BACKGROUND OF THE INVENTION

This invention relates to cardiac ultrasonography, e.g. for determining information about a mechanical wave in the heart.

It has been known for many years to use ultrasound imaging techniques to image human and animal hearts, and to analyse the acquired image data in order to extract clinically useful information.

Mechanical waves are oscillations of matter, which transfer energy through a medium. Mechanical waves are generated in the human body by the motion of the heart and blood passing through it—e.g. a shear wave is generated due to the closing of the aortic valve. These mechanical waves can be detected both in the heart and in the arteries closely surrounding the heart. Exactly how they travel through the heart can depend the health of the heart. Thus, it can be desirable to analyse the motion of such waves, e.g. in order to gain insights into patient health.

It is known in the art to use ultrasound imaging to detect mechanical waves within the heart, for example as described in “Clutter Filter Wave Imaging”, Salles et al., 2019, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. PP. 1-1. 10.1109/TUFFC.2019.2923710, and in “Natural mechanical wave detection using ultrafast ultrasound and velocity Clutter Filtering Wave Imaging”, Salles et al., 2019, HAL Id: hal-02094581.

However, the information which is obtained using currently known ultrasound techniques can be difficult to analyse and interpret.

It is therefore desired to provide improved methods of processing and imaging cardiac mechanical waves.

SUMMARY OF THE INVENTION

From a first aspect, the invention provides a method of processing cardiac ultrasound data for determining information about a mechanical wave in the heart, comprising:receiving data representative of a time series of three-dimensional data frames, generated from ultrasound signals from a human or animal heart, wherein each frame comprises a set of voxels, each voxel of the frame having a value representative of an acceleration component of a respective location in the heart at a common time;identifying, for each voxel in the series of frames, a frame of the series in which the value of the voxel is at a maximum;generating a three-dimensional time-propagation data set comprising a set of voxels corresponding to the voxels of the data frames, by assigning each voxel of the data set a value representative of the time of the respective frame in the time series for which the corresponding voxel in the time series is at a maximum; andgenerating data representative of a three-dimensional velocity vector field by calculating time derivatives from the voxels of the three-dimensional time-propagation data set.

From a second aspect, the invention provides an ultrasound data-processing apparatus for determining information about a mechanical wave in the heart, comprising:a processing system, configured to:receive data representative of a time series of three-dimensional data frames, generated from ultrasound signals from a human or animal heart, wherein each frame comprises a set of voxels, each voxel of the frame having a value representative of an acceleration component of a respective location in the heart at a common time;identify, for each voxel in the series of frames, a frame of the series in which the value of the voxel is at a maximum;generate a three-dimensional time-propagation data set comprising a set of voxels corresponding to the voxels of the data frames, by assigning each voxel of the data set a value representative of the time of the respective frame in the time series for which the corresponding voxel in the time series is at a maximum; andgenerate data representative of a three-dimensional velocity vector field by calculating time derivatives from the voxels of the three-dimensional time-propagation data set.

From a third aspect, the invention provides software, or a signal or tangible medium bearing said software, comprising instructions which, when executed by a processing system, cause the processing system to:receive data representative of a time series of three-dimensional data frames, generated from ultrasound signals from a human or animal heart, wherein each frame comprises a set of voxels, each voxel of the frame having a value representative of an acceleration component of a respective location in the heart at a common time;identify, for each voxel in the series of frames, a frame of the series in which the value of the voxel is at a maximum;generate a three-dimensional time-propagation data set comprising a set of voxels corresponding to the voxels of the data frames, by assigning each voxel of the data set a value representative of the time of the respective frame in the time series for which the corresponding voxel in the time series is at a maximum; andgenerate data representative of a three-dimensional velocity vector field by calculating time derivatives from the voxels of the three-dimensional time-propagation data set.

Thus it will be seen that, in accordance with the invention, a 3D velocity vector field is generated for the heart based on the propagation of times of a maximum acceleration through the heart. This allows the propagation of a natural mechanical wave through the heart, in three dimensions, to be identified and potentially imaged. Information about the propagation of mechanical waves in three-dimensions, and the optional displaying of images representing this data, may provide useful clinical information about the heart to a clinician who views the image data. This development may offer an improved scientific understanding of mechanical wave behaviour in hearts in general. Moreover, it is believed that data obtained using embodiments of this approach may provide clinically useful information, such as an indication of fibre orientation of the heart, or even a new pathologic marker.

The method may comprise determining information about a mechanical wave in the heart, which may be a shear wave, a pressure wave or a pulse wave. The information may comprise the data representative of the three-dimensional velocity vector field and/or output data derived therefrom.

In some embodiments, the method further comprises generating output data from the data representative of the three-dimensional velocity vector field. The output data may be image data, e.g. for display on a display apparatus. In some embodiments, the method further comprises displaying the image data on a display apparatus, such as on a flat-panel monitor or through a stereoscopic virtual-reality headset.

In some embodiments, the output data may be exported as numerical values for further analysis, or used as input to another algorithm—e.g. an automated diagnosis system. This may be instead of or additional to generating and displaying display data.

The method may comprise generating, from the three-dimensional velocity vector field, two-dimensional output data representing (i) orientations of the three-dimensional velocity vector field; and/or (ii) magnitudes of the three-dimensional velocity vector field.

Generating the output data may comprise reconstructing one or more trajectories from the data representative of three-dimensional velocity vector field—e.g. one or more streamlines (or streamline segments). Generating the output data may comprise performing a vector field visualisation process, such as streamline simulation. Generating the output data may comprise applying an integration process to the data representative of the velocity vector field. It may comprise performing a Runge-Kutta method, e.g. a fourth-order Runge-Kutta method. The image data may represent a planar section through the heart, or may represent a projection of a three-dimensional surface. Generating the image data may comprise performing a 3D rendering step.

The time derivatives may be calculated in three dimensions—e.g. by calculating respective time derivatives along one, two or three orthogonal axes. In some embodiments a time derivative is calculated for each voxel in the time-propagation data set. A time derivative for a voxel may be calculated from a neighbourhood around the voxel—e.g. from the time values in one or more immediately adjacent voxels. Calculating the time derivative may comprise subtracting a time value of a voxel from a time value of an adjacent voxel. The time derivatives may be calculated taking account of the spatial resolutions of the data set along one or more axes. The may be helpful if the 3D data frames have different resolutions along different respective axes. The step of calculating time derivatives may be carried out by the processing system.

It has been appreciated that calculating absolute velocity vectors may not be necessary in order to extract useful information from the data. Hence, in some embodiments, rather than using the absolute distance between voxels, in each of the x, y and z directions, to differentiate each voxel, the data representative of a three-dimensional velocity vector field may be time-difference data which has not been used to divide distance information relating to the spacing between voxels. In other words, the data may be proportional to the inverse of velocity. This may preserve accuracy by avoiding a division operation which could introduce noise due to rounding errors. This differentiation thus does not produce a vector which represents the absolute velocity vector, but it still represents the direction and relative magnitude of the vectors in the vector field, and fully represents the velocities subject to knowing the voxel spacing.

It is possible to apply the technique described herein to three-dimensional data frames which have already been acquired, however some embodiments advantageously comprise a system which for acquiring the data frames. Thus, in some embodiments, the method further comprises acquiring the series of three-dimensional data frames. In some embodiments, the ultrasound imaging apparatus comprises an ultrasound probe, comprising an array of ultrasound transducers (which may be a two-dimensional array). The ultrasound probe and the processing system may be configured for acquiring the series of three-dimensional data frames.

In some embodiments, acquiring the series of three-dimensional data frames may comprise acquiring high-frame rate three-dimensional Doppler ultrasound data—e.g. of part or all of the heart.

The respective common time for each three-dimensional data frame may be a common time period. It may equal or relate to the time taken to complete one 3D Doppler scan of the heart, or of a region of the heart. The value representative of the time of a respective frame may be a time of a start or mid-point of such a time period.

In some embodiments, acquiring the series of three-dimensional data frames comprises transmitting a series of ultrasound waves towards part or all of the heart, over a time window. The ultrasound waves may be plane or diverging ultrasound waves (also known as unfocused or defocused waves). It may comprise receiving reflections of a series of ultrasound waves. It may comprise processing the received reflections to generate a time series of three-dimensional Doppler data sets, wherein each three-dimensional Doppler data set represents velocities at locations in the heart. It may comprise calculating time derivatives of velocities within a Doppler data set.

It will thus be appreciated that the data representative of the time series of three-dimensional data frames may be received from outside the apparatus (e.g. over a cable or network interface), but may be received internally within the apparatus (e.g. via an internal memory such as RAM) as part of a processing method.

References herein to the human or animal heart may encompass any part of the heart and/or blood vessels immediately adjoining the heart, such as the carotid artery.

In some embodiments, a clutter filter may be applied to each three-dimensional Doppler data set. This may advantageously attenuate or remove from the data set a range of velocity values—e.g. a range selected by a user as being of interest. This range of velocity values may be selected to encompass a velocity component of the mechanical wave (e.g. a mean or maximum expected or actual group velocity of the mechanical wave). As a result, after further processing of the data sets, regions in which these velocity values are present may be apparent by an absence of other data. In some embodiments the clutter filter may be applied by applying a high-pass filter to the three-dimensional Doppler images.

In some embodiments, a spatiotemporal filter may be applied to the time series of three-dimensional Doppler data sets. This advantageously smooths the series of images, increasing the signal-to-noise ratio of the images.

In some embodiments, envelope detection may be carried out on the three-dimensional Doppler images. This may produce a 3D brightness-mode (B-mode) image with improved image quality.

In some embodiments, the method further comprises selecting the time series of frames in which a mechanical wave propagates through the region from a longer set of three-dimensional data frames, e.g. as a time window of interest. Data frames outside the period of interest may be discarded. This advantageously allows the particular time window of interest to be selected, and any data from outside of this time window to be removed, thus significantly reducing the quantity of data which is to be processed and thereby making the imaging method both faster and more efficient. In some embodiments, a period of interest, in which a mechanical wave propagates through the region, may be determined using electrocardiography (ECG). The ultrasound imaging apparatus may comprise an electrocardiogram device, which may be configured to be used to determine a period of interest in which a mechanical wave propagates through a region of the heart. This may allow the period of interest to be detected automatically, and the irrelevant data frames to be removed automatically.

In some embodiments, the method may further comprise applying a segmentation process, e.g. to identify one or more structures within the heart. The segmentation process may use other ultrasound images of the heart, which may be intensity-based (i.e. non-Doppler). These other images may be acquired at a lower frame rate and/or higher spatial resolution than the Doppler data sets or the acceleration data frames. The method may comprise removing, from the series of three-dimensional data frames, data which corresponds to a region of the heart which does not contain a structure of interest. The method may comprise acquiring such ultrasound images of the heart. This step advantageously allows structures of interest to be identified within the images, if the Doppler images are of too low resolution to identify structures within them accurately. The additional use of images, which are of higher quality, allows structures within the images to be reliably identified. Any data which does not correspond to the structure of interest may then be removed from the high-frame rate images, thus reducing the amount of data to be processed and making the method faster and more efficient. For example, the segmentation process may allow identification of the left ventricle of a patient's heart, and thus it would be possible to only keep data relating to the left ventricle, and to discard all other data.

The values representative of acceleration in the data frames may be represented in any appropriate format. They preferably scale linearly with acceleration. They may be calibrated values—e.g. in m/s2—or they may be in any other appropriate unit.

By determining the time at which each voxel experiences a maximum acceleration, within the time window, the propagation of a wavefront past the locations in the heart can be determined.

The time values in the three-dimensional time-propagation data may be represented in any appropriate format. They may be values expressed in seconds or milliseconds, or in any other appropriate unit of time.

The processing system may be configured to implement some or all steps disclosed herein. The processing system may take any suitable form. It may comprise one or more of: a central processing unit, a graphics processing unit, a microcontroller, an ASIC, an FPGA, and any other discrete or integrated components or circuitry. It may comprise memory storing software instructions for instructing a processor to perform some or all of the steps disclosed herein. The processing system may be a single processing unit or device, or it may be distributed—e.g. comprising one or more servers. Processing may be carried out on a single device or may be shared across multiple devices in any appropriate way. For instance, one device may generate data representing a velocity vector field and a different device (possibly remote from the first) may determine a streamline or streamline segment and/or display the output data. In some embodiments, sampled image data may be collected by a first device (e.g. an ultrasound scanner) and sent to a remote computer or server which applies some or all of the processing operations.

Features of any aspect or embodiment described herein may, wherever appropriate, be applied to any other aspect or embodiment described herein. Where reference is made to different embodiments or sets of embodiments, it should be understood that these are not necessarily distinct but may overlap.

