Patent Description:
Echocardiography is widely used as a method of imaging the heart. It uses a series of rapidly acquired pulse-echo ultrasound images to build up, for example, a real time video image of the heart. The images are typically two dimensional (2D) and the images are typically analysed visually by a skilled clinician, although computer analysis of the images is known, for example from <CIT> and "<NPL>. In stress echocardiography, the heart is imaged in a rest condition, i.e. when the subject is at rest, and under a stress condition, for example after exercise. The function of the heart in the two conditions can be compared to provide information on how it responds to stress. If it is not appropriate for the subject to be exercised, then stress can be induced or simulated, for example by injecting a stimulant such as dobutamine into the subject. Dobutamine stress echo (DSE) is widely used in diagnosing coronary artery disease (CAD).

The present invention provides a system for measuring deformation of the heart as defined in claim <NUM>.

The processing means may be arranged to compare the value of at least one parameter with reference data to generate a diagnostic output.

The at least one parameter may include any one or more of: a displacement in at least one direction of a part of the heart; a mean, for all of said parts of the heart, of the displacements in at least one direction; the mean, for all of said parts of the heart, of that sum of displacements; and the principle transformation which is described in more detail below.

The locating means may comprise a user input device arranged to enable a user to locate said pairs of points in the images and to record the positions of said pairs of points, for example by recording the coordinates of each of the points in a two dimensional coordinate system.

The imaging system may be arranged to store the images as respective image data sets, and the locating means may be arranged to process the image data sets to determine the locations of said pairs of points and to record the positions of said pairs of points.

The at least one parameter may comprise a plurality of parameters and the processing means may be arranged to compare the value of each of the parameters with a respective reference value.

The system may be arranged to acquire a further set of two images of the heart at respective points in a cardiac cycle, with heart in a second condition, which is different from its condition when the first set of two images are acquired. For example one of the conditions may be a rest condition when the subject is at rest and one of the conditions may be a stress condition when the subject is under stress. Each of the parameters may be determined once for each set of images. One or more further parameters may be defined which combine data from the two sets of images. For example the difference in the value of one of the parameters, between the two sets of images, may be used as a further parameter.

The processing means may be arranged to define a decision tree for generating the diagnostic output from the values of the parameters. The decision tree may include a plurality of decision points. Each decision point may define a reference value of one of the parameters. For example one of the decision points may define a reference value of the principal transformation, and/or one of the decision points may define a value of the shear transformation as described in more detail below, and/or one of the decision points may define a reference value of the difference between the principal transformation in the two different conditions of the heart. Systems for building decision trees from training data are well known, such as C4. <NUM> and J48.

The invention further provides a method of measuring deformation of the heart as defined in claim <NUM>.

The invention further provides a method of producing a system for diagnosing a heart condition, the method including analysing a set of images, wherein each of the images has a diagnostic outcome associated with it, the method including calculating a value of the at least one parameter for each of the images, analysing the values and the diagnostic outcomes to determine a relationship or correlation between the two.

The method may further comprise using machine learning to develop a decision tree for generating the diagnostic output from the values of the parameters. The method may be performed on a computer system or processor system, which may form part of an imaging system, or may comprise a separate computer.

The diagnostic output may relate to a variety of cardiac conditions, such as coronary artery disease (CAD), or mitral regurgitation, or hypertrophic cardiomyopathy.

The imaging system may comprise an echocardiography system, or it may be an X-ray imaging system such as an X-ray computer tomography (CT) scanner, magnetic resonance imaging (MRI) scanner, or a positron emission tomography (PET) scanner.

The system or method may further comprise, in any workable combination, any one or more features or steps of the preferred embodiments of the invention, as will now be described with reference to the accompanying drawings.

Referring to <FIG>, an echocardiography system <NUM> comprises a transducer array <NUM> arranged to be located close to the body of the patient <NUM>, typically as close to the heart as possible, a processing unit <NUM> which includes a processor <NUM> which may be a digital electronic processor, a memory <NUM> such as a hard disk, and a display <NUM>, such as a flat screen monitor or LED display. The system may further include a user input device, for example a touchscreen <NUM> integrated into the display <NUM>, which provides a user input allowing a user to provide inputs to the system <NUM>. Other user inputs such as a mouse, touchpad or keyboard may of course be used. The processor unit <NUM> is connected to the transducer array <NUM> and is arranged to control the transducer array as a phased array so as to emit an ultrasound beam which scans across the patient in a series of pulses, and detect reflected ultrasound from the heart from each pulse. One scan of the heart builds up a single image, and the scan is repeated at typically <NUM> to <NUM> images per second to build up a real time video image of the heart showing its movement during the cardiac cycle. Each image may be stored in the memory <NUM> as an image data set which may comprise, for example, intensity values for each of the pixels of which the image is made up.

While the system is described in general terms above, suitable echocardiography systems include, for example the Philips Epic iE33, GE vivid e9, or portable systems such as the Philips CX50, or hand held systems.

The process of echocardiography is well known and will not be described in detail. There are several different imaging methods, but two dimensional imaging may be used. It is known to provide images on several different planes through the heart, which show different aspects of the four main chambers of the heart, the left ventricle (LV), right ventricle (RV), left atrium (LA) and right atrium (RA). Such views include, for example, an apical four chamber view, apical two or three chamber views and parasternal long and short axis views. In each case, while a single still image can be obtained, typically a series of views is acquired over the cycle of the heart so that its movement can be recorded and analysed.

Referring to <FIG>, if the images are four chamber apical views, they show a 2D plane of the heart <NUM> showing the left ventricle <NUM>, the right ventricle <NUM>, the left atrium <NUM> and the right atrium <NUM>. The plane includes the long axis <NUM> of the LV, also extends through the apex <NUM> of the LV, the lateral wall <NUM> of the LV and the septum <NUM>.

Referring to <FIG>, the echocardiography system <NUM> may be arranged to acquire at step <NUM> a sequence of 2D images and store them in its memory <NUM>. The images may be acquired over a single cardiac cycle, and may include for example between ten and <NUM> images covering one cycle. The acquisition of the images can of course be carried out on a conventional echocardiography system <NUM>. The following analysis of the images can be carried out using the same processing unit <NUM> that forms part of the echocardiography system as shown in <FIG>. However the images may be downloaded onto a computer, such as a laptop or PC, which has a processor, memory, user input and display, which operate for this purpose in the same way as those of the control unit <NUM>, and the further analysis of the images may be carried out on that computer under the control of dedicated software.

At step <NUM>, the images closest to end systole, i.e. maximum contraction, and end diastole, i.e. maximum volume of the LV, may be identified. This can be done by a user viewing all of the images on the display <NUM> and selecting one of them as the closest to end systole and one of them as the closest to end diastole using the user input device <NUM>. This selection may be made by the user on the basis of an assessment and comparison of the volume of the LV in each of the images as judged by eye, or by noting the points of opening and closing of the mitral valve, or using the QRS complex on an ECG plot, or by any combination of these. This is reasonably easy for a practiced clinician to do. Alternatively the processor <NUM> may be arranged to use image processing techniques to identify, and measure the volume of, the LV in each of the images, compare the volumes of the LV in the different images, and identify the image with the smallest LV volume as the end systole image and the image with the largest LV volume as the end diastole image. In either case, once the end systole and end diastole images have been identified, they may be identified in the memory <NUM>, for example being marked with an appropriate flag, so that they can be selected and viewed by a user.

Referring to <FIG>, once the end systole and end diastole images have been identified, corresponding points <NUM> on the wall of the LV at end systole 400a and at end diastole 400b may be identified at step <NUM>. A Cartesian coordinate system may also be defined, for example having a vertical axis (referred to as the y axis herein) through the apex <NUM> of the LV and extending along its longitudinal axis, and a horizontal axis (referred to as the x axis herein) through the mid-point of the LV half way between its apex <NUM> and its base <NUM>. The apex <NUM> and the base <NUM> may be identified by the user via the user input, or by image processing. The coordinate of each of the points 400a, 400b on the coordinate system may then be determined and recorded. Since the scale of the image is known from the echocardiography system, the coordinates of each of the points define the position of the point in the plane of the image, and therefore the distance between the two points in each pair indicates the distance moved by the relevant part of the heart between end systole and end diastole. Again, the identification of the points may be done manually by a user selecting each of the points on each of the images using the user input <NUM>, or it may be done by image processing software running on the system <NUM> and arranged to analyse the shapes of the LV in each of the end systole and end diastole images and identify specific points. These may include for each of the two images, for example, a point at the apex 402a, 402b of the LV at both end systole and end diastole, a point 404a, 406a, at one side of the base of the LV, and a point at the other side of the base of the LV, two points at the midpoint of the LV, two points at the start of the apex, and various intermediate points spaced between. Some of these points are described in more detail below with reference to <FIG>.

Referring to <FIG>, which shows an echo image acquired with a contrast agent, in each of the images of the LV, the apex <NUM> of the LV can be located as the extreme end of the LV, and the base of the LV on each side <NUM>, <NUM> can be located from the shape of the side walls. The y axis can then be defined as the line passing through the apex <NUM> and the midpoint between the two sides of the base <NUM>, <NUM>. The x axis can then be defined as the line perpendicular to the y axis half way between the apex and the midpoint between the two sides of the base. The mid-point on each side <NUM>, <NUM> can be identified as the point where the x axis intersects the side wall on that side. The lower end of the apex on each side <NUM>, <NUM> can also be identified where the sidewalls start to taper towards the apex <NUM>. As mentioned above, each of these points may be identified by a user. Alternatively image processing may be used to identify them. If image processing is used, the outline of the LV is first identified as the boundary between the lighter area within the LV and the darker area of the myocardium forming the walls around it (or vice versa for images acquired without use of a contrast agent). This boundary is not sharp, but algorithms for identifying such boundaries are well known. Once the boundary has been identified, the algorithm may then be arranged to identify the highest point (maximum y value) of the boundary as being the apex <NUM>, and the points where the boundary changes direction at the lower end, for example as can be seen at the point <NUM> on the right hand side of the base line in <FIG>. Again algorithms for analysing the radius and direction of curvature, and how that changes around the boundary, can be used to identify these points, and the points <NUM>, <NUM> at the lower end of the apex.

Referring to <FIG>, further points on the walls of the LV can be identified, either manually or by the processor using simple algorithms. For example these might be points on the side walls equally spaced in the y direction between the points indicated in <FIG>.

Referring back to <FIG>, once all of these points have been identified, their x and y coordinates in the Cartesian coordinate system may be stored in the memory <NUM>, for example as an end systole coordinate set including the coordinates of the points on the end systole image and an end diastole coordinate set including the coordinates of the points on the end diastole image. The processor may be arranged at step <NUM> to calculate, from the two coordinate sets, the transformation in geometry of the LV between end systole and end diastole. For example the processor may be arranged to use the coordinate sets to calculate movement in one or more directions as will now be described in more detail.

Referring back to <FIG>, the processor <NUM> is arranged to calculate, for the deformation of the shape of the LV between end systole and end diastole, values for various parameters that quantify the movement of the LV between end systole and end diastole.

The calculation may include working out how far each point has moved in each of the x and y directions, by working out the change in position (End diastole - End systole) along both the x axis and the y axis. This gives a set of x axis movements Δx, with one value for each corresponding pair of points, as shown in <FIG> for the points 404a, 404b, and a set of y axis movements Δy, again with one value for each corresponding pair of points. Each of these values may be a simple distance with no indication of direction. The mean change of all the points in both the x axis (ΔX) and y axis (ΔY) may then be calculated separately so as to provide an average Δx value or x direction movement ΔX, and an average Δy value or y direction movement ΔY for the entire ventricle. If each of the individual movement values are purely distance, without any indication of whether they are in the positive or negative x or y direction, then these averages will describe the total amount of movement, but not give an indication of the direction or of whether different parts of the LV wall are moving in the same direction or opposite directions.

Another parameter that maybe calculated is, for each point on the LV, i.e. each pair of points on the images, to calculate the mean of the x and y direction movements Δx and Δy, where the mean value for each point Δxy=(Δx+Δy)/<NUM>. The mean of all the values of Δxy for all points can then be calculated to a value for the entire ventricle ΔXY. This calculation is similar to the calculation of shear strain and is therefore referred to herein as the shear transformation. It will be appreciated that, for a given distance of movement, this parameter will be largest for movements at <NUM> degrees to both of the x and y axes, and smallest for movements along one of the axes.

A further parameter that can be calculated is similar to the principal transformation that can be calculated from x and y strain components, and is therefore referred to herein as the principal transformation, given by <MAT> where C1 and C2 are constants. For example C1 may be ½ and C2 may be <NUM>. These values were used in the examples described below.

This transformation is closely related to the shear transformation and therefore tends to vary in a similar way to that parameter, but has a negative value indicating contraction of the heart. However, as indicated by the test results below, the principal transformation value can give a more reliable diagnosis in some cases, in particular of CAD.

It will be appreciated that each of these parameters relates to changes between end systole and end diastole in a single coronary cycle. However in stress echocardiography, (or corresponding tests carried out with other imaging methods) there will be one value for each parameter for the heart at rest and one value for the heart at stress. Comparing those values, for example determining the difference between them, gives further information about heart function that can be used in diagnosis.

Once the x and y movements, and shear and principal transformation values have been calculated, the processor is then arranged at step <NUM> to compare these with reference values stored in the memory <NUM> to make a diagnosis of one or more specific heart conditions, and to generate a diagnostic output. The output may be a simple binary output indicating a positive or negative diagnosis. The processor unit <NUM> may be arranged to display the output on the display <NUM>. Alternatively, or in addition, it may be arranged to store the output as data in association with the images on which it was based, for example by adding output data, indicative of the diagnosis, to a file in which the images are stored.

The reference values may for example be determined by analysis of images of hearts some of which do and some of which do not have the specific heart conditions to determine for example threshold values which are indicative of a specific condition.

The reference values can be determined by means of a learning algorithm which, for example, can be run on the processor unit <NUM>, and which uses a database of stress echo images with associated diagnoses as determined by conventional methods, which may be stored in the memory <NUM>. Specifically the database may include a large number of sets of images, each set comprising an end systole image and an end diastole image for both rest condition and stress condition, together with, for each set of images, an associated diagnosis, such as a positive or negative diagnosis for CAD. The learning algorithm may be arranged to analyse the images to calculate values of the various parameters described above, and then to determine the correlation between the diagnosis and the values of each of the various parameters.

Analysis was carried out on sample images from <NUM> subjects. All results generated were from an apical <NUM> chamber view. Firstly the values were compared for positive and negative outcomes as determined from the DSE results. Then the comparison was repeated with the DSE results corrected for confirmed false positives in the DSE results.

From the values of the various parameters obtained from the sample data, machine learning may be used to determine the accuracy of each parameter as an indicator of adjusted DSE outcome. Using the data above, a J48 pruned decision tree with <NUM> fold cross validation method was used to classify the data. The accuracy of each parameter as an indicator of diagnostic outcome is summarized in the tables below, in which the following abbreviations used are:.

Then from all of the variables, using machine learning, a decision tree which is shown in <FIG> was derived to provide accurate diagnosis from the data. The decision tree defines a series of decision points, each of which defines a reference or threshold value of a parameter. The decision tree outlines a simple algorithm which operates as follows. Firstly the principal transformation of the LV as described above is determined for the stress condition of the heart. If the transformation is less than - <NUM> (i.e. a negative value with magnitude greater than <NUM>) then the diagnosis is negative. If the value is greater than -<NUM> then difference in principal transformation between rest and stress conditions is greater than <NUM> then the diagnosis is negative, but if it is less than that distance, the diagnosis is positive. It will be appreciated that the structure of the decision tree, and the reference or threshold values at each decision point in the decision tree, will depend on the diagnosis that is to be made.

The decision tree was then used on the sample data to test its accuracy and the outcome is given below.

To test whether a <NUM> chamber view could be used instead of a <NUM> chamber view in a similar diagnostic system, two chamber views corresponding to each of the four chamber views in the sample data were analysed in the same way to derive the same parameters of principal transformation, shear transformation, and radial (X) and longitudinal (Y) movements.

A very significant result is the similarity in principal transformation values between the <NUM> and <NUM> chamber view. This implies that not only is the principal vector a sensitive parameter for detecting disease, it also implies that it gives a 3D functional assessment from a 2D view.

Table <NUM> illustrates that the principal transformation is reduced in other disease cohorts (hypertrophic cardiomyopathy (HCM) and mitral regurgitation) implying it is also sensitive at detecting hypertrophy, cardiomyopathies and valve disorders. Notice that the principal vector was reduced in both the <NUM> chamber and the <NUM> chamber views indicating that it is sensitive at detecting abnormalities in the heart from just a single plane. Specifically with regard to HCM where the hypertrophy occurred only in the <NUM> chamber view yet the principal transformation was still significantly reduced in the <NUM> chamber view.

Claim 1:
A system (<NUM>) for measuring deformation of a heart, the system (<NUM>) comprising:
an imaging system arranged to acquire two images of the heart at respective points in the cardiac cycle;
locating means for locating a series of pairs of points on the images, each pair of points indicating the respective positions of a single part of the heart in two images;
processing means (<NUM>, <NUM>) arranged to:
calculate from the positions of said pairs of points a value of at least one parameter of the deformation of the heart, characterized in that the at least one parameter includes the sum of displacements in the longitudinal and radial directions for at least one of said parts of the heart.