j, k) is represented by t (i,
j, k). The three-dimensional discrete Fourier transform of t (i, j, k) is represented as T (l, m, n). T (l, m, n)
Is a complex number. Since t (i, j, k) is a real number, l,
The numbers of m and n are half of N _i , N _j and N _k , respectively.

Here, except for the small components of l, m, and n, T
Assume that (l, m, n) = 0. This is t (i,
This corresponds to assuming that high frequency components are not included in (j, k). The excitement arrival time of the voxel which does not correspond to the ventricle will also be expressed, but such a value will be ignored. By using this method, the excitation arrival time t (i, j, k) in the ventricle can be expressed by only small components of l, m, and n, that is, a small number of parameters.

FIG. 7 and FIG.
Of the voxel-shaped ventricle model and the analysis of the excitation arrival time distribution / action potential amplitude distribution using the expression method of the excitation arrival time distribution / action potential amplitude distribution peculiar to the present embodiment, executed by the arithmetic processor 14a of FIG. Is shown in a flowchart.

The arithmetic processing unit 14a performs the processing of steps S21 to S33 shown in FIG. 7 in order to perform such an analysis. Among them, the processing of steps S21 to S23 is executed in the same manner as the processing of steps S1 to S3 in FIG. Thereafter, when the process proceeds to step S24, a voxel-shaped ventricular model is formed. FIG. 8 shows a more detailed processing flow relating to this configuration.

The processing in FIG. 8 will be described. Arithmetic processing unit 14a
Here, the processing performed here is positioning processing (step S24-1) and image processing (step S24-
2) tissue type classification processing (step S24-3), voxel re-creation processing (step S24-4), and conductivity distribution setting processing (step S24-5).

First, the positioning process (step S24-
In 1), the positional relationship between the various three-dimensional images of the chest measured by MRI and X-CT and the coordinate system of each channel of the SQUID sensor is obtained. For positioning between the three-dimensional images, it is a preferable example that a plurality of markers are printed on various images in advance, and the respective markers are physically located at the same position to obtain a positional relationship. SQUI
A magnetic field is also generated from the same position as the above marker in the alignment between the D sensor and each image, the position of the marker is obtained from the measured value, and the positional relationship between the two is determined assuming that it is at the same position as the marker on the three-dimensional image. .

As another alignment method between three-dimensional images,
Positioning may be performed so that the epidermis surface of the chest of the subject captured in each image is made to coincide. In this case, the SQUI
The alignment with the D sensor is performed by measuring the position of the magnetic field generator attached on the chest using a SQUID magnetometer, and aligning the positions with the skin surface of the chest of the three-dimensional image soil. Should be performed.

In the image processing (step S24-2), preprocessing is performed to facilitate subsequent tissue type classification processing. For example, image gradation reduction, noise removal, smoothing, image size conversion, differentiation processing, edge detection, and the like are performed.

An organization type database TDB is provided. This database is MRI T1, T2
The relationship between the signal intensity of each voxel of the enhanced image and the X-CT image and the tissue type is described. In this description, ventricular muscles, atrial muscles, blood vessels, valves, and other muscle tissues showing similar signal intensities are classified into the same category (muscle, etc.).

Further, the organization type classification processing (step S2)
According to 4-3), the tissue type is classified into each category for each voxel by referring to the tissue type database from the signal intensities of the various images subjected to the signal processing. Based on the category images obtained in this way, using known information such as the shape and position of each tissue such as the ventricle, each voxel can be converted into, for example, a ventricular muscle, an atrial muscle, a heart valve, or a muscle other than the heart. Classified into tissue, blood, lung, fat, skin, air, and open air. In particular, ventricular muscle, atrial muscle, heart valves,
The classification between muscle tissues other than the heart is performed using the thickness of the muscle tissue and the distance from outside air.

The voxel re-creating process (step S24)
In -4), the unnecessary air region, the neck, and the lower abdomen are removed, and the image size is converted so that only the chest fits in the image. The voxel size conversion is performed. At this time, instead of processing all the voxels to the same size, the processing may be performed so that the size becomes an integral multiple of the voxel of a predetermined minimum unit size.

Finally, the conductivity distribution setting process (Step S)
In 24-5), the conductivity of each voxel is set by referring to the conductivity database CDB in which representative values of the conductivity of the classified tissue type are described.

Next, initial value setting processing of the excitation arrival time distribution / action potential amplitude distribution is performed (step S25).
In this processing, specifically, a small l, 1 of a three-dimensional discrete Fourier transform of a typical excitation arrival time distribution of a normal heart is used.
T (l, m, n), in which values other than m and n are set to 0, is a variable T
Set as the initial value of. Or T (l, m, n)
May be set at random, and the other components may be set to 0 and set as the initial value of the variable T. The same initial setting process is performed on the action potential amplitude distribution.

Next, a calculation process of the excitation arrival time distribution and the action potential amplitude distribution is performed (step S26). Specifically, T (l, m, n) is subjected to a three-dimensional discrete inverse Fourier transform, and the distribution of the excitation arrival times is obtained. Similar calculation processing is performed on the action potential amplitude distribution.

[0113]

[Outside 7]

(Equation 8) Ask like. This allows

(Equation 9) It is expressed as Where σ (i, j, k) is the intracellular conductivity at voxel (i, j, k), Ax (i, j, k)
k), Αy (i, j, k) and Αz (i, j, k) are the cross-sectional area of the voxel in a plane perpendicular to the i, j, k directions of the voxel (i, j, k), Φ (I, j, k) is the action potential amplitude at voxel (i, j, k).

Next, a magnetic field calculation process is executed (step S28). In the magnetic field calculation process, as in the first embodiment, the magnitude of the magnetic field measured by the SQUID sensor is calculated using Biot-Savart's law, Servas's equation, and the like. The magnetic field may be calculated using a difference method using the conductivity distribution of the tissue set in the heart shape measurement / setting process (step S24). The difference method is a method of solving a partial differential equation using grid-like representative points, and has a feature that the algorithm is simpler than the boundary element method or the finite element method.

The processes of steps S26 to S28 are repeatedly executed a required number of times (step S29).

Next, the process of changing the excitation arrival time distribution / action potential amplitude distribution and the error calculation process are repeatedly executed a required number of times basically in the same manner as in the first embodiment (steps S30 to S32). ). Further, the display processing of the analysis result of the excitation arrival time distribution and the action potential amplitude distribution is executed basically in the same manner as in the first embodiment (step S33).

In the conventional method, in order to improve the estimation accuracy, it is necessary to represent the actual shape of the heart, lung, torso, etc. For this purpose, the boundary element method or the finite element method has been used. For example, in the boundary element method, the surface of the heart, lungs, or body surface is formed by a combination of triangles. In order to create such a model, it is necessary to extract a tissue boundary in a three-dimensional image from MRI and X-C− and create a triangular mesh approximating the boundary surface. However, in practice, it is difficult to perform all of this automatically, and it is practically impossible to create a triangular mesh model of a tissue boundary tailored to each patient for all patients to be examined. It was possible. In the case of the finite element method, the three-dimensional area is divided by a tetrahedral or hexahedral mesh, but it is necessary to match the boundaries of the tetrahedron and hexahedron with the boundaries of the tissue. there were.

On the other hand, in the present embodiment, in order to express the shape or conductivity distribution of the heart or the chest, it is necessary to create a model in which a triangular mesh, a tetrahedral mesh, a hexahedral mesh, or the like is matched with the tissue boundary. Instead, it is only necessary to classify the tissue of each hexahedron regularly arranged in a lattice. For this reason, a ventricle and a torso model can be easily created from three-dimensional images of MRΙ and X-CT, so that a ventricle model tailored to each patient can be easily created for all patients to be examined, and the ventricle model can be created. It can be used to simultaneously analyze the time of arrival distribution and the amplitude of action potential amplitude. In addition, since it is possible to easily create a conductivity distribution model for each patient and use it for potential calculation and magnetic field calculation, it is possible to analyze the distribution of the arrival time of the excitation and the action potential amplitude with higher accuracy than the conventional method. Can be. In particular, the analysis of this embodiment requires less time and effort for analysis than those of the above-described embodiments, so that it can be easily performed with a large number of patients and has good throughput.
It is possible to analyze the distribution of the excitation arrival time and the action potential amplitude, and a diagnostic device having high value in practicality is provided.

Other Embodiments In each of the above-described embodiments, a method of measuring the distribution of the time of excitation at the heart from the measured value of the magnetic field generated from the heart has been specifically described. Excitation arrival time in the heart can be estimated from the electrocardiogram data obtained in step (not shown) according to a similar method.

Also, the analysis target in each of the above-described embodiments has been described as an example of the distribution of the excitation arrival time and the action potential amplitude. (The action potential amplitude was adopted as one mode of the myocardial electrical activity.) ), And various modifications are also possible. For example, instead of the action potential amplitude,
Estimate the distribution of electrical conductivity in the ventricle, the distribution in the ventricle of the absolute value of the current dipole density, which is the product of the action potential amplitude and the electrical conductivity, or the amount related or deformed to those amounts. It may be. The implementation method and the effect in that case are the same as those when the excitation arrival time distribution and the action potential amplitude distribution are analyzed.

[0121]

As described above, according to the present invention, the distribution information of the excitation arrival time in the heart of the subject and the distribution information of the myocardial electric activity amount representing the magnitude of the electric activity of the myocardium of the heart are simultaneously obtained. Estimation and display of the distribution information of the arrival time of excitement and the distribution information of myocardial electrical activity, so that it is not necessary to perform the excitement propagation simulation that requires a huge amount of calculation time unlike the conventional method. It is possible to estimate the electrophysiological activity in the heart remarkably faster, more accurately and more stably than the conventional method, and it can be easily used for diagnosis of an unspecified number of patients such as patients having a disease such as myocardial infarction. It is possible to provide a diagnostic device and a diagnostic method for intracardiac electrical phenomena which are very practical and versatile.

Further, according to another aspect of the present invention, it is possible to easily and automatically extract the ventricular shape and the thoracic conductivity distribution of each patient, so that many patients can be easily extracted. Diagnosis can be made more practically.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a schematic configuration of a diagnostic apparatus for an intracardiac electrical phenomenon according to an embodiment of the present invention.

FIG. 2 is a schematic flowchart showing an estimation analysis of distribution information of an excitation arrival time and an action potential amplitude executed in the first embodiment.

FIG. 3 is a diagram showing an example of a ventricle model (ventricular shape).

FIG. 4 is a diagram showing an example of a display method of an excitation arrival time and an action potential amplitude distribution.

FIG. 5 is a diagram showing another example of a ventricle model (ventricular shape) according to the second embodiment.

FIG. 6 is a schematic flowchart showing an estimation analysis of distribution information of an excitation arrival time and an action potential amplitude executed in a third embodiment.

FIG. 7 is a schematic flowchart showing an estimation analysis of distribution information of an excitation arrival time and an action potential amplitude executed in a fourth embodiment.

FIG. 8 is a flowchart illustrating an outline of a heart model setting process according to a fourth embodiment.

FIG. 9 is a diagram functionally illustrating an estimation analysis part in the processing of FIG. 2 according to the first embodiment.

FIG. 10 is an explanatory diagram showing a flow of calculation in an example of a conventional estimation method.

FIG. 11 is a diagram showing another example of display of an excitation arrival time distribution and an action potential amplitude distribution which are analysis results.

FIG. 12 is a diagram showing still another example of the display of the excitation arrival time distribution and the action potential amplitude distribution which are the analysis results.

FIG. 13 is a view showing still another example of the display of the excitation arrival time distribution and the action potential amplitude distribution which are the analysis results.

FIG. 14 is a diagram showing still another example of the display of the excitation arrival time distribution and the action potential amplitude distribution which are the analysis results.

[Explanation of symbols]

 11 SQUID magnetometer 12 Image collection device 13 Image transfer device 14 Computer device 14a Arithmetic processor 14b Memory 14c Input device 14d Display

Claims (32)

[Claims] 1. An intracardiac electrical phenomenon diagnostic apparatus for estimating and displaying an electrical phenomenon in a heart of a subject based on at least one of a potential and a magnetic field measured from the subject. Distribution information estimating means for simultaneously estimating the distribution information of the myocardial electrical activity of the myocardium of the heart, and the distribution information of the myocardial electrical activity of the myocardium of the myocardium. A diagnostic device for intracardiac electrical phenomena, comprising: 2. The diagnostic apparatus according to claim 1, further comprising a measuring unit configured to measure at least one of a potential and a magnetic field from a measurement point on a body surface of the subject. Phenomenon diagnostic device. 3. The diagnostic device for intracardiac electrical phenomena according to claim 2, wherein the distribution information estimating means divides the distribution of the excitation arrival time and the distribution of the myocardial electric activity in the heart into a finite number. A time waveform at each measurement point of at least one of the potential and the magnetic field based on the distribution of the excitation arrival time and the distribution of the myocardial electrical activity expressed by the parameters. Waveform calculating means for calculating, and estimating the plurality of parameters using a non-linear optimization method such that at least one of the waveform of the measured potential and the magnetic field matches the time waveform of at least one of the calculated potential and the magnetic field A diagnostic device for intracardiac electrical phenomena, comprising: parameter estimation means. 4. The apparatus for diagnosing an intracardiac electrical phenomenon according to claim 3, wherein the distribution information estimating means includes a heart model setting means for setting the heart model from three-dimensional image data of the inside of the subject. A diagnostic device for intracardiac electrical phenomena, characterized in that: 5. The diagnostic apparatus for electrical phenomena in the heart according to claim 4, wherein the expression means represents the distribution of the excitation arrival time and the distribution of the myocardial electrical activity in the heart as a representative of the heart model. The distribution information estimating unit includes a calculating unit that calculates the distribution of the excitation arrival time and the distribution of the myocardial electric activity based on the small number of parameters. A diagnostic device for intracardiac electrical phenomena characterized by the following. 6. The diagnostic device for intracardiac electrical phenomena according to claim 4, wherein the expression means represents the excitation arrival time at each representative point of the heart model and the myocardial electrical activity in the heart. Means for expressing the obtained distribution of the excitation arrival time and the distribution of the amount of myocardial electric activity by a linear function of a smaller number of parameters than the representative points of the heart model. An apparatus for diagnosing intracardiac electrical phenomena, comprising: calculation means for calculating the distribution of the time of arrival of excitation and the distribution of the amount of myocardial electrical activity based on a small number of parameters represented by functions. 7. The diagnostic apparatus for an intracardiac electrical phenomenon according to claim 6, wherein the linear function is a function based on Fourier transform or inverse Fourier transform. 8. The diagnostic device for an intracardiac electrical phenomenon according to claim 7, wherein the linear function is a function based on a matrix operation. 9. The diagnostic apparatus according to claim 8, wherein the matrix of the matrix operation is a matrix obtained from a covariance matrix of measured or virtual excitation arrival times. Diagnosis device for internal electrical phenomena. 10. The diagnostic apparatus for electrical phenomena in a heart according to claim 4, wherein the heart model setting means is means for setting a shape of a ventricle as a combination of a plurality of polyhedrons as the heart model. Diagnostic device for electrical phenomena in the heart. 11. The diagnostic apparatus according to claim 10, wherein the polyhedron is a hexahedron, a pentahedron, or a tetrahedron, or a combination of at least two of these polyhedrons. A diagnostic device for electrical phenomena in the heart. 12. The apparatus according to claim 1, further comprising: image transfer means for inputting and transferring three-dimensional image data of the inside of the subject obtained from a medical modality, wherein the distribution information estimation is performed. The means for diagnosing an intracardiac electrical phenomenon comprises a heart model setting means for setting a model of the subject's heart from the three-dimensional image data transferred from the image transfer means. 13. The apparatus for diagnosing an intracardiac electrical phenomenon according to claim 4 or 11, wherein the heart model setting means moves, rotates and / or deforms a ventricle model having a predetermined shape, and performs nonlinear optimization. A diagnostic device for an intracardiac electrical phenomenon, which is means for setting the position, orientation, and shape of the ventricle of the ventricle model of the subject by fitting the surface of the ventricle based on the three-dimensional image data by a conversion method. 14. The apparatus for diagnosing an intracardiac electrical phenomenon according to claim 13, wherein the heart model setting means includes means for interactively moving, rotating, and / or deforming the ventricle model having the predetermined shape. A diagnostic device for intracardiac electrical phenomena, characterized in that: 15. The apparatus for diagnosing an intracardiac electrical phenomenon according to claim 13 or 14, wherein the heart model setting means includes means for superimposing and displaying the three-dimensional image data and the ventricle model of the subject. A diagnostic device for intracardiac electrical phenomena, characterized in that: 16. The apparatus for diagnosing an intracardiac electrical phenomenon according to claim 4 or 12, wherein the heart model setting means arranges units forming a plurality of types of predetermined shape images of a ventricle shape as the heart model. A diagnostic device for intracardiac electrical phenomena, characterized by comprising means for expressing. 17. The diagnostic apparatus for an intracardiac electrical phenomenon according to claim 16, wherein the predetermined shape is a hexahedron. 18. The apparatus for diagnosing an intracardiac electrical phenomenon according to claim 1, wherein the distribution information estimating means is configured to detect a living body from one or more kinds of three-dimensional image data obtained from one or more kinds of medical modalities. A biological model creation unit for creating a model, wherein the biological model creation unit includes a tissue type classification unit for classifying a tissue type of each region of a living body in a three-dimensional image region from the three-dimensional image data. Diagnostic device for electrical phenomena in the heart. 19. The apparatus for diagnosing intracardiac electrical phenomena according to claim 18, wherein the biological model creating means sets the electrical conductivity for each of the regions based on the classification result of the tissue type sorting means. A diagnostic device for intracardiac electrical phenomena, comprising: 20. The apparatus for diagnosing an intracardiac electrical phenomenon according to claim 19, wherein the living body model creating means comprises voxel constituting means for constituting a living body in the three-dimensional image region by a large number of voxels, and the distribution The information estimating means includes a calculating means for calculating at least one of the potential waveforms and the magnetic field waveforms of the plurality of voxels using a difference method. 21. The diagnostic device for intracardiac electrical phenomena according to claim 1, wherein the display unit displays the distribution information of the attainment time of the excitation at the same time as an isochronous line, a shade of color, a difference in hue, or an element thereof. A diagnostic device for an intracardiac electrical phenomenon, characterized in that it is means for displaying by a combination of at least two of the following. 22. The apparatus for diagnosing intracardiac electrical phenomena according to claim 1, wherein the display means displays the distribution information of the myocardial electrical activity in terms of contour lines, shades of color, or differences in hues, or elements of those elements. An apparatus for diagnosing intracardiac electrical phenomena, which is means for displaying by a combination of at least two. 23. The apparatus for diagnosing intracardiac electrical phenomena according to claim 1, wherein the display means displays the distribution information of the time of arrival of the excitation in an isochronous line, and displays the distribution information of the myocardial electrical activity. A diagnostic device for intracardiac electrical phenomena, characterized in that it is means for displaying a color shade or a difference in hue. 24. The apparatus for diagnosing intracardiac electrical phenomena according to claim 23, wherein the display means is a means for superimposing and displaying the distribution information of the excitation arrival time and the distribution information of the myocardial electrical activity. A diagnostic device for intracardiac electrical phenomena characterized by the following. 25. The diagnostic apparatus for electrical phenomena in the heart according to claim 1, wherein the display means displays distribution information of the excitation arrival time and distribution of the myocardial electrical activity on a developed view of the heart surface of the heart. An apparatus for diagnosing intracardiac electrical phenomena, which is a means for displaying information or both distribution information. 26. The apparatus for diagnosing intracardiac electrical phenomena according to claim 1, wherein the display means displays distribution information of the time of arrival of excitation and the amount of myocardial electrical activity on one or more sectional views of the heart. A diagnostic device for intracardiac electrical phenomena, characterized in that the diagnostic device is a means for displaying distribution information of both or both. 27. The diagnostic apparatus for an intracardiac electrical phenomenon according to claim 1, wherein the display means displays distribution information of the excitation arrival time on a perspective projection view when the heart is viewed from an arbitrary direction. An apparatus for diagnosing intracardiac electrical phenomena, which is means for displaying distribution information of myocardial electrical activity or distribution information of both. 28. The diagnostic apparatus for an intracardiac electrical phenomenon according to claim 1, wherein the display means displays the excitation arrival time on one or both of a projection view of the heart in an arbitrary direction and a development view of the heart surface. An apparatus for diagnosing intracardiac electrical phenomena, which is means for displaying distribution information, distribution information of the amount of myocardial electrical activity, or both. 29. The apparatus for diagnosing an intracardiac electrical phenomenon according to claim 2, wherein the measuring means is one or both of a multi-channel SQUID magnetometer for measuring the magnetic field and an electrocardiograph for measuring the electric potential. A diagnostic device for intracardiac electrical phenomena, characterized in that: 30. The diagnostic apparatus for an intracardiac electrical phenomenon according to claim 1, wherein the myocardial electrical activity is an amplitude value of an action potential in a rising phase of the action potential of the myocardium. A distribution of the electrical conductivity of the myocardium, an absolute value of a current dipole density which is a product of an amplitude value of the action potential and a distribution of the electrical conductivity, or an amount related to the amount thereof. Diagnostic device. 31. A method for diagnosing an intracardiac electrical phenomenon which estimates and displays an electrical phenomenon in the heart of a subject based on at least one of a potential and a magnetic field measured from the subject, wherein the distribution information of the time of arrival of excitation in the heart is provided. Estimating the distribution information of the myocardial electrical activity amount representing the magnitude of the electrical activity of the myocardium of the heart, and displaying the distribution information of the excitation arrival time and the distribution information of the myocardial electrical activity amount. A method for diagnosing intracardiac electrical phenomena, comprising: 32. The method for diagnosing electrical phenomena in a heart according to claim 31, wherein the step of simultaneously estimating the distribution information sets the heart model from three-dimensional image data in the body of the subject. The distribution of the excitation arrival time and the distribution of the myocardial electrical activity in the above are expressed by a finite number of parameters, and the distribution of the excitation arrival time and the distribution of the myocardial electrical activity expressed by these parameters are Calculating a time waveform at each measurement point of at least one of the electric potential and the magnetic field based on the waveform, and at least one of the waveform of the electric potential and the magnetic field measured from the subject matches the time waveform of at least one of the calculated electric potential and the magnetic field Estimating the plurality of parameters using a non-linear optimization method.