Method and system for fetal heart assessment

Abstract

A method for deriving a biometric parameter of a fetal heart is provided. The method includes acquiring a plurality of ultrasound images of a region of interest, wherein the region of interest includes a fetal heart, and comparing the plurality of ultrasound images to a predefined clinical view. A set of ultrasound images related to the predefined clinical view is selected based on the comparison, wherein the set of ultrasound images represents at least one cardiac cycle. Anatomical landmarks of the fetal heart are detected within ultrasound images of the set of ultrasound images and the anatomical landmarks of the fetal heart are detected or tracked across the set of ultrasound images. A biometric parameter of the fetal heart is then determined based on the anatomical landmarks detected or tracked from one or more ultrasound images of the set of ultrasound images.

Description

TECHNICAL FIELD

[0001] The present invention relates to the field of fetal ultrasound imaging, and more specifically to the field of fetal cardiac ultrasound assessment. BACKGROUND

[0002] Congenital heart disease (CHD) is a common disease affecting approximately 1% of live births and accounts for about 1 / 3 of all congenital diseases. Fetal ultrasound screening is recommended for every pregnant woman worldwide at 18-24 weeks of gestation and provides at least 5 and up to 8 recommended fetal cardiac screening views. The fetal heart is a complex structure and to screen for abnormalities, the recommended views include B-mode (gray scale), Doppler (color) and M-mode ultrasound to understand the structure and function of the fetal heart.

[0003] CHD can be asymptomatic in fetal life, but causes significant morbidity and mortality after birth and is the leading cause of birth defect neonatal infant mortality. The earlier CHD is diagnosed, the better the outcome at birth and treatment options. There are also an increasing number of available and effective in-utero therapies for specific CHD pathologies (e.g. in-utero aortic valve formation for HLHS) that can significantly improve the health of the fetus. These potential benefits all rely on accurate antenatal diagnosis of CHD. Even in areas with widespread antenatal ultrasound, the antenatal diagnosis rate for CHD is 30-50%.

[0004] A major cause of this diagnostic gap is the lack of expertise in interpreting fetal heart images, which is a diagnostic challenge due to the small and fast beating fetal heart and the less frequent encounter of congenital heart disease by the caregiver. The signs of heart disease are often very subtle and require careful targeted examination. Fetal heart assessment is typically performed at 18 and 22 weeks of gestation, but certain forms of congenital heart disease can even be found in the early stages of pregnancy, while other congenital heart diseases can appear or be discovered later.

[0005] Therefore, there is a need for a method to more accurately identify and assess CHD in antenatal ultrasound examinations.

[0006] EP3590436A1 discloses a method of identifying from a plurality of ultrasound images acquired during an ultrasound examination of a subject, an ultrasound image most suitable for analyzing a predetermined anatomical structure of the subject. According to the method described therein, an image similarity index between the ultrasound image and a reference image is computed for each of the plurality of ultrasound images. Next, for the (one or more) ultrasound image(s) having at least the best image similarity index, a biometric parameter is computed from the ultrasound image and a biometric parameter determined from the ultrasound image and a corresponding biometric parameter derived from the body examination are computed. The best ultrasound image is then selected based on the biometric similarity index. SUMMARY

[0007] The present invention is defined by the claims.

[0008] According to an example of an aspect of the present invention, there is provided a method for deriving a biometric parameter of a fetal heart, the method comprising:

[0009] acquiring a plurality of ultrasound images of a region of interest, wherein the region of interest comprises a fetal heart;

[0010] comparing the plurality of ultrasound images to a predefined clinical view;

[0011] selecting a set of ultrasound images related to the predefined clinical view based on the comparison, wherein the set of ultrasound images represents at least one cardiac cycle;

[0012] detecting anatomical landmarks of the fetal heart within ultrasound images of the set of ultrasound images;

[0013] detecting or tracking the anatomical landmarks of the fetal heart across the set of ultrasound images; and

[0014] deriving a biometric parameter of the fetal heart based on the detected or tracked anatomical landmarks from one or more ultrasound images of the set of ultrasound images.

[0015] The method provides a way to accurately assess the biometric parameter(s) of the fetal heart.

[0016] The fetal heart is generally difficult to study due to its motion and high variability.

[0017] By automatically selecting a set of ultrasound images corresponding to a desired clinical view over a cardiac cycle and tracking anatomical landmarks across the set of images, the biometric parameter of the fetal heart can be accurately derived.

[0018] In one embodiment, tracking the anatomical landmarks comprises:

[0019] automatically detecting the anatomical landmarks in the ultrasound images using a dynamic model of the fetal heart; and

[0020] detecting and tracking the anatomical landmarks across the set of ultrasound images using a dynamic motion model of the fetal heart.

[0021] In this way, the anatomical landmarks can be tracked with point precision, thereby improving the accuracy of the derived biometric parameter.

[0022] In a further embodiment, the plurality of ultrasound images comprises:

[0023] two-dimensional ultrasound images, wherein the tracking points of the dynamic motion model are derived from images; or

[0024] three-dimensional ultrasound images, wherein the tracking points of the dynamic motion model are derived from volumes;

[0025] In one embodiment, the method comprises detecting a plurality of anatomical landmarks, tracking the plurality of anatomical landmarks across the set of ultrasound images, and deriving a biometric parameter of the fetal heart based on the plurality of tracked anatomical landmarks.

[0026] By tracking a plurality of anatomical landmarks, a larger variety of biometric parameters can be derived with higher accuracy.

[0027] In one embodiment, the plurality of ultrasound images are two-dimensional ultrasound images, and wherein the method comprises deriving a three-dimensional biometric parameter based on the plurality of tracked anatomical landmarks.

[0028] In this way, a three-dimensional biometric parameter, such as a heart chamber volume, can be accurately derived from two-dimensional ultrasound images.

[0029] In one embodiment, the predefined clinical view comprises one or more of:

[0030] an abdominal view;

[0031] a four-chamber view;

[0032] a left ventricular outflow tract view;

[0033] a right ventricular outflow tract view;

[0034] a three-vessel view;

[0035] a three-vessel trachea view;

[0036] aortic arch view; and

[0037] ductal arch view.

[0038] In one embodiment, the predefined clinical view comprises a plurality of views.

[0039] In a further embodiment, the method comprises bookmarking the two-dimensional ultrasound images at a common point in the cardiac cycle for each of the plurality of views.

[0040] In this way, from a regular two-dimensional ultrasound scan, the fetal heart can be observed from a plurality of different viewing planes at the same point in the cardiac cycle.

[0041] In one embodiment, the method further comprises bookmarking an ultrasound image of interest based on the derived biometric parameter.

[0042] In this way, images related to the biometric parameter can be bookmarked for reference.

[0043] In one embodiment, the method further comprises bookmarking a movie playback of interest based on the derived biometric parameter.

[0044] In this way, a series of images related to the biometric parameter (e.g., one cardiac cycle of a given view, such as a four-chamber view or all relevant views in a cardiac cycle) can be bookmarked for reference.

[0045] In one embodiment, the biometric parameter is derived from one or more of:

[0046] an abdominal parameter;

[0047] a thoracic parameter;

[0048] an atrial parameter;

[0049] a ventricular parameter;

[0050] an arterial parameter;

[0051] a wall parameter, and

[0052] a valve parameter.

[0053] In one embodiment, the method further comprises:

[0054] identifying a gating position within an ultrasound image of the set of ultrasound images to place a Doppler gate;

[0055] tracking the gating position across the set of ultrasound images based on the anatomical landmark; and

[0056] automatically positioning a Doppler gate at the tracked gating position.

[0057] In this way, color Doppler data of a fetal heart can be automatically acquired. For example, a valve or septum of the heart can be tracked across the set of images to ensure that the Doppler gate is maintained within the correct valve or septum. For example, a gate can be placed on the ventricular septum to assess blood flow directly between the left and right ventricles due to holes in the septum.

[0058] In one embodiment, the method further comprises performing automatic M-mode data collection from the set of ultrasound images, wherein performing automatic M-mode data collection comprises:

[0059] automatically defining one or more sets of beams on the set of ultrasound images based on the tracked anatomical structure;

[0060] collecting M-mode data along the beam lines; and

[0061] tracking the beam line at the anatomical location based on the tracked anatomical feature across the cardiac cycle.

[0062] In this way, accurate data can be obtained relating to the motion along a given line at a given anatomical location for further analysis. M-mode data can for example identify arrhythmias.

[0063] According to an example of an aspect of the application, there is provided a computer program comprising computer program code means which is adapted, when said computer program is run on a computer, to implement the method as described above.

[0064] According to an example of an aspect of the application, there is provided a system for deriving a biometric parameter of a fetal heart, the system comprising a processor adapted to:

[0065] acquire a plurality of ultrasound images of a region of interest, wherein the region of interest comprises a fetal heart;

[0066] compare the plurality of ultrasound images to a predefined clinical view;

[0067] select a set of ultrasound images related to the predefined clinical view based on the comparison, wherein the set of ultrasound images represents at least one cardiac cycle;

[0068] detect anatomical landmarks of the fetal heart within the set of ultrasound images;

[0069] track the anatomical landmarks of the fetal heart in the set of ultrasound images; and

[0070] derive a biometric parameter of the fetal heart based on the tracked anatomical landmarks.

[0071] These and other aspects of the application will be apparent from and elucidated with reference to the embodiments described hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0072] Examples of the application will now be described in detail with reference to the accompanying drawings, in which:

[0073] Figure 1 An ultrasound diagnostic imaging system for explaining the general operation is shown;

[0074] Figure 2 A method of the application is shown; and

[0075] Figure 3 A first ultrasound image of a fetal heart at end-systole and a second ultrasound image at end-diastole are shown. DETAILED DESCRIPTION

[0076] The present application will be described with reference to the accompanying drawings.

[0077] It should be understood that the detailed description and specific examples, while indicating exemplary embodiments of apparatuses, systems and methods, are intended for purposes of illustration only and are not intended to limit the scope of the present application. These and other features, aspects, and advantages of the apparatuses, systems and methods of the present application will become better understood from the following description, appended claims, and accompanying drawings. It should be understood that the drawings are diagrammatic and schematic and are not drawn to scale. It should also be understood that the use of the same reference symbols in different drawings indicates similar or identical features or steps.

[0078] The present application provides a method for deriving a biometric parameter of a fetal heart. The method comprises acquiring a plurality of ultrasound images of a region of interest, wherein the region of interest comprises a fetal heart, and comparing the plurality of ultrasound images to a predefined clinical view. A set of ultrasound images related to the predefined clinical view is selected based on the comparison, wherein the set of ultrasound images represents at least one cardiac cycle.

[0079] Anatomical landmarks of the fetal heart are detected within ultrasound images of the set of ultrasound images and the anatomical landmarks of the fetal heart are detected or tracked across the set of ultrasound images. A biometric parameter of the fetal heart is then determined based on the anatomical landmarks detected or tracked from one or more ultrasound images of the set of ultrasound images.

[0080] Various ultrasound measurements, such as biparietal diameter (BPD), crown-rump length (CRL), head circumference (HC), abdominal circumference (AC) and femur length (FL) can be used to estimate gestational age (GA) of a fetus. These measurements are all based on body growth (mass or proportions) which is affected by genetic variations (e.g. fetal head size and shape), gender and inherent variability in the growth process of the fetus. Fetal heart rate and fetal heart valve intervals from fetal echocardiograms can be used as surrogate measures for estimating GA and have been shown to correlate with CRL.

[0081] Fetal echocardiography is a detailed assessment of cardiac structure and function, typically involving a continuous segmental analysis of the fetal heart using up to 8 recommended views. The recommended views can include: (a) abdominal view (ABDO); (b) four-chamber view (4C); (c) left ventricular outflow tract (LVOT); (d) right ventricular outflow tract (RVOT); (e) three-vessel view (3VV); (f) three-vessel trachea view (3VT); (g) aortic arch (AA); (h) ductal arch (DA). The segmental analysis includes a preliminary assessment of fetal right / left orientation, followed by assessment of the following segments and their relationships: meso-abdominal position, including stomach position and apex position; atrial assessment, including position, systemic and pulmonary venous connections, venous anatomy, atrial anatomy (including septum), and foramen ovale; ventricular assessment, including position, atrial connections, ventricular anatomy (including septum), relative and absolute size, function, and pericardium; great artery assessment (aortic, pulmonary trunk, and branch arteries, and ductal artery), including position relative to trachea, ventricular connections, and vessel size, patency, and flow (velocity and direction); atrioventricular junction, including anatomy, size, and function of atrioventricular (e.g., mitral and tricuspid) valves; and ventriculoarterial junction assessment, including anatomy, size, and function of semilunar (e.g., aortic and pulmonary) valves, including assessment of subpulmonary and subaortic regions.

[0082] However, the fetal heart is an organ that is difficult to measure using typical echocardiography techniques.

[0083] The general operation of an example ultrasound system will first be described Figure 1 with emphasis on the signal processing functions of the system, as the present invention relates to the processing of signals measured by a transducer array.

[0084] The system includes an array transducer probe 4 having a transducer array 6 for transmitting ultrasound and receiving echo information. The transducer array 6 can include CMUT transducers; piezoelectric transducers made 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 capable of scanning a 2D plane or a three-dimensional volume of interest. In another example, the transducer array can be a ID array.

[0085] The transducer array 6 is coupled to a microbeamformer 12, which controls the signal reception of the transducer elements. The microbeamformer is capable of at least partially beamforming the signals received by sub-arrays (generally referred to as “groups” or “tiles”) of the transducers, as described in U.S. Patents US 5997479 (Savord et al.), US 6013032 (Savord), and US 6623432 (Powers et al.).

[0086] It should be noted that the microwave beamformer is entirely optional. Furthermore, the system includes a transmit / receive (T / R) switch 16, the microwave beamformer 12 can be coupled to the transmit / receive switch 16 and switches the array between transmit and receive modes, and protects the main beamformer 20 from high energy transmit signals in the case where the microwave beamformer is not used and the transducer array is operated directly by the main system beamformer. Transmission of an ultrasound beam from the transducer array 6 is coupled through the T / R switch 16 to a transducer controller 18 of the microwave beamformer and main transmit beamformer (not shown) which can receive input from operation of a user interface or control panel 38 by a user. The controller 18 can include transmit circuitry arranged to drive the transducer elements of the array 6 (directly or via the microwave beamformer) during transmit mode.

[0087] In a typical line-by-line imaging sequence, the beamforming system within the probe can operate as follows. During transmission, the beamformer (which can be the microwave beamformer or the main system beamformer depending on the implementation) activates a transducer array or a sub-aperture of the transducer array. The sub-aperture can be a one-dimensional line of transducers or a two-dimensional patch of transducers within a larger array. In transmit mode, the focusing and steering of the ultrasound beam produced by the array or sub-aperture of the array is controlled as described below.

[0088] Upon receiving backscattered echo signals from the subject, the received signals are subjected to receive beamforming (as described below) to align the received signals and, in the case of a sub-aperture, to shift the sub-aperture, for example, by one transducer element. The shifted sub-aperture is then activated and the process is repeated until all transducer elements of the transducer array have been activated.

[0089] For each line (or sub-aperture), the total received signal associated with the line used to form the final ultrasound image will be the sum of the voltage signals measured by the transducer elements of the given sub-aperture during the receive period. The resulting line signal, after the beamforming process below, is commonly referred to as radio frequency (RF) data. Each line signal (RF data set) generated by the individual sub-apertures is then subjected to additional processing to generate a line of the final ultrasound image. The amplitude of the line signal as a function of time will contribute to the brightness of the ultrasound image as a function of depth, with high amplitude peaks corresponding to bright pixels (or collections of pixels) in the final image. Peaks occurring near the beginning of the line signal will indicate echoes from shallow structures, while peaks occurring later in the line signal will indicate echoes from structures of increasing depth within the subject.

[0090] 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 (perpendicular to the transducer array) or at different angles for a wider field of view. The steering and focusing of the transmit beam can be controlled according to the transducer element actuation times.

[0091] Two methods can be distinguished in conventional ultrasound data acquisition: plane wave imaging and "beam steering" imaging. The difference between the two methods is the presence of beamforming in the transmit mode ("beam steering" imaging) and / or in the receive mode (plane wave imaging and "beam steering" imaging).

[0092] Looking first at the focusing function, the transducer array generates a plane wave by activating all transducer elements at the same time, which diverges as it passes through the subject. In this case, the beam of ultrasound waves remains unfocused. By introducing a position-dependent time delay to the activation of the transducers, the wavefront of the beam can be made to converge on a desired point, which is called a focal zone. A focal zone is defined as a point where the lateral beam width is less than half the transmitted beam width. In this way, the lateral resolution of the final ultrasound image is improved.

[0093] For example, if the time delay causes the transducer elements to be activated in series starting from the outermost elements and ending at the central element(s) of the transducer array, a focal region will be formed at a given distance from the probe, in line with the central element(s). The distance of the focal region from the probe will vary according to the time delay between each subsequent round of transducer element activation. After the beam passes through the focal zone, it will start to diverge, forming a far-field imaging region. It should be noted that for focal zones located close to the transducer array, the ultrasound beam will diverge rapidly in the far field, resulting in beam width artifacts in the final image. Generally, due to the large overlap in the ultrasound beam, little detail is shown in the near field between the transducer array and the focal zone. Thus, changing the position of the focal zone can result in a significant change in the quality of the final image.

[0094] It should be noted that in the transmit mode, only one focus can be defined unless the ultrasound image is divided into multiple focal regions (each focal region can have a different transmit focus).

[0095] Additionally, when receiving echo signals from inside the subject, the inverse of the above process can be performed to perform receive focusing. In other words, the incoming signals can be received by the transducer elements and undergo electronic time delays before being passed into the system for signal processing. The simplest example is called delay-and-sum beamforming. The receive focusing of the transducer array can be adjusted dynamically according to time.

[0096] Now turning to the function of beam steering, by applying the correct time delay to the transducer elements, a desired angle can be imparted on the ultrasound beam as it leaves the transducer array. For example, by activating the transducers on a first side of the transducer array, and then activating the remaining transducers in an order that ends on the opposite side of the array, the wavefront of the beam will be tilted toward the second side. The size of the steering angle relative to the normal of the transducer array depends on the size of the time delay between the activation of subsequent transducer elements.

[0097] In addition, the steered beam can be focused, where the total time delay applied to each transducer element is the sum of the focusing and steering time delays. In this case, the transducer array is referred to as a phased array.

[0098] In the case where the CMUT transducers require activation of a DC bias voltage, the transducer controller 18 can be coupled to control a DC bias controller 45 of the transducer array. The DC bias controller 45 sets the bias voltage(s) applied to the CMUT transducer elements.

[0099] For each transducer element of the transducer array, an analog ultrasound signal, often referred to as channel data, enters the system through a receive channel. In the receive channel, a partially beamformed signal is produced from the channel data by the microbeamformer 12 and then passed to the main receive beamformer 20, where the partially beamformed signals from the individual transducer tiles 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 can include additional functionality. For example, the main beamformer 20 can have 128 channels, each of which receives the 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.

[0100] The beamformed receive signal is 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, for separating linear and nonlinear signals in order to identify nonlinear (higher harmonics of the fundamental frequency) echo signals returned from tissue and microbubbles. The processor can also perform signal enhancements such as speckle reduction, signal compounding, and noise cancellation. The bandpass filter in the signal processor can be a tracking filter, where its passband slides from a higher frequency band to a lower frequency band as the echo signals are received from increasing depths, thereby rejecting noise at higher frequencies from greater depths, which typically has no anatomical information.

[0101] The beamformers for transmission and for reception are implemented in different hardware and can have different functionalities. Of course, the receiver beamformers are designed to take into account the characteristics of the transmit beamformers. For simplicity, in Figure 1 only the receiver beamformers 12, 20 are shown in Fig. 1. Throughout the system, there will also be transmit chains with transmit micro-beamformers and main transmit beamformers.

[0102] The function of the micro-beamformer 12 is to provide an initial combination of signals in order to reduce the number of analog signal paths. This is typically performed in the analog domain.

[0103] The final beamforming is done in the main beamformer 20 and is typically after digitization.

[0104] The transmit and receive channels use the same transducer array 6 with a fixed frequency band. 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 (this is the classical approach) or by using bandpass processing it can extract only the bandwidth containing the desired information (e.g. the harmonics of the fundamental harmonic).

[0105] The RF signals 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 signals to image 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 whose amplitude 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 position of the relevant amplitude measurement along the RF signal and the line (beam) number of the RF signal. The B-mode image of this structure can be formed in either a harmonic or fundamental image mode or a combination of both as described in US patent US 6283919 (Roundhill et al.) and US patent US 6458083 (Jago et al.). The Doppler processor 28 can process time-discrete signals originating from tissue motion and blood flow for detecting moving matter, e.g. the flow of blood cells in the image field. The Doppler processor 28 typically includes a wall filter with parameters set to pass or reject echoes returned from selected types of material in the body.

[0106] The structural and motion signals generated by the B-mode and Doppler processors are coupled to a scan converter 32 and a multiplanar reformatter 44. The scan converter 32 arranges the echo signals in the desired image format from the spatial relationships in which the echo signals were received. In other words, the role of the scan converter is to convert the RF data from the cylindrical coordinate system in which the ultrasound image was acquired to a Cartesian coordinate system appropriate for displaying the 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 magnitude of the RF signal received from that location. For example, the scan converter can arrange the echo signals into a two-dimensional (2D) sector-shaped format, or a cone three-dimensional (3D) image. The scan converter can superimpose on the B-mode structural image colors corresponding to motion of points in the image field at which a given color Doppler-estimated velocity can produce. The combined B-mode structural image and color Doppler image depict motion of tissue and blood flow within the structural image field. The multiplanar reformatter converts echoes received from points in a common plane in a volumetric region of the body into an ultrasound image of that plane, as described in U.S. Patent 6,443,896 (Detmer). The volume renderer 42 converts echo signals of a 3D data set into a projected 3D image as seen from a given reference point, as described in U.S. Patent 6,530,885 (Entrekin et al.).

[0107] The 2D or 3D images are coupled from the scan converter 82, the multiplanar reformatter 44, and the volume renderer 42 to the image processor 30 for further enhancement, buffering, and temporary storage for display on the image display 40. The imaging processor can be adapted to remove certain imaging artifacts from the final ultrasound image, such as: acoustic shadows, e.g., caused by strongly attenuating or refracting objects; post-echoes, e.g., caused by weakly attenuating objects; reverberation artifacts, e.g., highly reflective tissue interfaces located in close proximity; and the like. In addition, the image processor can be adapted to process specific speckle reduction functions in order to improve the contrast of the final ultrasound image.

[0108] In addition to being used for imaging, the blood flow values generated by the Doppler processor 28 and the tissue structural information generated by the B-mode processor 26 are coupled to a quantification processor 34. The quantification processor generates measures of different flow conditions (e.g., volume rate of blood flow) and structural measurements (e.g., size of an organ and gestational age). The quantification processor 46 can receive output from the user control panel 38, such as a point in the anatomy of the image to be measured.

[0109] The output data PUHC from the quantification processor is coupled to a graphics processor 36 for rendering measurement graphics and values on a display 40 together with the images, and for outputting audio from the display device 40. The graphics processor 36 can also generate graphics overlays for display with the ultrasound images. These graphics overlays can include standard identifying information such as the patient's name, the date and time, the imaging parameters, etc. for the images. For these purposes, the graphics processor receives input from a user interface 38, such as the patient's name. The user interface is also coupled to the transmit controller 18 to control the ultrasound signal generation from the transducer array 6, and thus the images generated by the transducer array and 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.

[0110] The user interface can also be coupled to the multiplanar reformatter 44 to select and control the planes of the multiple multiplanar reformatted (MPR) images, which can be used to perform the measurements of the quantification in the image field of the MPR images.

[0111] Figure 2 A method 100 for deriving biometric parameters of a fetal heart is shown.

[0112] The method starts in step 110 with acquiring a plurality of ultrasound images of a region of interest, wherein the region of interest comprises a fetal heart. The ultrasound images can comprise 2D ultrasound images and / or 3D ultrasound images.

[0113] In step 120, the plurality of ultrasound images is compared to a predefined clinical view. As mentioned above, a number of different clinical views are considered in a typical fetal echocardiogram. For example, the predefined clinical view can comprise one or more of the following: abdominal view; four-chamber view; left ventricular outflow tract view; right ventricular outflow tract view; three-vessel view; three-vessel trachea view; aortic arch view; and ductal arch view.

[0114] In step 130, a set of ultrasound images related to the predefined clinical view is selected based on the comparison, wherein the set of ultrasound images represents at least one cardiac cycle.

[0115] The set of ultrasound images is selected to match the predefined clinical view as closely as possible. The set of ultrasound images is selected to cover at least one cardiac cycle of the fetal heart, since a particular predefined clinical view requires the fetal heart to be in a given point in the cardiac cycle in order to be accurate. For example, an accurate four-chamber view requires the heart valves to be closed.

[0116] In step 140, anatomical landmarks of the fetal heart are detected within the ultrasound images of the set of ultrasound images.

[0117] For example, anatomical landmarks can be identified based on a pose point (also referred to as keypoint in 2D images) model of a fetal heart view. A desired point in the cardiac cycle for a given clinical view can be selected from the set of ultrasound images by tracking the pose points in real-time. The anatomical landmarks associated with the pose points can include, for example, the aortic arch and ductal arch, the inferior and superior vena cava, the trachea, etc. Further, multiple anatomical landmarks can be considered and detected within the ultrasound images.

[0118] In step 150, the anatomical landmarks of the fetal heart are detected or tracked across the set of ultrasound images.

[0119] Tracking the anatomical landmarks can be performed, for example, by automatically detecting the anatomical landmarks in the ultrasound images with a dynamic model of the fetal heart and detecting and tracking the anatomical landmarks across the set of ultrasound images with a dynamic motion model of the fetal heart.

[0120] In an example of the dynamic model, the model can estimate the cardiac cycle directly from B-mode ultrasound data and is used to detect conditions such as arrhythmias. The dynamic model can also be used to automatically place and track Doppler gates, dynamically and accurately following the motion of the heart to examine valves, veins, arteries, septum, and oval foramen, potentially detecting related anatomical and functional abnormalities. The placement of Doppler gates will be discussed further below.

[0121] Further, in an example of the dynamic model, the model can also detect functional abnormalities, such as a dysfunctional valve, the membrane of the oval foramen flapping in the wrong atrium, etc.

[0122] In step 160, a biometric parameter of the fetal heart is derived based on the anatomical landmarks detected or tracked from one or more of the set of ultrasound images. The biometric parameter can be derived from one or more of the following: an abdominal parameter; a thoracic parameter; an atrial parameter; a ventricular parameter; an arterial parameter; a wall parameter; and a valve parameter.

[0123] The method provides a method of generating a fetal heart fingerprint during a real-time scan. The fingerprint can be constructed from key anatomical landmarks detected in real-time on the fetal heart structure from real-time ultrasound data. In this way, a fetal heart model can be built from which multiple parameters such as biometrics, structural and functional abnormalities can be detected. The model can be used to detect optimal scan planes and fetal screening views during a routine ultrasound scan of the fetal heart.

[0124] The method can operate on any ultrasound images. For example, the plurality of ultrasound images can comprise 2D ultrasound images, in which case the tracking points of the dynamic motion model are derived from the 2D images. In another example, the plurality of ultrasound images can comprise 3D ultrasound images, in which case the tracking points of the dynamic motion model are derived from the volumes.

[0125] Figure 3 A first ultrasound image 210 is shown of a fetal heart at end-systole (end of ejection phase) and a second ultrasound image 220 at end-diastole (end of ventricular filling).

[0126] According to an aspect of the application, the first and second ultrasound images comprise tracking points 230 corresponding to anatomical landmarks within the fetal heart. In Figure 3 In the image shown, the tracking points 230 have been used to define a cardiac axis 240, which is commonly used to derive biometrics from the fetal heart.

[0127] The cardiac axis (CAx) on a normal four-chamber fetal heart image is expected to be 45° ± 15°. The axis is defined as the line from the spine to the apex.

[0128] The nature of the abnormal CAx and CAx shift within the fetal cardiac cycle depends on the type of CHD. Zhao et al., Cardiac axis shift within the cardiac cycle of normal fetuses and fetuses with congenital heart defect, Ultrasound in Obstetrics and Gynecology (2014) found that, at 18 to 26 weeks of gestation, the mean CAx of normal fetal hearts was 45.9 ± 8.5° at end-systole and 38.3 ± 8.4° at end-diastole (P < 0.001). The mean CAx of fetuses with CHD was reported in this study to be 53.4 ± 17.8° at end-systole and 47.5 ± 17.3° at end-diastole (P < 0.001), resulting in a mean difference of 7.6 ± 3.2°. However, in some forms of CHD, such as left heart hypoplasia syndrome and transposition of the great arteries L, the CAx is greater at end-diastole than at end-systole, by more than 5°.

[0129] In Hornberger et al., Re: Cardiac axis shift within the cardiac cycle of normal fetuses and fetuses with congenital heart defect, Ultrasound in Obstetrics and Gynecology (2015), the authors conclude that the cardiac axis shows minimal variability in the control group at end-systole and is more abnormal in CHD patients at end-systole (abnormal defined as < 25° or > 65°), suggesting that end-systole can be the best time to assess the cardiac axis in the cardiac cycle. The study concludes that the heart pivots or swings from diastole to systole. The authors investigate the unique mechanical properties of the myocardium and the circumferential fibers of the ventricle, as well as the twisting mechanics that cause the swing and distinguish the mode of contraction that leads to the Ebstein syndrome of left heart hypoplasia.

[0130] The automatically tracked points 230 at the junctions of the valves, apex, and aorta can be used to quantify the cardiac motion mechanics throughout the cardiac cycle, improving the understanding of the biomechanical abnormalities in CHD.

[0131] In addition to the cardiac axis, the tracked points can be used to assess the atrial and ventricular dimensions. In particular, the tracked points at the left atrium, right atrium, mitral valve junction, and tricuspid valve junction can be used to provide the dimensions of the left and right atria. For example, the quantification of the atrial dimensions of the fetal heart can be used to identify Ebstein anomaly, which is characterized by an enlarged right atrium. In a similar manner, the tracked points at the left ventricle, right ventricle, mitral valve junction, and tricuspid valve junction can be used to predict the relative dimensions of the left and right ventricles, which are expected to be approximately equal. The tracked points can be tracked throughout all relevant phases of the cardiac cycle.

[0132] By monitoring the dimensions of the atria and ventricles over at least one cardiac cycle, a measure of the heart chamber function can be determined, which can also contribute to the biomeasurement parameters.

[0133] In some applications, 3D ultrasound imaging can not be possible; however, 3D biomeasurement parameters can still be derived from 2D ultrasound images based on multiple tracked anatomical landmarks.

[0134] In a regular clinical workflow, Simpson's rule can be used to measure volumes from 2D ultrasound images, which assumes that each ventricular slice is a cross-section of a cylinder and that the total volume is the sum of all cylinders present in the image. These measurements can then be plotted against gestational age (as determined by measuring biparietal diameter or fetal length). There exist standardized charts for right ventricular measurements against gestational age: for example, the left ventricular ratio. The above method not only provides an automated estimate of these biometrics, but also a more accurate 3D approximation of these biometrics at all stages of the cardiac cycle. This is possible because these points are tracked in all views and at all stages of the cardiac cycle, resulting in a more accurate patient-specific shape approximation.

[0135] In addition to the mitral and tricuspid valves, the fetal circulatory system uses shunts to bypass the lungs and liver, which are not fully developed yet. They are the foramen ovale, which transports blood from the right atrium of the heart to the left atrium, and the ductus arteriosus, which transports blood from the pulmonary artery to the aorta. Newborns with congenital heart disease can also have a persistent foramen ovale and / or ductus arteriosus. By tracking these points in the ultrasound images obtained, abnormal axes, right atrial enlargement, atrial septal defects, AV septal defects, truncus arteriosus, transposition of the great arteries, single ventricle, hypoplastic left heart syndrome, hypoplastic right heart syndrome, hypoplastic left heart syndrome, coarctation, valve abnormalities, etc. can be detected in prenatal ultrasound images.

[0136] Since the tracking points, valves, septa, and junctions can be tracked in real-time, the method can provide an automatic mode on the user interface to jump to the assessment of any given anatomical landmark, such as the foramen ovale or the mitral valve, and provide automation therein to set the focus / depth / preset parameters for the assessment by tracking these pose points. This can be helpful for less experienced sonographers or to speed up the examination time for fetal ultrasound screening.

[0137] The method can also include adding bookmarks to ultrasound images of interest based on the derived biometric parameters. For example, based on the derived biometric parameters, a user can wish to further investigate the parameters. By automatically adding bookmarks to one or more images, which form the basis of the derived biometric parameters, the user can have a better understanding of the source of the measurements. In addition to still ultrasound image frames, the method can also include adding bookmarks to movie loops of ultrasound images of interest based on the derived biometric parameters.

[0138] The ultrasound images can add bookmarks to multiple different clinical views of the same point in the cardiac cycle. By calculating the relative distances between the tracked points, a model can be used to identify consistent points of the bookmarks in different views of the cardiac cycle.

[0139] The method can further include identifying a gate position within an ultrasound image of the set of ultrasound images to place a Doppler gate, tracking the gate position across the set of ultrasound images based on an anatomical landmark, and automatically positioning a Doppler gate at the tracked gate position.

[0140] Doppler evaluation of the fetal heart can be used to assess the septum and valvular defects. Diastolic filling of the atrioventricular valves can be assessed by using color Doppler ultrasound measurements in an apical or basal approach. Pulse Doppler ultrasound can be used to observe the typical biphasic shape of the diastolic flow velocity waveform with an early diastolic peak velocity and a second peak during atrial contraction. On this plane, more common atrioventricular valve regurgitation at the tricuspid valve can be detected with color Doppler imaging during systole. Blood flow through the foramen ovale can be seen in a transverse approach to the four-chamber view. Color Doppler imaging can be used to confirm physiologic right-to-left shunting and visualization of the pulmonary veins entering the left atrium.

[0141] The Doppler acquisition described above requires placement of a Doppler gate at the relevant location for each of the evaluations described above. The gate position can be automatically detected using the tracking points described above. For example, the tracking points can be used to place a color Doppler window on the four-chamber view. In addition, the tracking points can also be used to place a pulse Doppler gate on the valve to measure atrioventricular flow.

[0142] In addition, the method can further include performing automatic M-mode data collection from the set of ultrasound images. The automatic M-mode data collection can include automatically defining one or more sets of beam lines on the set of ultrasound images based on the tracked anatomical structures and collecting M-mode data along the beam lines based on tracking anatomical features across the cardiac cycle.

[0143] The dynamic model described above can be used to automatically define M-mode data collection, where lines are drawn between relevant selected structures, such as: atria; ventricles; left atrium and left ventricle; right atrium and right ventricle; and left / right cross. Because of the known anatomical context of the tracking points, combined with the anatomical intelligence of the model, the data obtained can be used to detect arrhythmias and accurately identify the source of the arrhythmia.

[0144] The method described above can be integrated into any suitable ultrasound imaging system or other processing system to provide a workflow-guided cardiac evaluation for analyzing the fetal heart by identifying key anatomical landmarks and tracking them in real-time to enable independent evaluation of structural and functional aspects of each of these structures. For example, such a system can include a user interface for one-key evaluation of fetal heart structures to identify fetal heart defects.

[0145] In other words, fetal heart evaluation can be simplified and a guided workflow can be implemented.

[0146] Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the claims. In the claims, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality. Although specific measures are recited in mutually different dependent claims, this does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.

Claims

1. A method (100) for deriving biometric parameters of a fetal heart, the method comprising: Multiple ultrasound images of the region of interest (110) are acquired, wherein the region of interest includes the fetal heart; The multiple ultrasound images are compared with a predefined clinical view (120); Based on the comparison, a set of ultrasound images related to the predefined clinical view is selected (130), wherein the set of ultrasound images represents at least one cardiac cycle; Detect (140) anatomical landmarks of the fetal heart within the ultrasound images in the set of ultrasound images; The anatomical landmarks of the fetal heart are detected (150) across the set of ultrasound images; and (160) The biometric parameters of the fetal heart are derived based on anatomical landmarks detected or tracked from one or more ultrasound images in the set of ultrasound images.

2. The method (100) according to claim 1, wherein, Tracking the anatomical landmarks includes: The dynamic model of the fetal heart is used to automatically detect anatomical landmarks in ultrasound images; and The anatomical landmarks are detected and tracked across the set of ultrasound images using the dynamic motion model of the fetal heart.

3. The method (100) according to claim 2, wherein, The multiple ultrasound images include: 2D ultrasound images, wherein the tracking points of the dynamic motion model are derived from the images; or 3D ultrasound images, wherein the tracking points of the dynamic motion model are derived from volume.

4. The method (100) according to any one of claims 1 to 3, wherein, The method includes: detecting multiple anatomical landmarks, tracking the multiple anatomical landmarks across the set of ultrasound images, and deriving biometric parameters of the fetal heart based on the tracked multiple anatomical landmarks.

5. The method (100) according to claim 4, wherein, The multiple ultrasound images are 2D ultrasound images, and the method includes deriving 3D biometric parameters based on the multiple tracked anatomical landmarks.

6. The method (100) according to any one of claims 1 to 3, wherein, The predefined clinical view includes one or more of the following: Abdominal view; Four-cavity view; View of the left ventricular outflow tract; View of the right ventricular outflow tract; Three-vessel view; Three-vessel tracheal view; Aortic arch view; as well as View of the duct arch.

7. The method (100) according to any one of claims 1 to 3, wherein, The predefined clinical view includes multiple views.

8. The method (100) according to claim 7, wherein, The method includes bookmarking 2D ultrasound images at common points in the cardiac cycle for each of the plurality of views.

9. The method (100) according to any one of claims 1 to 3, wherein, The method also includes bookmarking ultrasound images of interest based on the derived biometric parameters.

10. The method (100) according to any one of claims 1 to 3, wherein, The method also includes bookmarking movie playbacks of ultrasound images of interest based on the derived biometric parameters.

11. The method (100) according to any one of claims 1 to 3, wherein, The biometric parameters are derived from one or more of the following: Abdominal parameters; Breast parameters; Atrial parameters; Ventricular parameters; Arterial parameters; Wall parameters; and Valve parameters.

12. The method (100) according to any one of claims 1 to 3, wherein, The method further includes: Identify the location of gates within the set of ultrasound images to place Doppler gates; The location of the gate is tracked across the set of ultrasound images based on the anatomical landmarks; and The Doppler gate is automatically positioned at the tracked gate location.

13. The method (100) according to any one of claims 1 to 3, wherein, The method further includes performing automatic M-mode data collection from the set of ultrasound images, wherein performing automatic M-mode data collection includes: Based on the tracked anatomical structures, one or more beamlines are automatically defined on the set of ultrasound images; M-mode data is collected along the beamline; and The beamline at the anatomical location is tracked based on the anatomical features observed across the cardiac cycle.

14. A system for deriving biometric parameters of a fetal heart, the system comprising a processor adapted to: Multiple ultrasound images (210, 220) of the region of interest were acquired (110), among which, The region of interest includes the fetal heart; The multiple ultrasound images are compared with a predefined clinical view (120); Based on the comparison, a set of ultrasound images related to the predefined clinical view is selected (130), wherein the set of ultrasound images represents at least one cardiac cycle; Detect (140) anatomical landmarks (230) of the fetal heart within the set of ultrasound images; The anatomical landmarks of the fetal heart are traced (150) across the set of ultrasound images; and The biometric parameters of the fetal heart (160) are derived based on the tracked anatomical landmarks.

15. A computer program product storing a computer program including computer program code modules, wherein when the computer program is run on a processor of the system according to claim 14, the computer program code modules are adapted to implement the method according to any one of claims 1 to 13.

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