CN118076299A - Improving cardiac ultrasound imaging - Google Patents

Abstract

The present invention provides a method for imaging a heart. An ultrasound probe is controlled to receive a first reflected ultrasound echo signal from the heart transmitted using a first transmission parameter, and an ultrasound image of the heart is created based on the first reflected ultrasound echo signal using a first echo signal processing parameter. Model-based segmentation is performed on the ultrasound image to identify the anatomical structure of the heart, and for at least one anatomical structure having an information loss region, the segmentation is used to extrapolate from the identified anatomical structure to identify the location of the information loss region within the image. A next ultrasound image of the heart is obtained using a second different echo signal processing parameter and/or a second different transmission parameter specifically for the at least one anatomical structure or for the information loss region of the at least one anatomical structure, thereby improving the signal-to-noise ratio at least for the information loss region.

Description

Improving cardiac ultrasound imaging

Technical Field

The present invention relates to the field of cardiac ultrasound imaging, and in particular to improving the signal-to-noise ratio of cardiac ultrasound images.

Background technique

The visibility of the side walls in cardiac B-ultrasound images is a key image quality component in cardiac ultrasound imaging. In many cardiac ultrasound images, the side walls are not visible due to low signal-to-noise ratio (SNR). There are known techniques to improve SNR in the image formation process, most of which involve averaging in some dimension, such as using multiple transmit pulses per scan line to adjust the receive beamforming (e.g., the number of elevation planes) and adjusting the retrospective transmit beamforming.

However, when these techniques are applied to ultrasound images, there is a significant increase in computational complexity, which may result in a reduction in frame rate. These drawbacks may prevent these techniques from being included as part of the default image formation mode. Therefore, there is a need for an improved method for improving the SNR of cardiac ultrasound images.

US2021/128114 A1 discloses a method for automatically adjusting beamformer parameters based on ultrasound image analysis to enhance ultrasound image acquisition.

Summary of the invention

The invention is defined by the claims.

According to an example of one aspect of the present invention, there is provided a method for imaging a heart, comprising:

controlling the ultrasound probe to receive a first reflected ultrasound echo signal from the heart, wherein the first reflected ultrasound echo signal is transmitted using a first transmission parameter;

creating an ultrasound image of the heart from the first reflected ultrasound echo signal using first echo signal processing parameters;

performing a model-based segmentation on the ultrasound image to identify anatomical structures of the heart;

for at least one anatomical structure having a dropout region, using the segmentation to extrapolate from the identified anatomical structure to identify a location of the dropout region within the image; and

A next ultrasound image of the heart is obtained using second different echo signal processing parameters and/or second different transmission parameters specifically for the at least one anatomical structure or for the information loss area of the at least one anatomical structure, thereby improving the signal-to-noise ratio at least for the information loss area.

The information loss area may be an area in the anatomical structure for which the received ultrasound signal is relatively weak. The information loss area (ie, information loss region) may be an area in the ultrasound image where the SNR is less than 15 dB or less than 10 dB.

Typically, in ultrasound images where the sidewalls are not visible (or barely visible), the sidewalls are the only areas in the image that require SNR improvement, since the rest of the image has sufficient SNR. If the location of the sidewalls is known a priori, averaging techniques for improving SNR (i.e., by using different signal processing parameters) can be selectively applied to areas of the image that contain the sidewalls. This will limit the disadvantages of using these techniques (i.e., reduced frame rate, increased computational complexity). However, before performing a scan, it may be difficult to determine where the sidewalls will be in the image, since this will vary from subject to subject and will depend on the position and orientation of the ultrasound probe relative to the subject's heart.

Thus, model-based segmentation can be used to identify anatomical structures, such as the lateral wall, in ultrasound images of the heart. Model-based segmentation can be able to segment regions of anatomical structures with low SNR (i.e., regions that are barely visible in the ultrasound image). In other words, model-based segmentation can be able to predict the location of regions of anatomical structures (with random deactivation) given other (visible) regions of the anatomical structure.

The central assumption of model-based segmentation methods is that the structure of interest has a tendency towards a specific shape. In other words, the segmentation is biased towards a specific shape. This is of particular interest in the case of cardiac imaging, because the heart is expected to have a specific shape regardless of the subject. Therefore, a probabilistic model that characterizes the shape of the heart (and its variations) for model-based segmentation can be sought. This model is used as prior information to impose constraints when segmenting the image. Such a task can involve the registration of training examples with common poses, the probabilistic representation of variations of the registered samples, and statistical inference between the model and the image.

For example, the Philips HeartModel(TM) is a model-based segmentation algorithm that can identify and label structures of the heart in ultrasound B-mode images. Thus, an initial scan can be performed using a default imaging scheme (i.e., first echo signal processing parameters) to create an input for the heart model. Model-based segmentation using the heart model can then monitor the image for areas containing the sidewall, or if the sidewall is not visible, the expected location of the sidewall can be predicted taking into account other heart structures.

The locations of random deactivations may be identified based on the output of the model-based segmentation (ie, the locations of anatomical structures) having low SNR.

The second transmit parameters and/or the second echo signal processing parameters may be selected to thereby improve the SNR of the ultrasound image relative to the first transmit parameters and/or the first echo signal processing parameters, respectively.

Typically, the information dropout area may cover about 10%-25% of the scan lines of the ultrasound image. Therefore, having different transmit/receive parameters for this small area in the ultrasound image does not affect computing resources and/or frame rate as much as using different transmit/receive parameters for which ultrasound image.

Acquiring a next ultrasound image of the heart using second different echo signal processing parameters and/or second different transmit parameters may be specifically targeted only at least one anatomical structure, or only at information dropout areas.

Obtaining a next ultrasound image of the heart may include controlling the ultrasound probe to receive a second reflected ultrasound echo signal, wherein the second reflected ultrasound echo signal is transmitted using second transmission parameters, and optionally, applying second different echo signal processing parameters to the second reflected ultrasound echo signal.

The second different transmit parameters may include different beamforming parameters compared to the first transmit parameters, wherein the second transmit parameters include one or more of the following relative to the first transmit parameters:

The number of transmitted pulses per scan line is increased;

Increased transmit aperture;

Increased scan line density;

Different pulse frequencies; and

Different pulse shapes.

Obtaining a next ultrasound image of the heart may include applying second, different echo signal processing parameters to the first reflected ultrasound echo signal.

The first echo signal processing parameter and the second echo signal processing parameter may include parameters for retrospective dynamic transmit focusing. For example, they may include the number of transmit beams for each scan line combination. The number of transmit beams for the combination of the second echo signal processing parameter may be greater than the number of transmit beams for the combination of the first echo signal processing parameter.

The parameters for retrospective dynamic transmit focusing may include the number of so-called "multiline inputs" in each transmit beam. The number of multiline inputs for the second echo signal processing parameters may be greater than the number of multiline inputs for the first echo signal processing parameters. Forming multilines may be a prerequisite for retrospective dynamic transmit focusing. The number of multilines needs to be increased to increase the number of transmit beams per scan line.

The first echo signal processing parameter and the second echo signal processing parameter may include parameters for elevation spatial compounding. For example, they may include the number of elevation planes. The number of elevation planes for the second echo signal processing parameter may be greater than the number of elevation planes for the first echo signal processing parameter.

The first echo signal processing parameters and the second echo signal processing parameters may include frequency compounding parameters, and the frequency compounding parameters for the second echo signal processing parameters may be selected such that they improve the contrast-to-noise ratio of the information dropout region.

The first echo signal processing parameters and the second echo signal processing parameters may include spatial compounding parameters, and the spatial compounding parameters for the second echo signal processing parameters may be selected such that they improve the contrast-to-noise ratio of the information dropout region.

Identifying locations of information dropout regions within the image may include using a confidence metric generated by the model-based segmentation.

The at least one anatomical structure may include a lateral heart wall.

The present invention also provides a computer program comprising computer program code, which is suitable for implementing the aforementioned method of imaging the heart when the program is run on a computer.

The present invention also provides an ultrasonic imaging system for imaging a heart, comprising:

An ultrasound probe configured to transmit a first ultrasound signal using a first transmission parameter and receive a first reflected ultrasound echo signal from the heart;

an image creation system for creating an ultrasound image of the heart using first echo signal processing parameters;

a model-based segmentation unit for identifying anatomical structures of the heart based on the ultrasound image, wherein the image-based segmentation unit is adapted to extrapolate from the identified anatomical structures to identify locations of at least one anatomical structure dropout region within the image; and

A controller is adapted to select second different echo signal processing parameters and/or second different transmission parameters specifically for the at least one anatomical structure or for the information dropout region of the anatomical structure, thereby improving the signal-to-noise ratio at least for the information dropout region.

The controller may be adapted to control the ultrasound probe to transmit a second ultrasound signal using second different transmission parameters and receive a second reflected ultrasound echo signal from the heart, and optionally, to apply the second different echo signal processing parameters to the second reflected ultrasound echo signal.

The controller may be adapted to apply second, different echo signal processing parameters to the first reflected ultrasound echo signal.

The image-based segmentation unit may be adapted to perform extrapolation based on the identified anatomical structures to identify locations of information dropout regions within the image by using the confidence volume generated by the model-based segmentation.

These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the invention, and in order to more clearly show how the invention may be put into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

FIG1 illustrates the general operation of an exemplary ultrasound system;

FIG2 shows a graphical representation of an ultrasound image of a heart; and

FIG3 shows the output of a model-based segmentation of an ultrasound image of the heart.

Detailed ways

The present invention will be described with reference to the accompanying drawings.

It should be understood that the detailed description and specific examples, although indicating exemplary embodiments of the apparatus, system and method, are intended for illustrative purposes only and are not intended to limit the scope of the invention. These and other features, aspects and advantages of the apparatus, system and method of the present invention will become better understood based on the following description, the appended claims and the accompanying drawings. It should be understood that the drawings are only schematic and are not drawn to scale. It should also be understood that the same reference numerals are used throughout the drawings to indicate the same or similar parts.

The present invention provides a method for imaging a heart. An ultrasound probe is controlled to receive a first reflected ultrasound echo signal from the heart transmitted using a first transmission parameter, and an ultrasound image of the heart is created based on the first reflected ultrasound echo signal using a first echo signal processing parameter. Model-based segmentation is performed on the ultrasound image to identify the anatomical structure of the heart, and for at least one anatomical structure having an information loss region, the segmentation is used to extrapolate from the identified anatomical structure to identify the location of the information loss region within the image. A next ultrasound image of the heart is obtained using a second different echo signal processing parameter and/or a second different transmission parameter specifically for at least one anatomical structure or for the information loss region of at least one anatomical structure, thereby improving the signal-to-noise ratio at least for the information loss region.

The general operation of an exemplary ultrasound system is described first with reference to FIG. 1 , with emphasis placed on the signal processing functions of the system as the present invention relates to the processing of signals measured by a transducer array.

The system includes an array transducer probe 4 having a transducer array 6 for transmitting ultrasound and receiving echo information. The transducer array 6 may include CMUT transducers; piezoelectric transducers formed of materials such as PZT or PVDF; or any other suitable transducer technology. In this example, the transducer array 6 is a two-dimensional array of transducers 8 that can scan a 2D plane or a three-dimensional volume of a region of interest. In another example, the transducer array may be a 1D array.

The transducer array 6 is coupled to a microbeamformer 12, which controls the reception of signals by the transducer elements. The microbeamformer is capable of beamforming at least the signals received by a subarray (generally referred to as a "group" or "block") of transducers, as described in U.S. Patents 5,997,479 (Savor et al.), 6,013,032 (Savord), and 6,623,432 (Powers et al.).

It should be noted that the microbeamformer is optional overall. In addition, the system includes a transmit/receive (T/R) switch 16 to which the microbeamformer 12 can be coupled and which switches the array between transmit and receive modes and protects the main beamformer 20 from high energy transmit signals in the event that the microbeamformer is not used and the transducer array is operated directly by the main system beamformer. Transmission of ultrasound beams from the transducer array is directed by a transducer controller 18, which is coupled to the microbeamformer by the T/R switch 16 and the main transmit beamformer (not shown), which can receive user-operated input from a user interface or control panel 38. The controller 18 can include transmit circuitry arranged to drive the transducer elements of the array 6 (directly or via the microbeamformer) during the transmit mode.

In a typical line-by-line imaging sequence, the beamforming system within the probe may operate as follows. During transmit, the beamformer (which may be a microbeamformer or a main system beamformer depending on the implementation) activates the transducer array, or a subaperture of the transducer array. The subaperture may be a one-dimensional transducer line or a two-dimensional transducer sheet within a larger array. In transmit mode, the focusing and steering of the ultrasound beam generated by the array or a subaperture of the array is controlled as described below.

When receiving the backscattered echo signal from the object, the received signal undergoes receive beamforming (described below) to align the received signal, and in the case where a subaperture is used, the subaperture is then displaced, for example, by one transducer element. The displaced subaperture is then activated and the process is repeated until all transducer elements of the transducer array have been activated.

For each line (or sub-aperture), the total received signal used to form the associated line in the final ultrasound image will be the sum of the voltage signals measured by the transducer elements of a given sub-aperture during the receive cycle. After the following beamforming process, the resulting line signal is typically referred to as radio frequency (RF) data. The per-line signal (RF data set) generated by each sub-aperture then undergoes additional processing to generate the lines of the final ultrasound image. The change in the amplitude of the line signal over time will contribute to the change in the brightness of the ultrasound image with depth, where high amplitude peaks will correspond to bright pixels (or sets of pixels) in the final image. Peaks that appear near the beginning of the line signal will represent echoes from shallow structures, while peaks that appear gradually later in the line signal will represent echoes from structures at gradually increasing depths within the object.

One of the functions controlled by the transducer controller 18 is the direction in which the beam is steered and focused. The beam can be steered straight ahead from (orthogonal to) the transducer array, or at a different angle for a wider field of view. The steering and focusing of the transmit beam can be controlled as a function of the transducer element actuation time.

In general ultrasound data acquisition two methods can be distinguished: plane wave imaging and "beam steering" imaging. The two methods are distinguished by the presence of beamforming in the transmit ("beam steering" imaging) and/or receive mode (plane wave imaging and "beam steering" imaging).

First, let's look at the focusing function. By activating all transducer elements at the same time, the transducer array generates a plane wave that diverges as it travels through the object. In this case, the beam of ultrasound waves remains unfocused. By introducing a position-dependent time delay to the activation of the transducer, it is possible to cause the wavefront of the beam to converge at a desired point, called the focal zone. The focal zone is defined as the point where the lateral beam width is less than half the transmit beam width. In this way, the lateral resolution of the final ultrasound image is improved.

For example, if the time delay causes the transducer elements to be activated sequentially, starting with the outermost elements of the transducer array and ending at the center element(s), a focal zone will be formed at a given distance from the probe, in line with the center element(s). The distance of the focal zone from the probe will vary depending on the time delay between each subsequent round of transducer element activations. After the beam passes through the focal zone, it will begin to diverge, forming a far-field imaging region. It should be noted that for a focal zone located near the transducer array, the ultrasound beam will diverge rapidly in the far field, resulting in beam width artifacts in the final image. Typically, the near field located between the transducer array and the focal zone shows little detail due to the large overlap in the ultrasound beam. Therefore, varying the position of the focal zone may result in significant changes in the quality of the final image.

It should be noted that in transmit mode, only one focus may be defined, unless the ultrasound image is divided into multiple focal zones (each of which may have a different transmit focus).

Furthermore, in the case of receiving echo signals from within the subject, it is possible to perform the inverse of the above process to perform receive focusing. In other words, the incoming signal may be received by the transducer elements and subjected to an electronic time delay before being passed to the system for signal processing. The simplest example of this is known as delay and sum beamforming. It is possible to dynamically adjust the receive focus of the transducer array as a function of time.

Turning now to the functionality of beam steering, by properly applying time delays to the transducer array, it is possible to impose a desired angle on an ultrasound beam as it leaves the transducer array. For example, by activating a transducer on a first side of the transducer array, followed by the remaining transducers in the sequence, ending on the opposite side of the array, the wavefront of the beam is angled toward a second side. The magnitude of the steering angle relative to the normal to the transducer array depends on the magnitude of the time delay between subsequent transducer element activations.

Additionally, it is possible to focus the steered beam, wherein the total time delay applied to each transducer element is the sum of both the focusing and the steering time delays. In this case, the transducer array is called a phased array.

In the case of CMUT transducers, which require a DC bias voltage for their activation, the transducer controller 18 can be coupled to control the transducer array's DC bias control 45. The DC bias control 45 sets the DC bias voltage(s) applied to the CMUT transducer elements.

For each transducer element of the transducer array, an analog ultrasound signal (commonly referred to as channel data) enters the system in the form of a receive channel. In the receive channel, a partially beamformed signal is generated from the channel data by the microbeamformer 12, which is then passed to the main receive beamformer 20, where the partially beamformed signals from the individual blocks of transducers are combined into a fully beamformed signal (referred to as radio frequency (RF) data). The beamforming performed at each stage can be performed as described above, or additional functions can be included. For example, the main beamformer 20 can have 128 channels, each of which receives partially beamformed signals from tens or hundreds of transducer elements. In this way, the signals received by thousands of transducers of the transducer array can effectively contribute to a single beamformed signal.

The beamformed received signals are coupled to a signal processor 22. The signal processor 22 can process the received echo signals in various ways, such as: bandpass filtering; decimation; I and Q component separation; and harmonic signal separation, which is used to separate linear and nonlinear signals, thereby enabling identification of nonlinear (higher harmonics of the fundamental frequency) echo signals returned from tissue and microbubbles. The signal processor can also perform additional signal enhancements, such as speckle reduction, signal compounding, and noise elimination. The bandpass filter in the signal processor can be a tracking filter, whose bandpass slides from a higher frequency band to a lower frequency band as the echo signals are received from increasing depths, thereby rejecting higher frequency noise from greater depths, which is generally lacking anatomical information.

The beamformers for transmission and for reception are implemented in different hardware and can have different functions. Of course, the receiver beamformer is designed to take into account the characteristics of the transmit beamformer. For simplicity, only the receiver beamformers 12, 20 are shown in Figure 1. In the complete system, there is also a transmit chain with a transmit microbeamformer and a main transmit beamformer.

The function of the microbeamformer 12 is to provide initial combining of the signals to reduce the number of analog signal paths. This is typically performed in the analog domain.

Final beamforming is done in the main beamformer 20, typically after digitization.

The transmit and receive channels use the same transducer array 6 with fixed frequency bands. However, the bandwidth occupied by the transmit pulses can vary depending on the transmit beamforming used. The receive channel can capture the entire transducer bandwidth (which is the classical approach), or by using bandpass processing, which can extract only the bandwidth containing the desired information (e.g. harmonics of the main harmonic).

The RF signal can then be coupled to a B-mode (i.e., brightness mode, or 2D imaging mode) processor 26 and a Doppler processor 28. The B-mode processor 26 performs amplitude detection on the received ultrasound signal for imaging structures in the body (e.g., organ tissue and blood vessels). In the case of line-by-line imaging, each line (beam) is represented by an associated RF signal, the amplitude of which is used to generate a brightness value to be assigned to a pixel in the B-mode image. The exact location of the pixel within the image is determined by the location of the associated amplitude measurement together with the RF signal and the number of lines (beams) of the RF signal. The B-mode image of such a structure can be formed as a harmonic or fundamental image mode, or a combination of the two, as described in U.S. Pat. No. 6,283,919 (Roundhill et al.) and U.S. Pat. No. 6,458,083 (Jago et al.). The Doppler processor 28 processes the temporally different signals generated by tissue movement and blood flow for detecting moving materials, such as the flow of blood cells in the image field. The Doppler processor 28 typically includes a wall filter, the parameters of which are set to pass or reject echoes returned from selected types of materials in the body.

The structure and motion signals generated by the B-mode or Doppler processor are coupled to the scan converter 32 and the multi-plane reformatter 44. The scan converter 32 arranges the echo signals in a spatial relationship from which they are received in a desired image format. In other words, the scan converter is used to convert the RF data from a cylindrical coordinate system to a Cartesian coordinate system suitable for displaying an ultrasound image on the image display 40. In the case of B-mode imaging, the brightness of a pixel at a given coordinate is proportional to the amplitude of the RF signal received from that position. For example, the scan converter can arrange the echo signals into a two-dimensional (2D) sector format, or a pyramid-type three-dimensional (3D) image. The scan converter can overlay the B-mode structural image with a color corresponding to the motion at a point in the image field, where the Doppler estimated velocity is used to generate a given color. The combined B-mode structural image and color Doppler image depict the motion of tissue and blood flow within the structural image field. The multi-plane reformatter will convert the echoes received from points in a common plane in a volumetric region of the body into an ultrasound image of the plane, as described in U.S. Pat. No. 6,443,896 (Detmer). The volume renderer 42 converts the echo signals of the 3D data set into a projected 3D image as viewed from a given reference point, as described in US Pat. No. 6,530,885 (Entrekin et al.).

The 2D or 3D images from the scan converter 32, the multi-planar reformatter 44, and the volume renderer 42 are coupled to the image processor 30 for further enhancement, buffering, and temporary storage for display on the image display 40. The imaging processor may be adapted to remove certain imaging artifacts from the final ultrasound image, such as: acoustic shadowing, such as caused by a strong attenuator or refraction; back enhancement, such as caused by a weak attenuator; reverberation artifacts, such as where a highly reflective tissue interface is located adjacent; etc. In addition, the image processor may be adapted to process certain speckle reduction functions to improve the contrast of the final ultrasound image.

In addition to being used for imaging, the blood flow values produced by the Doppler processor 28 and the tissue structure information produced by the B-mode processor 26 are coupled to a quantification processor 34. In addition to structural measurements (such as organ size and gestational age), the quantification processor also produces measurements of different flow conditions, such as the volumetric rate of blood flow. The quantification processor can receive input from a user control panel 38, such as points in the anatomical structure of the image at which measurements are to be made.

The output data from the quantization processor is coupled to the graphics processor 36 for reproducing the measurement graphs and values on the display 40 together with the image, and for outputting audio from the display device 40. The graphics processor 36 can also generate image overlays for display with the ultrasound image. These graphic overlays can contain standard identification information, such as the patient's name, the date and time of the image, imaging parameters, etc. For these purposes, the graphics processor receives input from the user interface 38, such as the patient's name. The user interface is also coupled to the transmit controller 18 to control the generation of ultrasound signals from the transducer array 6 and thus control the images produced by the transducer array and the ultrasound system. The transmit control function of the controller 18 is only one of the functions performed. The controller 18 also takes into account the operating mode (given by the user) and the corresponding required transmitter configuration and bandpass configuration in the receiver analog-to-digital converter. The controller 18 can be a state machine with fixed states.

The user interface is also coupled to a multi-planar reformatter 44 for selection and control of planes of a plurality of multi-planar reformatted (MPR) images that may be used to perform quantitative measurements in the image field of the MPR images.

The present invention aims to improve the signal-to-noise ratio (SNR) of the region in the ultrasound image where the side wall is expected to appear. By using a heart model to predict the expected position of the side wall, SNR improvement techniques can be applied selectively to limit their negative impact on frame rate and computational complexity.

Figure 2 shows an illustration of an ultrasound image of a heart and is used to explain the processing steps for improving the signal-to-noise ratio. A first B-mode ultrasound image 202 is acquired using default imaging techniques and parameters. These are first transmit parameters and first echo signal processing parameters.

Typically, the first image 202 is obtained using first transmit parameters and first receive parameters (ie, first echo signal processing parameters). The transmit parameters are parameters that affect the transmit pulses of the ultrasound probe/transducer. The receive parameters are parameters that affect the processing of the received echo signals.

Model-based segmentation (MBS) is applied to the first image 202 (e.g., using a fast heart model) to identify the anatomical structures of the heart. The identified anatomical structures are visible in the segmented ultrasound image 204. As can be seen in the segmented image 204, MBS is able to identify where the anatomical regions should be, even if a particular region is not visible due to random inactivation caused by too weak ultrasound signals.

Thus, the image of the anatomical structure has regions of information loss as represented by image portion 206, and the segmentation is used to extrapolate from the anatomical structure identified in image 204 to identify the location of the regions of deactivation in the image at any given time.

For example, areas in the first image 202 that either contain sidewalls (if the sidewalls are visible) or are expected to contain sidewalls (if the sidewalls are not visible) are identified based on the segmentation. This can be seen in the upper left area of the segmented image 204, where the MBS has identified the sidewalls even though they are not shown in the first image 202.

MBS is known to be a robust technique applied to ultrasound images even in the presence of missing or noisy data. It is thus possible to predict anatomical regions such as the sidewall based on a trained constellation model. Applying MBS with a small number of grid points and iterations allows for a fast adaptation of the image with sufficient accuracy.

This allows, for example, identifying sidewall regions in one frame and dynamically adjusting imaging parameters (ie, transmit and/or receive parameters) for the next frame of the ultrasound sequence.The fast model can be used to identify regions that frequently experience poor visibility or random deactivations.

The next step involves obtaining a second ultrasound image 208 having a better SNR for the sidewalls than the first image 202 for the information dropout region 206 .

The second ultrasound image may be obtained using second different echo signal processing parameters specifically for the at least one anatomical structure or for the information dropout region 206 of the at least one anatomical structure and/or by using second different transmission parameters.

A first embodiment for obtaining the second image 208 involves retransmission of a new scan line pulse sequence for the information dropout region 206. In practice, when performing cardiac ultrasound imaging, the information dropout region 206 will likely be the side walls.

For image lines containing sidewalls (i.e. corresponding to information dropout regions 206), instead of transmitting one pulse at the position and acquiring a set of channel data at these positions, it is proposed to transmit N pulses and obtain N sets of channel data. For example, the number of pulses per line can be increased from one to two or three.

The transmit pulses may also be sent at different frequencies and/or with different waveforms. These changes may help decorrelate the acoustic multipath noise and thus improve the SNR of the second image 208.

Additionally, if the default image settings (i.e., the first transmit parameters) do not use the largest transmit aperture (corresponding to the thinnest, most focused transmit beam), the transmit aperture may be increased for scan lines around the information dropout region 206. For example, the line density may be increased from 1 degree to 0.25 degrees.

For each of the scan line positions where multiple data sets (eg, N transmit pulses) are acquired, the data sets acquired for the same position may be averaged. If the receive beamforming is based on retrospective transmit beamforming, the averaging may need to be performed prior to the retrospective transmit beamforming.

Retrospective transmit beamforming can be used to form synthetic focused ultrasound images using standard (scanned and focused) ultrasound transmissions. In general, retrospective transmit beamforming is a synthetic focusing technique that uses a combination of standard, scanned beam transmit data, dynamic receive focusing, and time-aligned data from multiple transmit beams to form an image.

Beamforming may also be based on multi-line beamforming. Multi-line beamforming is based on obtaining more than one scan line (i.e., multi-lines) for a single transmit beam to increase the frame rate. The number of multi-lines in each transmit beam is based on the width of the transmit beam (i.e., the wider the transmit beam, the more lines can be obtained). Multi-line beamforming may be a prerequisite for retrospective transmit beamforming. Retrospective transmit beamforming is a known method for increasing the SNR of ultrasound images. The number of multi-lines may be a parameter for retrospective transmit beamforming. Therefore, the first and second echo signal processing parameters may include parameters for retrospective transmit beamforming, wherein the parameters may include the number of multi-lines obtained and/or the number of transmit beams combined.

For example, the number of multilines is adjusted before adjusting the number of transmit beams combined in retrospective transmit beamforming.

However, averaging of the transmit pulses can be done before or after the receive multiline is formed. Typically, some form of averaging is performed on one or more of the dimensions along which the several acquisitions are obtained. The averaging can be coherent and/or incoherent.

The remainder of the image formation process for the second image 208 (as discussed above) may be completed based on a conventional process (e.g., the same as the image formation process for the first image 202). This is possible because the change in emission parameters for the information dropout region 206 should have an improved SNR for the sidewall region compared to the first image 202.

In general, the first embodiment can be summarized as: identifying an information missing region 206 from a first image 202, adjusting transmit parameters (e.g., via optimization) by, for example, using known SNR improvement techniques, and obtaining a subsequent second image 208 in which the information missing region 206 is obtained using the adjusted (second) transmit parameters. By continuously applying MBS to each ultrasound frame, it is possible to refine the region of interest or track it as the object or transducer moves.

The first embodiment described above involves re-transmitting a custom pulse sequence after running MBS (e.g., a Philips heart model) on the first ultrasound image 202. Although this approach is theoretically possible, implementing the solution may require extensive software and/or field programmable gate array (FPGA) code changes and rework. Therefore, in some cases, it may be preferred not to adjust the transmit parameters.

A second embodiment is proposed that does not involve retransmission. Instead of acquiring new echo signal data, after the MBS identifies the information dropout region 206, the channel data used to create the first image 202 is reprocessed in a different manner (ie, with different reception parameters). Various SNR enhancement techniques are proposed below.

An exemplary SNR improvement technique for receive parameters involves, in the information dropout region 206, increasing the number of simultaneously received beams (also referred to as multilines) for each formed transmit beam (up to a certain point depending on the width of the transmit beam) when using a retrospective dynamic transmit focusing method (i.e., a version of retrospective transmit beamforming), which then allows for an increase in the number of transmit beams combined per scan line. Retrospective dynamic transmit focusing averages multiple transmit beams for a single location to increase the signal-to-noise ratio. When multiple transmit beams are combined to generate each scan line, and each transmit beam has a different noise pattern, the noise sums incoherently while the signal sums coherently, thus improving the signal-to-noise ratio.

In general, the parameters of the retrospective dynamic transmit focusing can be adapted to increase the SNR of the information dropout region 206. The parameters of the retrospective dynamic transmit focusing may include the number of multilines in the transmit beams that are combined (eg, averaged) and/or the number of transmit beams that are combined.

Another exemplary SNR improvement technique for receive parameters involves forming and averaging a number of closely spaced elevation planes used in an elevation spatial compounding method in the information dropout region 206. The averaging can be done coherently in the RF domain in beam summing, or incoherently after log compression, in which case it is generally referred to as "elevation compounding." The number of elevation planes can only be increased to a certain point before reduced return is achieved because the elevation planes must all fit within the transmit elevation beam.

Generally, the parameters of the elevation spatial compounding can be adapted to increase the SNR of the information dropout region 206 .

Adjusting the amount of frequency compounding and/or performing additional spatial compounding may also be used to improve the contrast-to-noise ratio of the information dropout region 206. However, improving the contrast-to-noise ratio by changing the frequency compounding and/or performing additional spatial compounding may come at the expense of resolution.

Frequency compounding is commonly used to reduce speckle variations in ultrasound images to enhance contrast resolution by averaging two or more uncorrelated sub-band images (after receive beamforming). In other words, two or more different bandpass filters are applied to the received echoes and the results are averaged.

Similarly, spatial compounding involves obtaining echo data from several different angles and combining them (eg, by averaging) to produce a single ultrasound image.

Can be used together or separately to increase the number of transmit beams combined in retrospective dynamic transmit focusing, as well as to form closely spaced elevation planes.

The first embodiment (changing the transmission parameters) can be combined with the second embodiment (changing the reception parameters) so that a new second image 208 is obtained with different reception and transmission parameters, wherein the changes in both reception and transmission parameters are used to improve the SNR of the information loss area 206 relative to the first image 204.

With regard to the location of information loss regions, a built-in confidence measure including MBS is proposed to identify the regions. FIG3 shows the output of the model-based segmentation on an ultrasound image of a heart 302. MBS has been applied to the ultrasound image 302 and the results have been superimposed on the ultrasound image 302. For illustrative purposes, a portion 304 of the MBS output has been illustrated below the ultrasound image 302.

For regions 306 with high confidence (thick black line), the beamforming parameters may not need to be modified, whereas in regions 308 of low confidence (dashed line), the beamforming may need to be adapted to achieve a higher SNR for the information dropout regions.

It may be advantageous to gradually steer the (computational) effort put into retransmission/recomputation. This ideally balances the drop in frame rate with a useful investment in SNR improvement. For example, the number of pulses N may be determined by the "weakness" of image features and/or the determination of the SNR for areas of information loss.

A person skilled in the art will readily be able to develop a controller for executing any of the methods described herein. Therefore, each step in the flow chart may represent a different action performed by the controller and may be executed by a corresponding module of the processing controller.

A controller can be implemented in a variety of ways using software and/or hardware to perform the various functions required. A processor is an example of a controller that employs one or more microprocessors that can be programmed using software (e.g., microcode) to perform the required functions. However, a controller may be implemented with or without a processor, and may also be implemented as a combination of dedicated hardware that performs certain functions and a processor (e.g., one or more programmed microprocessors and associated circuits) that performs other functions.

Examples of controller components that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field programmable gate arrays (FPGAs).

In various embodiments, a processor or controller may be associated with one or more storage media, such as volatile and non-volatile computer memory, such as RAM, PROM, EPROM, and EEPROM. The storage media may be encoded with one or more programs that, when run on one or more processors and/or controllers, perform the desired functions. The various storage media may be fixed within the processor or controller, or may be removable so that one or more programs stored thereon may be loaded into the processor or controller.

Those skilled in the art anticipate that variations of the disclosed embodiments will be understood and implemented in practicing the claimed invention based on a study of the drawings, the disclosure and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality.

The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

The computer program may be stored/distributed on suitable media, such as optical storage media or solid-state media provided together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

If the term "suitable for" is used in the claims or the specification, it should be noted that the term "suitable for" is intended to be equivalent to the term "configured to".

Any reference signs in the claims should not be construed as limiting the scope.

Claims (15)

1. A method for imaging a heart, comprising: controlling the ultrasound probe to receive a first reflected ultrasound echo signal from the heart, wherein the first reflected ultrasound echo signal is transmitted using a first transmission parameter; creating an ultrasound image of the heart from the first reflected ultrasound echo signal using first echo signal processing parameters (202); performing a model-based segmentation on the ultrasound image (202) to identify anatomical structures of the heart; For at least one anatomical structure having an information dropout region (206), using the segmentation to extrapolate from the identified anatomical structure to identify a location of the information dropout region (206) within the image; and A next ultrasound image (208) of the heart is obtained using second different echo signal processing parameters and/or second different transmission parameters specifically for the at least one anatomical structure or for the information loss area (206) of the at least one anatomical structure, thereby improving the signal-to-noise ratio at least for the information loss area (206). 2. The method of claim 1 , wherein obtaining a next ultrasound image ( 208 ) of the heart comprises controlling the ultrasound probe to receive a second reflected ultrasound echo signal, wherein the second reflected ultrasound echo signal is transmitted using the second transmission parameters, and optionally, applying the second different echo signal processing parameters to the second reflected ultrasound echo signal. 3. The method of claim 2, wherein the second different transmit parameters comprise different beamforming parameters compared to the first transmit parameters, wherein the second transmit parameters comprise one or more of the following relative to the first transmit parameters: The number of additional transmit pulses per scan line; Increased transmit aperture; Increased scan line density; Different pulse frequencies; and Different pulse shapes. 4. The method of claims 1 to 3, wherein obtaining a next ultrasound image (208) of the heart comprises applying the second different echo signal processing parameters to the first reflected ultrasound echo signal. 5 . The method of claim 4 , wherein the first echo signal processing parameter and the second echo signal processing parameter include parameters for retrospective dynamic transmit focusing, including, for example, the number of transmit beams per scan line combination. 6. The method of claim 5, wherein the parameters for retrospective dynamic transmit focusing include the number of multilines in each transmit beam. 7. The method according to any one of claims 4 to 6, wherein the first echo signal processing parameters and the second echo signal processing parameters include parameters for elevation space compounding, including for example the number of elevation planes. 8. A method as claimed in any one of claims 4 to 7, wherein the first echo signal processing parameters and the second echo signal processing parameters include frequency compounding parameters and/or spatial compounding parameters, wherein the frequency compounding parameters and/or spatial compounding parameters for the second echo signal processing parameters are selected so that they improve the contrast-to-noise ratio of the information loss area (206). 9. The method of any one of claims 1 to 8, wherein identifying the location of the information dropout region (206) within the image comprises using a confidence measure generated by the model-based segmentation. 10. The method of any one of claims 1 to 9, wherein the at least one anatomical structure comprises a lateral heart wall. 11. A computer program comprising computer program code means adapted to implement the method of any one of claims 1 to 10 when the program is run on a computer. 12. An ultrasound imaging system for imaging a heart, comprising: an ultrasound probe configured to transmit a first ultrasound signal using a first transmission parameter and receive a first reflected ultrasound echo signal from the heart; an image creation system for creating an ultrasound image (202) of the heart using first echo signal processing parameters; a model-based segmentation unit for identifying an anatomical structure of the heart based on the ultrasound image, wherein the image-based segmentation unit is adapted to extrapolate from the identified anatomical structure to identify a location of at least one anatomical structure dropout region (206) within the image; and A controller is adapted to select second different echo signal processing parameters and/or second different transmission parameters specifically for the at least one anatomical structure or for the information loss region (206) of the anatomical structure, thereby improving the signal-to-noise ratio at least for the information loss region (206). 13. The system of claim 12, wherein the controller is adapted to control the ultrasound probe to transmit a second ultrasound signal using the second different transmission parameters and to receive a second reflected ultrasound echo signal from the heart, and optionally, to apply the second different echo signal processing parameters to the second reflected ultrasound echo signal. 14. The system of claim 12 or 13, wherein the controller is adapted to apply the second different echo signal processing parameters to the first reflected ultrasound echo signal. 15. The system of any one of claims 12 to 14, wherein the image-based segmentation unit is adapted to identify the location of information dropout regions (206) within the image by using a confidence measure generated by the model-based segmentation.