Multi-plane-based cardiac wall motion detection in medical ultrasound

Abstract

To improve the data quality in detecting cardiac wall motion.SOLUTION: Regions of abnormal wall motion are detected from a view. The scan settings are then changed to focus scanning on each region, providing improved data such as data with more speckles being present. The scan settings may include changing an orientation of a scan plane, reducing out-of-plane motion, and / or increasing speckle content. The improved data is used to more accurately determine strain.SELECTED DRAWING: Figure 1

Description

[Background technology]

[0001] The present embodiment relates to medical ultrasound imaging, in particular cardiac wall motion analysis is performed using ultrasound.

[0002] Two-dimensional (2D) B-mode echocardiography provides important information for cardiac surgery, such as volume, diastolic function, right ventricular function, hemodynamics, and valvular regurgitation. Furthermore, the detection of heart failure is facilitated by the assessment of global longitudinal and regional strain in the heart wall. Patients' myocardial mechanics, ischemic heart disease, cardiomyopathies, left ventricular (LV) diastolic dysfunction, and asymptomatic myocardial dysfunction can be detected.

[0003] Current clinical tools require sonographers to acquire multiple standard views of the heart chambers, such as A2C, A3C, A4C, and longitudinal views, while optimizing image quality, maximizing frame rate, and minimizing shortening, all of which are important to reduce measurement variability. Global and regional distortions for each view are calculated by first creating an initial contour at the myocardial border, and then this contour is tracked to every frame using speckle. A 17-segment bull's-eye model (or 16 or 18 segments) is constructed from the six regional distortions for each view. Specifically, a quantitative wall motion score can be assigned to each segment. Typically, a four-grade scoring is applied based on the segment model: (1) normal or hyperkinetic, (2) hypokinetic, (3) akinetic, and (4) dyskinetic.

[0004] Unfortunately, the semi-quantitative wall motion score for each segment is not always reliable. The accuracy of regional distortion can be adversely affected by speckle tracking problems due to insufficient imaging at certain locations. It can be difficult to select a set of capture parameters that results in capturing wall motion in all areas of a standard view. If there is significant motion in the out-of-plane direction at a certain location, it can be difficult to track the speckles. Summary of the Invention

[0005] First, the preferred embodiments described below include a method, system, and non-transitory computer readable medium for detecting cardiac wall motion. To improve data quality, regions of abnormal wall motion are detected from the views. Scan settings are then changed to focus the scan on each region, providing improved data, such as data with more speckle present. Scan settings may include changing the orientation of the scan plane, reducing out-of-plane motion, and / or increasing speckle content. The improved data is used to more accurately determine strain.

[0006] In a first aspect, a method is provided for detecting cardiac wall motion with a medical ultrasound scanner. The medical ultrasound scanner first performs a first scan of a first plane through a patient's heart using first values ​​of scan settings. The first plane is scanned at various times to provide first ultrasound data representative of the heart at the various times. Abnormal wall motion is detected from the first ultrasound data. The medical ultrasound scanner performs a second scan of a region of abnormal wall motion in a second plane using second values ​​of scan settings. The second settings are different from the first values ​​of scan settings. The second plane is different from the first plane, and the second scan results in second ultrasound data. Distortion is determined for the abnormal wall motion from the second ultrasound data. An image represents the distortion.

[0007] In one embodiment, a standardized view of the heart is scanned first. The second scan has a smaller field of view that just covers the area of ​​abnormal wall motion, the area being smaller than the field of view in the standardized view.

[0008] In another embodiment, a machine learning model detects abnormal wall motion.

[0009] In the scan, various scan settings such as gain, contrast, depth, and / or field of view may be changed. For example, the field of view for the first scan is for the entire heart region (larger field of view) and the field of view for the second scan is a smaller field of view for the area around the abnormal motion. The scan settings may include the orientation of the plane, such as changing the scan settings to reorient from the first plane to the second plane. In one example, the scan settings of the second scanner configure a medical ultrasound scanner for generating speckle.

[0010] According to one embodiment, the second plane is selected based on the level of scattering of the heart wall and / or the amount of relative motion. The region of abnormality may be scanned along various planes, and one of the planes may be selected as more optimal compared to the other planes.

[0011] In one embodiment, the distortion is determined for a segment of a multi-segment model. The multi-segment model including the distortion is displayed. In another embodiment, the distortion is determined in multiple dimensions for a first face and a second face. A multi-dimensional distortion map is displayed.

[0012] The second scan and strain determination may be repeated for different regions corresponding to different locations of abnormal wall motion. The first scan, detection of abnormal wall motion, second scan, and strain determination may be repeated for different views of the heart (e.g., different standardized views). The strain from the different standardized views is then displayed.

[0013] In a second aspect, an ultrasound system for detecting cardiac wall motion is provided. An ultrasound scanner is configured to detect a location of a cardiac wall motion abnormality in a patient, scan the location at various values ​​of one or more settings, and determine a cardiac wall motion strain at the location from ultrasound data from the scan using at least one of the various values ​​of the one or more settings. A display is configured to display an image of the strain.

[0014] In one embodiment, the one or more settings are for scanning different planes such that the ultrasound scanner is configured to scan locations in different planes, hi another embodiment, the one or more settings are configured for the ultrasound scanner to scan locations for imaging with different levels of speckle.

[0015] In another embodiment, the ultrasound scanner is configured to scan the patient's heart with standardized views, detect locations from the standardized views, and scan the locations with a field of view that is smaller than the standardized views.

[0016] In a third aspect, a non-transitory computer readable storage medium has stored thereon data representing instructions executable by a programmed processor for detecting cardiac wall motion, the storage medium including instructions for scanning a patient's heart in a standardized view, detecting abnormal wall motion of the heart from the standardized view, acquiring B-mode data at a patch of the abnormal wall motion, determining a distortion from the B-mode data for the patch, and displaying a distortion map including a first segment of the distortion map having the distortion determined from the B-mode data for the patch and a second segment of the train map having another distortion determined from the standardized view.

[0017] According to one embodiment, the instructions for acquiring B-mode data in the patch include acquiring B-mode data with the patch in a different plane than the standardized view, the instructions further including selecting the different plane based on an amount of speckle or motion.

[0018] In another embodiment, the instructions to acquire B-mode data include acquiring B-mode data having a greater level of speckle in the patch than in a standardized view at the location of abnormal wall motion.

[0019] In yet another embodiment, the instructions further include repeating the scanning, detecting, acquiring, and determining for various standardized views. The instructions for displaying the distortion map include displaying a 16, 17, or 18 segment bull's eye view.

[0020] The following claims define the invention, and nothing in this section should be taken as a limitation on those claims. Further aspects and advantages of the present invention are described below in conjunction with the preferred embodiments. [Brief description of the drawings]

[0021] The components and figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. Moreover, with reference to the drawings, like reference characters indicate corresponding parts throughout the various views.

[0022] [Figure 1] FIG. 1 is a flow chart of one embodiment of a method for detecting cardiac wall motion.

[0023] [Diagram 2] FIG. 2 illustrates the detection of abnormal motion using a target scan for an area of ​​abnormal motion.

[0024] [Diagram 3] FIG. 3 illustrates various planes for scanning to acquire ultrasound data for strain determination.

[0025] [Figure 4] FIG. 4 is a block diagram of one embodiment of a system for detecting cardiac wall motion. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] Tracking acquisition assistance is provided, such as for fusion of 2D B-mode echocardiograms for 2D global and / or regional cardiac dynamics. The user is assisted in acquiring the best possible or sufficient B-mode echocardiogram data for quantifying global and / or regional strain, for example, by sampling in multiple planes for any region of abnormal wall motion.

[0027] Automated solutions calculate global and regional cardiac mechanics from 2B echo data. The analysis of distortions in these solutions is highly image quality dependent. One set of parameters or settings for the ultrasound scanner, such as gain settings, may not capture wall motion in all areas. For example, one or more gaps in the heart wall may appear due to lack of speckle, shadowing, and / or other artifacts.

[0028] To aid the user and / or automated solutions for better images in certain regions, the patch is scanned with various values ​​for scan settings. Regions with abnormal wall motion, whether due to actual abnormalities or poor scanning, are scanned again with various settings to improve the data by additional acquisitions and / or by better settings for scanning their location. In one approach, the various settings are for scanning the abnormal region along various scan planes. The field of view is shifted not only in area (smaller) and focus (within the area), but also in the plane being scanned. This multi-plane analysis of cardiac wall motion abnormalities from 2D echo can provide usable data for more accurate tracking and corresponding strain analysis. Improved data, as well as confidence measures, can be provided for tracking and image quality. Image quality is improved, especially in low confidence regions, by changing acquisition parameters, such as visually rotating the probe if necessary. Tracking may be performed again only in low confidence regions.

[0029] In one embodiment, the sonographer is assisted during 2D B-mode echocardiography acquisition to obtain good quality data in the patch and fuse the results for strain analysis. When the sonographer acquires 2D B-mode echocardiography images in standard orientation views such as A2C, the system first detects myocardial boundaries with abnormal wall motion and acquires more data (automatically or through user guidance) only for the regions of abnormal wall motion, for example, by adjusting input parameters and orientation planes. The system causes some probe rotation, either automatically and / or by guidance to the sonographer, to quantify the motion in multiple planes only in the vicinity of the abnormal wall motion in real time. Tracking is performed, for example, only in these corrected regions. The resulting calculated strain is collected or fused with previously obtained results from other regions. The multi-plane system may result in more robust strain analysis and less inter- and intra-user variability. Image quality is improved, especially for images acquired by less experienced sonographers. A framework is established for semi-autonomous or fully autonomous robotic device-based 2D B-mode echocardiography image acquisition.

[0030] FIG. 1 illustrates a method of detecting cardiac wall motion using a medical ultrasound scanner. Distortion and / or other indices of cardiac abnormalities and / or normal motion or operation are detected. Detection relies on acquiring additional data at various settings, such as various fields of view, gains, and / or scan planes, for regions that appear to have abnormal cardiac wall motion. By collecting this additional data at various values ​​to configure the scan, improved ultrasound data for distortion or other analysis in those regions is provided.

[0031] The method may be performed by the system of Figure 4, an ultrasound scanner, an image processor, or a variety of systems or devices. In one embodiment, an ultrasound imaging system or a medical ultrasound scanner performs the operations of Figure 1. In other embodiments, a processor performs operations 110, 130, 140, and 150. An ultrasound scanner performs operations 100 and 120. A display may be used for operation 150.

[0032] Additional, different, or fewer operations may be performed. For example, operations are provided for determining volumetric flow, ejection fraction, information about cardiac motion, or other flow analysis of the heart. As another example, operations are provided for using strain or other cardiac wall motion analysis, such as to classify a cardiac condition, for segment scoring, and / or for diagnosis. As another example, operation 150 is not provided.

[0033] The actions may be performed in the order shown (top to bottom or numerically) or in various orders.

[0034] In operation 100, an ultrasound scanner scans the patient's heart. The patient's heart is scanned in B-mode using a transducer scanning through a rib window from outside the patient, a transesophageal transducer scanning from within the patient's throat, or an intracardiac echocardiographic catheter scanning from within the heart.

[0035] The scan is performed using scan setting values, which are values ​​for the beamformer, transducer, image processor, and / or post-processing. The values ​​of the field of view (e.g., depth, width, and / or scan pattern), gain, contrast, frequency, and / or other setting or acquisition parameters for the scan of operation 100 are set by the sonographer and / or are default values ​​for cardiac images.

[0036] The values ​​generate a field of view that scans an entire region of the heart and / or a chamber of the heart. In one embodiment, the scan is for a standardized view of the heart, such as a longitudinal, A4C, A3C, or A2C view. The scan of operation 100 may be for other standardized or non-standard views. The scan is 2D, so it is along a plane through the heart.

[0037] The scan of operation 100 is repeated to acquire two or more images representing the patient's heart at various times. For example, a sequence of five or more images per cardiac cycle is acquired. An ECG can be used to specify the timing of the images relative to the cardiac cycle. Gates or triggers can be used to acquire images at desired times in the cardiac cycle, such as end diastole (ED) and end systole (ES).

[0038] 2D B-mode echocardiography data (e.g., images) are acquired for a plane within the heart, such as in one of the standard views (e.g., A2C). Figure 2 shows a B-mode image 200 of a plane through the heart.

[0039] In operation 110, an image processor detects abnormal wall motion from the ultrasound data resulting from the scan of operation 100. Ultrasound images from various times are used to detect abnormal wall motion. Images are used for ultrasound data from the scan in a display format or scan format prior to scan conversion. Data representative of the patient that may be used or used to generate images from the ultrasound scan is used as image data. Detection uses images from various times during the cardiac cycle, for example multiple images of standardized views.

[0040] After acquiring the best or acceptable quality images for the 2D view, the image processor detects endocardial and / or epicardial borders (e.g., myocardial borders). FIG. 2 shows an example where a border 216 is detected in the ED image 212 and the ES image 214 in operation 210. Any detection may be used, for example, random walker, thresholding, or pattern or model fitting. In one embodiment, a machine learning algorithm or model detects the border 216. The detection is performed on each of the images at other times of the cardiac cycle. In another embodiment, the border 216 is automatically detected on the ED frame or image. The detected border 216 is tracked in other frames, such as using speckle or local feature tracking (e.g., optical flow, motion prior, or speckle tracking). As a result, the border 216 is located in each of the images of the cardiac cycle.

[0041] In operation 220, any locations along the boundary 216 with abnormal motion are detected (operation 110). For example, global and local strains are calculated from the tracked points along the boundary 216. Examples of tracked points are shown as white dots in images 212, 214 of FIG. 2, where the strain indicates an extension or shortening of the boundary between the points over a predetermined portion of the cardiac cycle, such as ED-ES. A strain greater than or less than a threshold may indicate abnormal motion. Machine learning models or image processors may be used to detect abnormal motion. Segment boundaries with abnormal motion are identified. Segments or regions of abnormal motion may be larger or smaller than standard segments used in bull's eye visualization, such as a standard six-segment division. One or more regions of abnormal wall motion are not detected. For example, myocardial boundary regions with abnormal motion, if any, are selected. Strain from regions of normal motion may be calculated and saved for use in a strain map (e.g., a bull's eye model).

[0042] Anomalies may be due to defects in actual cardiac wall motion or due to poor tracking. An example is shown in FIG. 2. Anomaly detection of operation 220 detects a region 224 of abnormal wall motion. As shown in image 222 and highlighted in a zoomed-in portion 226 of image 222, this region 224 has no B-mode return. There is little speckle. The resulting tracking to find boundary 216 may be inaccurate, resulting in an inaccurate strain calculation and corresponding display of the abnormal wall motion.

[0043] To improve the strain calculation of region 224, a further targeted acquisition is provided in operation 230. The targeted acquisition may acquire an image 232 with more B-mode returns, such as speckle, for region 224. The acquisition is focused on and targets region 224. A smaller field of view and corresponding settings are used to acquire better data for tracking and / or strain calculation within the region, rather than the entire view of the heart chamber.

[0044] 1, an ultrasound scanner scans a region 224. Various regions 224 of detected abnormal wall motion may be scanned as separate patches. The scan results in ultrasound data or images representative of the region 224 over time in one or more cardiac cycles.

[0045] The ultrasound scanner is configured with settings (e.g., acquisition parameters) for scanning the region 224 or patch. The values ​​in operation 120 are different from those used in operation 100. For example, a smaller field of view or patch is scanned. The depth, width, focus, and / or scan pattern are varied, such as scanning only a sub-region of the region of the image 200. The field of view is set to just cover the region 224 of abnormal wall motion. This region 224 is smaller than the entire boundary 216 and the corresponding original image 200. The gain, contrast, or other settings may be the same or different. Multiple combinations of values ​​may be tried to identify a combination that provides the desired or optimal ultrasound data, such as increasing speckle or landmark information used for tracking. The values ​​of the patch or region 224 are set to generate speckle in the resulting image 232.

[0046] In one embodiment, the field of view is changed by changing the orientation of the scan plane relative to the region 224 and / or the heart of the patient. The B-mode data is acquired using a patch or scan plane that is a different plane than that used for scanning in operation 100. For example, the scan plane is rotated so that the region 224 is at a non-zero angle with respect to the plane of the standardized view acquired in operation 100. An example is shown in FIG. 3. The region 224 is first scanned in plane 300. The scan plane is rotated in multiple planes 310 to scan the patch around the region 224. Scanning in multiple planes may result in more speckle in the patch image 232 than the region 224 in the original image 222 (see portion 226). Alternatively or additionally, more accurate motion of the heart wall can be detected since there is less out-of-plane motion at the boundary 216 in the multiple planes.

[0047] Any amount of rotation may be used. One or more rotational scan planes may be used, such as sampling (e.g., scanning) at various rotations and corresponding planes. The result is additional data or samples that can be used to improve accuracy and / or find an optimal or better scan plane for region 224. Alternatively, only one rotation and corresponding scan plane is used.

[0048] The transducer may be automatically rotatable. For example, a robot controlling the transducer rotates the transducer. As another example, electronic steering of a helical or multi-dimensional (2D) array is used. As another example, mechanical rotation of the array, such as in a transesophageal probe, is used. Alternatively, commands to rotate are output to the sonographer, who then manually rotates the array to scan along various planes and corresponding fields of view.

[0049] In one embodiment, the user is guided to obtain higher quality images in the vicinity of such detection regions 224 by optimizing machine or acquisition parameters and / or rotating the imaging plane. The system recommends the user to rotate the probe to a certain extent and quantify the motion only in this selected region 224. The probe or array is rotated by a certain angle suggested by the system, the system analyzes the motion pattern and then estimates the next best possible position. The optimal orientation plane can be automatically determined by minimizing out-of-plane motion (e.g., finding the plane that shows the maximum amount of motion) and / or improving the quality of the speckle pattern, which results in improved tracking and motion field estimation. For example, if the abnormal motion region 224 is close to the apex and the speckles move towards the apex, it is difficult to track such speckles accurately. A better orientation plane improves tracking in such selected positions or regions 224, and since the image 232 includes only the small selected region 224, tracking can be done in real time.

[0050] In another embodiment, the user or image processor adjusts the ultrasound scanner settings (e.g., values ​​of acquisition parameters) until a better, improved, more, or sufficient speckle pattern is seen in the selected area. Speckle can be measured by a machine learning model and / or by a measure of variance. The resulting improved ultrasound data can be used to verify that the abnormality is not due to poor imaging quality in such areas. The distortion measurements of the area 224 are updated by tracking only the selected area 224 using the target acquisition image 232. The resulting distortion is fused or collected with the previously determined distortion for segments or areas without abnormal wall motion. The accuracy of the partial distortion is improved due to the improved image quality. If it is not possible to acquire higher quality data in such areas for other reasons, having additional data as additional sampling can result in more accurate distortion determination.

[0051] In operation 130, the image processor selects the value of the setting that provides the most accurate tracking, distortion, and / or analysis. If multiple different value combinations are sampled, the one that provides sufficient or better ultrasound data is selected. The level of speckle and / or out-of-plane motion is measured in one approach. In one example, different scan planes with corresponding different orientations to the region 224 may result in different levels of speckle and / or out-of-plane motion. By maximizing the speckle content and / or minimizing the out-of-plane motion (maximizing the in-plane motion), improved ultrasound data for tracking and / or distortion determination is provided. The best or sufficient (above / below threshold) setting value available is selected. For example, the view in one plane is selected over another plane based on the level of scattering and / or the amount of relative motion of the heart wall. The selection of the value is the selection of the ultrasound data to use for tracking or distortion determination, and vice versa.

[0052] Alternatively, no selection is made: ultrasound data from only one of the various value sets attempted is used, or data from any sampling is used together (e.g., calculating strain or wall position from each and averaging the results).

[0053] In operation 140, the image processor determines the abnormal wall motion distortion. Ultrasound data from the targeted acquisitions of operations 120, 230 are used to determine the distortion. Patches of B-mode data of region 224 from various times in the cardiac cycle are used to track boundary 216 from which distortion is calculated. As improved data is used, the resulting tracking and / or distortion may be more accurate. Data from selected planes and / or combinations of settings are used for tracking and distortion determination. Data from other combinations of settings may not be used or may be used additionally (e.g., average results).

[0054] A distortion is determined for a region 224. This distortion may be interpolated, extrapolated, and / or used for a segment of the multi-segment model. The region 224 may be at a different location and / or a different size than the segments of the multi-segment model, and thus the corresponding distortion of the region is used or modified to be used to represent the segment.

[0055] In another embodiment, sampling on one or more planes in addition to the original plane is used. Multi-dimensional distortion is determined. For example, distortion as a vector in three dimensions is determined using data from various planes. Motion is tracked along multiple planes. As a result, three-dimensional distortion is determined.

[0056] 1 illustrates the feedback from operation 140 to operation 120. If the image processor detects multiple regions 224 in a given view (e.g., image 200 with corresponding images 212 and 214) over different times, the target scanning of operation 120, the selection of operation 130, and the distortion determination of operation 140 are repeated for each of the regions 224. The scanning of the patch and the distortion determination for that patch are repeated to provide an improved distortion for each region 224. The repetition is performed until the distortion is determined for each of the regions of the anomaly. The same process is followed for all the anomaly segments.

[0057] 1 also illustrates feedback from operation 140 to operation 100. In the case of a multi-segment bull's-eye model, the different segments may be from different views. Operations 100, 110, 120, 130, and 140 are repeated for each of the views (e.g., A4C, A3C, and A2C). For each standard or non-standard view, the scanning of operation 100, the detection of regions 224 of abnormal wall motion in operation 110, the target scanning of a patch or patches around each region of operation 120, the selection of operation 130, and the determination of distortion 140 are repeated.

[0058] This tracking-based guidance continues until the full extent of the abnormal wall motion region is covered. The bull-eye visualization is updated from additional strain calculations. Alternatively, a 3D motion field is constructed by fusing tracked motion or strain from various directions. 3D surface tracking can be performed and 3D strain measurements can be calculated.

[0059] The image processor outputs the distortion in operation 150. The output may be an image output to a display, a computer network transmission, or a memory.

[0060] The image represents the distortion. The distortion may be represented as text, graphics, highlighting, annotation, or other presentation. In one embodiment, the image is a multi-segment model including the distortion. For example, a 16, 17, or 18 segment bull's-eye view or model is used. The distortion of each segment and the overall distortion are represented by color and / or alphanumeric. The distortion may be used for scoring, such that the output score reflects the distortion. Other distortion maps may be used. The distortion in a given distortion map may include one or more distortions determined from a standard view and one or more distortions determined from a rescan or target scan of the patch using various values ​​for the scan settings. For example, a 17 segment model is a distortion map with distortions determined from B-mode data of the patch (e.g., from scan 120) for one segment and a second segment of the distortion map with another distortion determined from a standardized view (e.g., from scan 100). The distortions determined from the various data may be fused together (e.g., presented together) to better quantify the extent of the abnormal wall motion region. Target scanning may improve the distortion map (e.g., for 17-segment models) since dense distortion values ​​are now available within each segment of the map, instead of values ​​resulting from fusing only two adjacent views. Furthermore, a more reliable four-stage scoring is constructed for rapid assessment of pathology and movement abnormalities.

[0061] In another embodiment, an image processor generates a multi-dimensional distortion map for display. Volume, surface, or other three-dimensional renderings are used. The distortions may be from various views. Some of the distortions may be from scans along various planes. These distortions from various planes provide a 3D map or vector of distortion. 3D surface tracking may be applied to any multi-planar acquisition to generate a 3D distortion map only in selected regions. For these regions, greater accuracy may be provided. Full 3D B-mode echocardiography has lower temporal and spatial resolution than 2D and requires longer analysis time, and therefore is used to provide 3D distortion or tracking for specific subsets or regions. Alternatively, a graph of the distortion vector or another image showing the distortion in various dimensions is displayed.

[0062] One embodiment of a system for detecting cardiac wall motion is shown in Figure 5. By reacquiring the ultrasound data of the suspect patch with different acquisition settings, cardiac wall motion can be determined more accurately.

[0063] The system performs the operations of Figure 1. For example, an ultrasound scanner 400 performs the scans of operations 100 and 120 using a beamformer 408 and a transducer 410. An image processor 402 performs operations 110, 130, and 140, with the distortion output of operation 150 on a display 420. Alternatively, the system performs different, additional, or fewer operations.

[0064] The system includes a medical diagnostic ultrasound scanner 400, a detachable transducer 410, a display 420, and an ECG device 430. Additional, different, or fewer components may be provided. For example, a processor external to the ultrasound scanner 400 is provided for analyzing the ultrasound data. As another example, the ECG device 430 is integrated within the ultrasound scanner 400. In yet another example, the ECG device 430 is not provided because the ultrasound scanner 400 uses the ultrasound data to determine cardiac cycle characteristics (e.g., intervals).

[0065] The ECG device 430 is a processor, circuitry, and / or electrodes. Any ECG device now known or later developed may be used. By placing the electrodes on the patient, a cardiac trace of the patient's cardiac cycle is generated. The ECG device 430 detects the R waves, other phases, intervals, and / or heart rate of the cardiac cycle. Alternatively, the ultrasound scanner 400 detects the phase of the cardiac cycle from the trace signal received from the ECG device 430.

[0066] The ultrasound scanner 400 is a medical diagnostic ultrasound imaging system. In other embodiments, the ultrasound scanner 400 is a therapeutic system that can also be imaged. Any system now known or later developed for cardiac echocardiography can be used. The ultrasound scanner 400 is configured with a setup for performing an echocardiogram of a patient. During an imaging session, the transducer array 410 is positioned to scan the patient's heart and images are generated over one or more cardiac cycles.

[0067] The ultrasound scanner 400 includes a transducer array 410, a beamformer 408, an image processor 402, a display 420, an input 406, an image processor 402, and a memory 404. Additional, different, or fewer components may be provided. For example, the transducer array 410 and / or the display 420 are separate from the scanner 400. As another example, the memory 404 is separate from or not part of the ultrasound scanner 400. As yet another example, a scan converter, a temporal filter, a spatial filter, or other ultrasound imaging components may be provided.

[0068] The transducer array 410 is an array of transducer elements in a housing adapted or shaped for handheld use outside the patient, or shaped as a catheter, intraoperative probe, intercavitary probe, transesophageal probe, or other now known or later developed transducer probe. The array may be linear, multi-dimensional, annular, or other now known or later developed array of piezoelectric or microelectromechanical elements.

[0069] The transducer array 410 generates acoustic energy in response to electrical signals from the beamformer 408. For imaging, acoustic echoes received by the transducer array 410 are converted to electrical signals, and the transducer array 410 provides the electrical signals to the beamformer 408.

[0070] The beamformer 408 may be a transmit beamformer, a receive beamformer, or both a transmit beamformer and a receive beamformer. As a transmit beamformer, the beamformer 408 includes waveform generators or pulses, delays, phase rotators, timing generators, amplifiers, combinations thereof, or other now known or later developed transmit beamformer components in the multiple channels. For transmission, the beamformer 408 generates relatively delayed and apodized waveforms for each of the multiple channels for the corresponding multiple transducer elements. The transducer array 410 forms one or more acoustic beams in response to the waveforms.

[0071] As a receive beamformer, the beamformer 408 includes channels with delays, phase rotators, amplifiers, or combinations thereof, and includes multiple summers or a single summer for adding together the signals from each channel. For receive, the beamformer 408 generates samples that represent various spatial locations.

[0072] The receive beamformed samples are provided to an image processor 402 to generate an image. The image processor 402 may be a detector, filter, scan converter, three-dimensional processor, combinations thereof, or other now known or later developed image generators. Detection is B-mode (intensity). The samples are detected, scan converted, and provided to a display 420. Other image processors may also be applied.

[0073] The ultrasound scanner 400, including the beamformer 408, the transducer array 410, and / or the image processor 402, operates according to set values. Various acquisition parameters are set to establish the field of view, contrast, gain, and / or other characteristics of the image. The ultrasound scanner 400 is configured to scan or acquire ultrasound data based on the set values.

[0074] The ultrasound scanner 400 is configured to scan the patient's heart. For example, using manual guidance and / or automatic detection, the heart is scanned to identify a standardized view. A series of images over one or more cardiac cycles of the view are acquired by scanning. The values ​​of the settings define the scan and image processor for acquiring the ultrasound data.

[0075] The ultrasound scanner 400 is configured to scan a target patch. One or more locations within the initial view are scanned in a more targeted manner. Values ​​of settings are changed to rescan a local area, such as an area exhibiting abnormal cardiac motion. Field of view, gain, contrast, or other settings are changed to scan a patch around the area.

[0076] The input 406 is a user input device such as a keyboard, touch screen, trackball, mouse, buttons, rotatable knobs, and / or sliders. The input allows for the sonographer to input settings for configuring the ultrasound scanner 400. Other user input information may also be entered, such as for detection of abnormal regions and / or indication or selection of improved images.

[0077] The image processor 402 may be a general purpose processor, a digital signal processor, a controller, an artificial intelligence processor, an application specific integrated circuit, a field programmable gate array, an analog circuit, a digital circuit, a combination thereof, or another now known or later developed device for tracking, determining distortion, configuring the ultrasound scanner 400, and / or generating distortion images. The image processor 402 may be configured with hardware, software, and / or firmware to detect anomalies, select settings, determine distortion, and / or output images.

[0078] The image processor 402 is configured to detect the location of abnormalities in the patient's cardiac wall motion. Ultrasound data from a scan, such as a scan of a standardized view of the heart, is used to detect regions with abnormal cardiac wall motion, either due to actual abnormalities or due to poor data quality.

[0079] The image processor 402 is configured to determine cardiac wall motion distortion at various locations along the heart wall. The image processor 402 detects the wall, such as by using artificial intelligence. The image processor 402 tracks positions along the wall through a series of images to provide cardiac wall motion. The image processor 402 calculates distortion from the tracking or wall motion. The distortion may be determined for various regions or segments in the original view.

[0080] If abnormal wall motion is detected, the image processor 402 is configured to cause the ultrasound scanner 400 to scan a target patch around the area of ​​abnormal wall motion and acquire improved ultrasound data of the patch or area using different values ​​of one or more settings, the ultrasound data being acquired from the scan using at least one of the different values ​​of the one or more settings compared to the values ​​of the settings for the original view.

[0081] The values ​​may define different scan planes to use to scan the region. The image processor 402 may control the ultrasound scanner 400 to scan different planes of the patch and / or instruct the sonographer to change the orientation of the transducer array 410. The region is scanned in two or more different planes based on control by the image processor 402. Other settings such as gain and / or contrast may be different instead of or in addition to the field of view (depth, azimuth range, and / or scan plane) for rescanning the patch. The use of different value settings may result in different levels of speckle and / or out-of-plane motion. The ultrasound data with more speckle and / or less out-of-plane motion is used by the image processor 402 to determine the distortion of the region.

[0082] The display 420 may be a monitor, liquid crystal display, plasma display, light emitting diode display, printer, projector, or other device for outputting one or more images for viewing. Any now known or later developed display device may be used. The display 420 may be part of the ultrasound scanner 400, or may be a remote device, such as a wall mounted or remote workstation display.

[0083] The display 420 shows one or more images of the patient's heart. The images are generated as part of an echocardiographic examination during which the ultrasound scanner 400 may also output guidance, such as rotation of the transducer array 410 on the display 420.

[0084] The display 420 shows one or more images with the distortion information. The distortion image is generated by the image processor 402 and output by the display 420. The distortion image may be a bull's-eye view from a multi-segment model. Other distortion maps showing distortion as a function of position along the heart wall may be output. 3D surface distortion may be output by surface or volume rendering, etc.

[0085] The memory 404 is a non-transitory computer-readable storage medium, such as a cache, a buffer, a RAM, a removable media, a hard drive, or other computer-readable storage medium. The computer-readable storage medium includes various types of volatile and non-volatile storage media. The memory 404 stores ECG data, tracking, strain, detected boundaries, abnormal regions, artificial intelligence (e.g., machine learning models), strain images, or other information for guidance in cardiac echocardiography. Input, output, and / or information during processing are stored.

[0086] The memory 404 or another memory stores instructions for the image processor 402 and / or other processors. Data representing instructions executable by a programmed processor for stress echocardiography guidance is stored in the memory. The instructions are for performing the processes, methods, and / or methods discussed herein. The functions, operations, or tasks illustrated in the figures or described herein are performed in response to one or more sets of instructions stored in or on a computer-readable recording medium. The functions, operations, or tasks are independent of a particular type of instruction set, recording medium, processor, or processing strategy and may be performed by software, hardware, integrated circuits, firmware, microcode, etc. operating alone or in combination. Similarly, the processing strategy may include multiprocessing, multitasking, parallel processing, etc. In one embodiment, the instructions are stored on a removable media device for reading by a local or remote system. In other embodiments, the instructions are stored in a remote location for transfer over a computer network or over telephone lines. In still other embodiments, the instructions are stored within a given computer, CPU, GPU, or system.

[0087] Although the present invention has been described above with reference to various embodiments, it should be understood that many changes and modifications can be made without departing from the scope of the present invention. It should be understood, therefore, that the above detailed description is to be interpreted as illustrative rather than limiting, and that it is the appended claims, including all equivalents, which are to define the spirit and scope of the present invention.

Claims

1. 1. A method for detecting cardiac wall motion with a medical ultrasound scanner, comprising: a first scan of a first plane through a patient's heart with the medical ultrasound scanner using first values ​​of scan settings, the first plane scanned at different times providing first ultrasound data representative of the heart at the different times; detecting abnormal wall motion from the first ultrasound data; a second scan of the region of abnormal wall motion in a second plane with the medical ultrasound scanner using second values ​​of the scan settings, the second settings being different from the first values ​​of the scan settings, the second plane being different from the first plane, the second scan yielding second ultrasound data; determining the abnormal wall motion distortion from the second ultrasound data; and displaying an image representative of said distortion; The method includes:

2. the first scan includes a first scan of a standardized view of the heart; the second scan includes a second scan with a smaller field of view that just covers the region of abnormal wall motion; the region is smaller than a field of view in the standardized view; The method of claim 1.

3. Detection includes detection by machine learning models, The method of claim 1.

4. The scan settings include gain, contrast, depth, and / or field of view. The method of claim 1.

5. the scan setup includes the field of view; the first value is for a larger field of view than the second value; the first value is for the first surface; the second value is for the second surface; The method according to claim 4.

6. selecting the second surface based on a level of dispersion and / or an amount of relative motion at the heart wall; The method of claim 1 further comprising:

7. a third scan of the area in a third plane different from the first plane and the second plane; The method of claim 1 further comprising:

8. a second scan including a second scan with the second value configuring the medical ultrasound scanner to generate speckle; The method of claim 1.

9. determining the distortion includes determining the distortion for a segment of a multi-segment model; and displaying the multi-segment model including the distortion. The method of claim 1.

10. determining the distortion includes determining the distortion in multiple dimensions of the first surface and the second surface; The display includes displaying a multi-dimensional distortion map. The method of claim 1.

11. For various regions of abnormal wall motion, the second scan; and said determination; Repeatedly, The method of claim 1 further comprising:

12. for various standardized views of the heart; the first scan; The detection. the second scan; and said determination; Repeatedly, Further comprising: and displaying the distortion from the various standardized views. The method of claim 1.

13. 1. An ultrasound system for detecting cardiac wall motion, comprising: locating a cardiac wall motion abnormality in a patient; scanning the location with various values ​​at one or more settings; determining a strain of motion of the heart wall at the location from ultrasound data from the scan using at least one of the different values ​​of the one or more settings; An ultrasound scanner configured as follows: a display configured to display an image of the distortion; and A system comprising:

14. The one or more settings include: wherein the ultrasound scanner is configured to scan the locations in the various planes; For scanning various surfaces, 14. The ultrasound system of claim 13.

15. The one or more settings include: wherein the ultrasound scanner is configured to scan the location for imaging at different levels of speckle; gain, contrast, and / or depth; 14. The ultrasound system of claim 13.

16. The ultrasonic scanner includes: scanning the patient's heart with standardized views; Detecting the location from the standardized view; scanning the location with a field of view smaller than the standardized view; It is configured as follows:

14. The ultrasound system of claim 13.

17. 1. A non-transitory computer readable medium having stored thereon data representing instructions executable by a programmed processor for detecting cardiac wall motion, the non-transitory computer readable medium comprising: Scanning the patient's heart in standardized views; detecting abnormal wall motion of the heart from the standardized views; acquiring B-mode data for said patch of abnormal wall motion; determining distortion from the B-mode data of the patch; displaying a distortion map including a first segment of the distortion map having a distortion determined from the B-mode data of the patch and a second segment of the train map having a different distortion determined from the standardized view; Includes instructions for A non-transitory computer-readable recording medium.

18. the instructions for acquiring the B-mode data within the patch include acquiring the B-mode data with the patch in a different plane than the standardized view; The instructions further include selecting the different surface based on an amount of speckle or motion.

20. The non-transitory computer readable storage medium of claim 17.

19. and the instructions for acquiring the B-mode data include acquiring the B-mode data having a greater level of speckle in the patch than in the standardized view at the location of the abnormal wall motion.

20. The non-transitory computer readable storage medium of claim 17.

20. The instructions include instructions for various standardized views: The scanning; The detection. The acquisition; said determination; Further including repetition of the instructions for displaying the distortion map include displaying a 16, 17, or 18 segment bull's eye view.

20. The non-transitory computer readable storage medium of claim 17.

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