Guided cardiac ultrasound imaging to minimize apical shortening

Abstract

An ultrasound system is provided. The ultrasound system comprises a processor configured to communicate with a display, a transducer array of a handheld ultrasound probe, and memory. The processor controls the transducer array to acquire ultrasound images. The ultrasound images show one or more views of the patient's anatomical structures. The processor of the ultrasound system determines a shortening metric corresponding to the ultrasound image. The processor then outputs a shortening indicator to the display representing the shortening metric and compares the shortening metric to one or more predetermined criteria. In response to a shortening metric that does not meet one or more predetermined criteria, the processor outputs guidance to the user of the ultrasound system to adjust the orientation of the handheld ultrasound probe.

Description

Technical Field

[0001] The present disclosure generally relates to ultrasonic images. In particular, an ultrasonic system provides guidance for obtaining ultrasonic images that minimize shortening, such as apical shortening, in ultrasonic images of the left ventricle of the heart.

Background Art

[0002] Physicians use many different medical diagnostic systems and tools to monitor a subject's health and diagnose and treat medical conditions. Ultrasonic imaging systems are widely used for medical imaging and measurement. An ultrasonic transducer probe transmits sound waves into a subject's body and records the sound waves reflected and backscattered from tissues, blood vessels, and internal organs by an array of ultrasonic transducer elements in the subject. The transmission and reception of the sound waves generate an image of the internal anatomical structure of the subject using various beamforming and processing techniques.

Summary of the Invention

Problems to be Solved by the Invention

[0003] Ultrasonic images are safe, useful, and in some applications, non-invasive tools for diagnostic examinations, interventions, and / or treatments. Ultrasonic images can be used to diagnose a wide variety of medical conditions. When imaging various aspects of a patient's heart, accurate probe placement is essential to optimize the view of the ultrasonic image obtained. For example, using ultrasonic images, measurement criteria such as ejection fraction (EF), stroke volume (SV), and global longitudinal strain (GLS) can be quantified. If the probe placement during the imaging procedure is inaccurate, an inappropriate view will result, which in turn can lead to inaccurate measurements of a patient's specific area of interest and inappropriate diagnoses. Lack of expertise, time, or resources can lead to low-quality ultrasonic images and inaccurate measurements.

Means for Solving the Problems

[0004] Aspects of this disclosure relate to systems, apparatus, and methods for guiding ultrasound images to minimize shortening. Shortening can occur when the ultrasound image plane does not extend through the actual / true / anatomical apex of the anatomical structure being imaged. For example, apical shortening can occur when the ultrasound image plane does not extend through the actual / true / anatomical apex of the left ventricle within the heart. Aspects of this disclosure advantageously assist users of an ultrasound imaging system in precisely positioning the ultrasound imaging probe to acquire images with minimal shortening, such as apical shortening, in ultrasound images of the left ventricle. This advantageously enables less experienced and / or users to acquire better ultrasound images in point-of-care settings, non-ideal imaging settings, etc. Ultrasound images with less shortening can be further processed to generate more accurate measurements of relevant features of the patient's anatomical structure, such as cardiac ejection fraction (EF), stroke volume (SV), and global longitudinal distortion (GLS).

[0005] In some embodiments, the system targets a user of an ultrasound imaging system who acquires ultrasound images. The ultrasound imaging system uses a deep learning network to analyze the received images and determine whether the images were obtained in the correct intercostal space and at the correct probe angle. If the images were not obtained in the correct intercostal space, the system outputs a guide instructing the user to move the probe to the correct intercostal space, and additional images are received. If the images are obtained in the correct intercostal space but at an incorrect probe angle, the system maintains the probe position but outputs a guide to the user to change the probe angle. As the user moves the probe as indicated by the system, the quality of the received images is improved until the user obtains an ultrasound image in the correct intercostal space and at the correct angle so that apical shortening of the image is minimized.

[0006] In some embodiments, the ultrasound imaging system is designed to allow a user to acquire ultrasound images in multiple intercostal spaces. The ultrasound imaging system then analyzes these images and assigns an apical shortening indicator metric (AFIM) to each image. It then compares the AFIM values ​​of each image and selects the image with the lowest AFIM as the reference image, using the original AFIM value as the reference AFIM value. The ultrasound imaging system then outputs a guide to the user for positioning the probe in the intercostal space corresponding to the reference AFIM to acquire additional images. As each image is received, the ultrasound imaging system calculates the AFIM value of the image and compares the new value to the reference AFIM value. If the new AFIM value is less than the reference AFIM value, the new AFIM value is set as the reference AFIM value. However, if the new AFIM value is greater than or equal to the reference AFIM value, the reference AFIM value is not updated. The ultrasound imaging system repeats this process until the user finishes acquiring images or until the reference AFIM decreases below a threshold.

[0007] In an exemplary embodiment, an ultrasound system is provided. The ultrasound system includes a display, a transducer array of a handheld ultrasound probe, and a processor configured to communicate with memory, the processor being configured to control the transducer array to acquire multiple ultrasound images corresponding to one or more views of a patient's anatomical structure, to determine a shortening metric corresponding to one or more of the ultrasound images, to output a shortening indicator to the display representing the shortening metric, to compare the shortening metric to one or more predetermined criteria, and, in response to the shortening metric not meeting one or more predetermined criteria, to output a guide to the display to adjust the orientation of the handheld ultrasound probe.

[0008] In some embodiments, the abbreviation indicator comprises a plurality of discrete regions, each associated with a different value of the abbreviation metric, and a graphical element aligned with one of the plurality of regions based on the value of the abbreviation metric. In some embodiments, the abbreviation indicator includes a continuous spectrum of different values ​​of the abbreviation metric and a graphical element at a position along the continuous spectrum based on the value of the abbreviation metric. In some embodiments, in response to a abbreviation metric that satisfies one or more predetermined criteria, the processor is configured to store one or more ultrasound images in memory. In some embodiments, in response to a abbreviation metric that does not satisfy one or more predetermined criteria, the processor is configured to control the transducer array to acquire additional ultrasound images, calculate an updated abbreviation metric corresponding to the additional images, and modify the abbreviation indicator based on the updated abbreviation metric. In some embodiments, the processor is configured to compare the updated abbreviation metric with one or more predetermined criteria and to modify the guidance to the user based on the comparison between the updated abbreviation metric and one or more predetermined criteria. In some embodiments, the abbreviation metric comprises a first component related to probe position and a second component related to probe tilt. In some embodiments, to compare a shortening metric with one or more predetermined criteria, the processor is further configured to compare a first component relating to probe position with a desired probe position and a second component relating to probe tilt with a desired probe tilt. In some embodiments, in response to the first component relating to probe position not matching the desired probe position, the user guidance includes a message for adjusting the position of the handheld ultrasound probe. In some embodiments, the message includes moving the handheld ultrasound probe upward or downward into different intercostal spaces.In some embodiments, in response to a first component related to probe position matching a desired probe position and a second component related to probe tilt not matching a desired probe tilt, the guidance to the user includes a message for adjusting the angle of the handheld ultrasound probe while maintaining the position of the handheld ultrasound probe. In some embodiments, satisfying one or more predetermined criteria includes a first component matching a desired probe position and a second component matching a desired probe tilt. In some embodiments, one or more predetermined criteria comprises a threshold for a shortening metric. In some embodiments, the processor is further configured to control the transducer array to acquire a third set of ultrasound images corresponding to one or more views of the patient's anatomical structure, while the handheld ultrasound probe is in a first position corresponding to a first view of the patient's anatomical structure; while the handheld ultrasound probe is in a second position corresponding to a second view of the patient's anatomical structure; and while the handheld ultrasound probe is in a third position corresponding to a third view of the patient's anatomical structure, the processor is further configured to control the transducer array to acquire a third set of ultrasound images. In some embodiments, the patient's anatomical structure includes a heart, the first view corresponds to a first intercostal space, the second view corresponds to a second intercostal space above the first intercostal space, and the third view corresponds to a third intercostal space below the first intercostal space. In some embodiments, the processor is further configured to calculate a shortening metric corresponding to one or more of a plurality of ultrasound images, by calculating a first shortening metric associated with a first plurality of ultrasound images, a second shortening metric associated with a second plurality of ultrasound images, and a third shortening metric associated with a third plurality of ultrasound images. In some embodiments, the processor is further configured to compare the first shortening metric, the second shortening metric, and the third shortening metric, and to assign one of the first, second, or third shortening metric to a hierarchical shortening metric.In some embodiments, one or more predetermined criteria include a history reduction metric.

[0009] In an exemplary embodiment, an ultrasound system for guiding a user to obtain an optimized ultrasound image, A handheld ultrasound probe with a transducer array, The display and Memory and A processor configured to communicate with the transducer array, the display, and the memory, wherein the processor The steps of controlling the transducer array to acquire a first plurality of ultrasound images corresponding to one or more views of the patient's anatomical structure, A step of calculating a shortening metric corresponding to one or more ultrasound images from the aforementioned plurality of ultrasound images, The steps include outputting a shortened indicator representing the shortened metric to the display, The steps include comparing the shortened metric with one or more predetermined criteria, Steps include: repeatedly outputting guidance to the user to adjust the orientation of the handheld ultrasound probe in response to the shortening metric not meeting one or more predetermined criteria, acquiring a further set of ultrasound images, calculating a further shortening metric corresponding to the further set of ultrasound images, updating the shortening indicator based on the further shortening metric, and comparing the further shortening metric to the one or more predetermined criteria until the updated shortening metric meets one or more predetermined criteria; An ultrasonic system is provided, configured to perform the following actions:

[0010] Further aspects, features, and advantages of this disclosure will become apparent from the following detailed description.

[0011] Exemplary embodiments of this disclosure will be described with reference to the accompanying drawings. [Brief explanation of the drawing]

[0012] [Figure 1] This is a schematic diagram of an ultrasonic imaging system according to an aspect of the present disclosure. [Figure 2] This is a schematic diagram of a processor circuit according to the embodiments of this disclosure. [Figure 3] This is a flowchart illustrating a method for acquiring an ultrasonic image and guiding a user of an ultrasonic imaging system to position an ultrasonic imaging probe, according to an aspect of the present disclosure. [Figure 4A] This is a schematic diagram of a graphical user interface for displaying a user guide for acquiring ultrasound images, according to an aspect of this disclosure. [Figure 4B] This is a schematic diagram of a graphical user interface for displaying a user guide for acquiring ultrasound images, according to an aspect of this disclosure. [Figure 4C] This is a schematic diagram of a graphical user interface for displaying a user guide for acquiring ultrasound images, according to an aspect of this disclosure. [Figure 5] This is a flowchart illustrating a method for acquiring an ultrasound image and guiding a user of an ultrasound imaging system to position an ultrasound imaging probe, according to an aspect of the present disclosure. [Figure 6A] This is a schematic diagram of a graphical user interface for displaying a user guide for acquiring ultrasound images, according to an aspect of this disclosure. [Figure 6B] This is a schematic diagram of a graphical user interface for displaying a user guide for acquiring ultrasound images, according to an aspect of this disclosure. [Figure 6C] This is a schematic diagram of a graphical user interface for displaying a user guide for acquiring ultrasound images, according to an aspect of this disclosure. [Figure 7] This is a schematic diagram of a deep learning algorithm according to the embodiments of this disclosure. [Figure 8] This is a schematic diagram of a convolutional neural network (CNN) configuration according to the aspects of this disclosure. [Figure 9]A schematic diagram of a graphical user interface for displaying a user guide for probe position according to an aspect of the present disclosure. [Figure 10] A schematic diagram of a graphical user interface for displaying a user guide for probe angle according to an aspect of the present disclosure.

Embodiments for Carrying Out the Invention

[0013] For the purpose of facilitating understanding of the principles of the present disclosure, reference is now made to the embodiments shown in the drawings and the principles of the present disclosure are described using specific language. However, it is understood that no limitation to the scope of the present disclosure is intended. Any changes and further modifications to the described apparatus, system, and method, as well as any further applications of the principles of the present disclosure, are fully contemplated and included within the present disclosure as would be normally contemplated by those skilled in the art to which the present disclosure pertains. In particular, features, components, and / or steps described with respect to one embodiment may be combined with features, components, and / or steps described with respect to other embodiments of the present disclosure. However, for the sake of brevity, many repetitions of these combinations are not described separately.

[0014] FIG. 1 is a schematic diagram of an ultrasonic imaging system 100 according to an aspect of the present disclosure. The system 100 is used to scan a region or volume of a subject's body. The subject may include a patient undergoing an ultrasonic imaging procedure, or any other person, or any suitable living or non-living thing or structure. The system 100 includes an ultrasonic imaging probe 110 that communicates with a host 130 via a communication interface or link 12Ø The probe 110 may include a transducer array 112, a beamformer 114, a processor circuit 116, and a communication interface 118. The host 130 may include a display 132, a processor circuit 134, a communication interface 136, and a memory 138 for storing subject information.

[0015] In some embodiments, the probe 110 is an external ultrasound imaging device including a housing 111 configured for hand-held operation by a user. The transducer array 112 can be configured to acquire ultrasound data while the user is gripping the housing 111 of the probe 110, and the transducer array 112 is disposed adjacent to or in contact with the subject's skin. The probe 110 is configured to acquire ultrasound data of anatomical structures within the subject's body while the probe 110 is disposed outside the subject's body for general images such as abdominal images, liver images, etc. In some embodiments, the probe 110 can be an external ultrasound probe, a trans-thoracic probe, and / or a curved array probe. In other embodiments, the probe 110 can be an internal ultrasound imaging device and can include a housing 111 configured to be disposed within a lumen of the subject's body for general imaging such as abdominal imaging, liver imaging, etc. In some embodiments, the probe 110 may be a curved array probe. The probe 110 can be in any suitable form for any suitable ultrasound imaging application, including both external and internal ultrasound imaging.

[0016] In some embodiments, aspects of the present disclosure can be implemented using medical images of a subject obtained using any suitable medical imaging device and / or modality. Examples of medical images and medical imaging devices include x-ray images (angiography images, fluoroscopy images, images with or without contrast obtained by an x-ray imaging device), computed tomography (CT) images obtained by a CT imaging device, positron emission tomography (PET-CT) images obtained by a PET-CT imaging device, magnetic resonance images (MRI) obtained by an MRI device, single photon emission computed tomography (SPECT) images obtained by a SPECT imaging device, optical coherence tomography (OCT) images obtained by an OCT imaging device, and intravascular photoacoustic (IVPA) images obtained by an IVPA imaging device. The medical imaging device can be located outside the subject's body, spaced apart from the subject's body, adjacent to the subject's body, in contact with the subject's body, and / or within the subject's body to acquire medical images.

[0017] In the case of an ultrasound imaging device, the transducer array 112 emits an ultrasound signal toward an anatomical object 105 of the subject and receives an echo signal reflected from the object 105 and returned to the transducer array 112. The ultrasound transducer array 112 can include any appropriate number of acoustic elements, including one or more acoustic elements and / or multiple acoustic elements. In some examples, the transducer array 112 includes a single acoustic element. In some examples, the transducer array 112 can include an array of acoustic elements having any number of acoustic elements in any appropriate configuration. For example, the transducer array 112 can include one acoustic element and between 10,000 acoustic elements, including values ​​such as 2 acoustic elements, 4 acoustic elements, 36 acoustic elements, 64 acoustic elements, 128 acoustic elements, 500 acoustic elements, 812 acoustic elements, 1,000 acoustic elements, 3,000 acoustic elements, 8,000 acoustic elements, and / or other values ​​greater than and less than 10,000. In some embodiments, the transducer array 112 may include an array of acoustic elements having any number of acoustic elements in any suitable configuration, such as a linear array, planar array, curved array, curved array, circumferential array, annular array, phased array, matrix array, one-dimensional (1D) array, 1.x-dimensional array (e.g., a 1.5D array), or two-dimensional (2D) array. The array of acoustic elements (e.g., one or more rows, one or more columns, and / or one or more orientations) can be controlled and activated uniformly or independently. The transducer array 112 can be configured to acquire one-dimensional, two-dimensional, and / or three-dimensional images of the anatomical structure of the subject. In some embodiments, the transducer array 112 may include piezoelectric micromachined ultrasonic transducers (PMUTs), capacitive micromachined ultrasonic transducers (CMUTs), single crystals, lead zirconate titanate (PZT), PZT composites, other suitable transducer types, and / or combinations thereof.

[0018] Object 105 may include any anatomical structure or feature, such as the kidneys, liver, and / or any other anatomical structure of the subject. The disclosure can be implemented in relation to any number of anatomical locations and histological types, including, but not limited to, organs including the liver, kidneys, gallbladder, pancreas, and lungs, tubules, intestines, brain, dural sac, spinal cord, and nervous system structures including peripheral nerves, the urinary tract, and valves in blood vessels, blood, abdominal organs, and / or other systems of the body. In some embodiments, Object 105 may include malignancies such as tumors, cysts, lesions, hemorrhages, or blood pools in any part of a human anatomical structure. The anatomical structure may be a blood vessel as an artery or vein in the vascular system of the subject, including the cardiovascular system, peripheral vascular system, neurovascular system, renal blood vessels, and / or any other suitable lumens in the body. In addition to natural structures, the disclosure can be implemented in the context of artificial structures such as heart valves, stents, shunts, filters, implants, and other devices, but is not limited thereto.

[0019] The beamformer 114 is coupled to the transducer array 112. The beamformer 114 controls the transducer array 112, for example, for transmitting an ultrasonic signal and receiving an ultrasonic echo signal. In some embodiments, the beamformer 114 can apply a time delay to the signals transmitted to individual acoustic transducers in the array within the transducer 112 so that the acoustic signals are steered in any suitable direction as they propagate away from the probe 110. Based on the response of the received ultrasonic echo signal, the beamformer 114 may further provide the image signal to the processor circuit 116. The beamformer 114 may include multiple stages of beamforming. Beamforming can reduce the number of signal lines to be coupled to the processor circuit 116. In some embodiments, the transducer array 112 combined with the beamformer 114 may be referred to as the ultrasonic image component.

[0020] The processor 116 is coupled to the beamformer 114. The processor 116 may also be described as a processor circuit which may include other components that communicate with the processor 116, such as memory, the beamformer 114, a communication interface 118, and / or other appropriate component elements. The processor 116 may include a central processing unit (CPU), a graphical processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a controller, a field-programmable gate array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein. The processor 116 may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors working with a DSP core, or any other such configuration. The processor 116 is configured to process the beamformed image signal. For example, the processor 116 may perform filtering and / or quadrature demodulation to adjust the image signal. The processors 116 and / or 134 can be configured to control the array 112 to acquire ultrasonic data related to the object 105.

[0021] The communication interface 118 is coupled to the processor 116. The communication interface 118 may include one or more transmitters, one or more receivers, one or more transceivers, and / or circuits for transmitting and / or receiving communication signals. The communication interface 118 may include hardware components and / or software components that implement a specific communication protocol suitable for transferring signals to the host 130 over the communication link 120. The communication interface 118 may be referred to as a communication device or a communication interface module.

[0022] Communication link 120 can be any suitable communication link. For example, communication link 120 could be a wired link such as a Universal Serial Bus (USB) link or an Ethernet® link. Alternatively, communication link 120 could be a wireless link such as an ultra-wideband (UWB) link, an IEEE 802.11 WiFi link, or a Bluetooth link. In host 130, the communication interface 136 can receive image signals. The communication interface 136 may be substantially the same as the communication interface 118. Host 130 may be any suitable computing and display device, such as a workstation, personal computer (PC), laptop, tablet, or mobile phone.

[0023] The processor 134 is coupled to the communication interface 136. The processor 134 may also be described as a processor circuit which may include other components that communicate with the processor 134, such as memory 138, the communication interface 136, and / or other appropriate component elements. The processor 134 may be implemented as a combination of software and hardware components. The processor 134 may include a central processing unit (CPU), graphics processing unit (GPU), digital signal processor (DSP), application-specific integrated circuit (ASIC), controller, FPGA device, another hardware device, firmware device, or any combination thereof configured to perform the operations described herein. The processor 134 may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors working with a DSP core, or any other such configuration. The processor 134 may be configured to generate image data from an image signal received from the probe 110. The processor 134 may apply advanced signal processing and / or image processing techniques to the image signal. In some embodiments, the processor 134 can form a three-dimensional (3D) volumetric image from the image data. In some embodiments, the processor 134 can perform real-time processing on the image data to provide a streaming video of an ultrasound image of the object 105. In some embodiments, the host 130 includes a beamformer. For example, the processor 134 may be part of such a beamformer and / or may be able to communicate with such a beamformer. The beamformer in the host 130 may be a system beamformer or a main beamformer (providing one or more subsequent stages of beamforming), while the beamformer 114 may be a probe beamformer or a microbeamformer (providing one or more initial stages of beamforming).

[0024] Memory 138 is coupled to processor 134. Memory 138 can be any suitable storage device, such as cache memory (e.g., the cache memory of processor 134), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), field-programmable gate array read-only memory (PROM), erasable field-programmable gate array read-only memory (EPROM), electrically erasable field-programmable gate array read-only memory (EEPROM), flash memory, solid-state memory device, hard disk drive, solid-state drive, other forms of volatile and non-volatile memory, or combinations of different types of memory.

[0025] Memory 138 may be configured to store subject information relating to the subject's medical history, history of procedures performed, anatomical or biological characteristics, characteristics, or medical conditions related to the subject, measurements, data, or computer-readable instructions such as files, codes, software, or other applications, as well as any other appropriate information or data. Memory 138 may be located within host 130. Subject information may include, but is not limited to, measurements, data, files, ultrasound images, ultrasound videos, and / or other imaging information relating to the subject's anatomical structure, and may include other forms of medical history. Subject information may include parameters related to imaging procedures, such as anatomical scan windows, probe directions, and / or subject position during imaging procedures. Memory 138 may also be configured to store information relating to the training and implementation of machine learning algorithms (e.g., neural networks), and / or information relating to the implementation of image recognition algorithms for detecting / segmenting anatomical structures, image quantification algorithms, and / or image acquisition guide algorithms, including those described herein.

[0026] The display 132 is coupled to the processor circuit 134. The display 132 may be a monitor or any suitable display. The display 132 is configured to display ultrasound images, image videos, and / or any imaging information of the object 105.

[0027] System 100 may be used to assist an ultrasound technician in performing an ultrasound scan. The scan may be performed in a point-of-care set. In some examples, the host 130 is a console or a mobile cart. In some cases, the host 130 may be a mobile device such as a tablet, mobile phone, or portable computer. During the imaging procedure, the ultrasound system can acquire ultrasound images of a specific region of interest within the anatomical structure of the subject. The ultrasound system 100 can then analyze the ultrasound images to identify various parameters related to image acquisition, such as the scan window, probe orientation, subject position, and / or other parameters. The system 100 can then store the images and these associated parameters in memory 138. In subsequent imaging procedures, the system 100 may be used to guide the user of the system 100 to use the same or similar parameters in subsequent imaging procedures. For display to the user, previously acquired ultrasound images and associated parameters may be retrieved, which will be described in more detail below.

[0028] In some embodiments, the processor 134 can utilize a deep learning-based predictive network to identify parameters of the ultrasound image, including anatomical scan windows, probe orientation, target location, and / or other parameters. In some embodiments, the processor 134 can receive metrics or perform various calculations regarding the physiological state of the acquired region of interest or target during the imaging procedure. These metrics and / or calculations can also be displayed to the ultrasound operator or other users via the display 132.

[0029] Figure 2 is a schematic diagram of a processor circuit according to an aspect of the present disclosure. One or more processor circuits may be configured to perform the operations described herein. The processor circuit 210 may be implemented in the probe 110, the host system 130 in Figure 1, or any other suitable location. For example, the processor 116 of the probe 110 may be part of the processor circuit 210. For example, the processor 134 and / or memory 138 may be part of the processor circuit 210. In one example, the processor circuit 210 may communicate with the transducer array 112, the beamformer 114, the communication interface 122, the communication interface 136, and / or the display 132, and any other suitable components or circuits in the ultrasonic system 100. As shown, the processor circuit 210 may include a processor 260, a memory 264, and a communication module 268. These elements may communicate with each other directly or indirectly, for example, via one or more buses.

[0030] The processor 260 may include a CPU, GPU, DSP, application-specific integrated circuit (ASIC), controller, FPGA, another hardware device, a firmware device, or any combination thereof, configured to perform the operations described herein. The processor 260 may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors working with a DSP core, or any other such configuration. The processor 260 may also include an analysis module, as described in more detail below. The analysis module may implement various machine learning algorithms, which may be implemented in hardware or software. The processor 260 may further include a preprocessor in either a hardware or software implementation. The processor 260 may execute a variety of instructions, including instructions stored on a non-temporary computer-readable medium such as memory 264.

[0031] Memory 264 may include cache memory (e.g., the cache memory of processor 260), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), field-programmable gate array read-only memory (PROM), erasable field-programmable gate array read-only memory (EPROM), electrically erasable field-programmable gate array read-only memory (EEPROM), flash memory, solid-state memory devices, hard disk drives, other forms of volatile and non-volatile memory, or combinations of different types of memory. In some cases, memory 264 includes non-temporary computer-readable media. Memory 264 can store instructions 266. When executed by processor 260, instructions 266 may include instructions that cause processor 260 to perform the operations described herein with reference to probe 110 and / or host 130 (Figure 1). Instructions 266 are sometimes referred to as code. The terms “instruction” and “code” should be broadly interpreted to include any type of computer-readable statement. For example, the terms “instruction” and “code” may refer to one or more programs, routines, subroutines, functions, procedures, etc., and “instruction” and “code” may include a single computer-readable statement or many computer-readable statements. Instruction 266 may include a preprocessor, a machine learning algorithm, a convolutional neural network (CNN), or various other instructions or various forms of code. In some embodiments, memory 264 may be or include a non-temporary computer-readable medium. The communication module 268 may include any electronic and / or logic circuits to facilitate direct or indirect communication of data between the processor circuit 210, the probe 110, and / or the host 130. In this regard, the communication module 268 may be an input / output (I / O) device. In some cases, the communication module 268 facilitates direct or indirect communication between various elements of the processor circuit 210 and / or the probe 110 (Figure 1) and / or the host 130 (Figure 1).

[0032] Figure 3 is a flowchart of a method according to an aspect of the present disclosure for acquiring an ultrasound image and guiding a user of an ultrasound imaging system to position an ultrasound imaging probe. As shown in the figure, method 300 includes several enumerated steps, but aspects of method 300 may include additional steps before, after, or between the enumerated steps. In some aspects, one or more of the enumerated steps may be omitted, performed in a different order, or performed simultaneously. The steps of method 300 may be performed by appropriate components within system 100, and not all steps need to be performed by the same component. In some aspects, one or more steps of method 300 may be performed by, or in that manner by, a processor circuit including, for example, processor 116 (Figure 1), processor 134 (Figure 1), processor 260 (Figure 2), or any other appropriate component.

[0033] Aspects of Method 300 can describe a method for providing guidance to non-expert ultrasound imaging system users in order to acquire high-quality ultrasound images. As described below, these ultrasound images may include views of areas of the patient's anatomical structure. Various aspects of Method 300 and Figure 3 will be described with reference to Figures 4A to 4C, schematic diagrams of a graphical user interface 400 that displays a user guide for acquiring ultrasound images according to aspects of this disclosure.

[0034] In some embodiments, method 300 can be implemented by an ultrasound imaging system 100, for example by a processor 134 and / or processor 210, to ensure that the user acquires ultrasound images with minimal shortening. To do so, the ultrasound imaging system 100 can ensure that the probe orientation is correct during image acquisition. Probe orientation can be described as including at least two components, namely (1) the position of the probe or intercostal muscle in the intercostal space through which the ultrasound probe passes to acquire an image, and (2) the angle of the ultrasound transducer probe. In some embodiments, probe orientation may include other components such as the inclination of the ultrasound transducer along one intercostal space or the longitudinal position of the probe.

[0035] In step 302, method 300 includes receiving 2D ultrasound / echocardiographic images. Receiving ultrasound images may include a processor that controls a transducer array to acquire ultrasound images. In some embodiments, the ultrasound images show anatomical structures of the heart, such as the cardiac chambers (left ventricle, left atrium, right ventricle, right atrium). The ultrasound images may be a time-series of image frames (e.g., ultrasound video or video clip). The ultrasound images may be live ultrasound images received during live acquisition (e.g., in real time or near real time). The images received in step 302 may include any suitable type of ultrasound image. For example, in some embodiments, the received images may include B-mode images, Doppler images, and / or combinations thereof. Furthermore, in step 302, any suitable type of ultrasound imaging system may be used to acquire ultrasound images. For example, the ultrasound imaging system may be similar to the ultrasound imaging system 100 described with reference to Figure 1.

[0036] In step 304, method 300 includes verifying that the images received in step 302 correspond to at least one complete cardiac cycle. In step 304, the ultrasound imaging system 100 may ensure that the images received in step 302 are sufficient to perform the subsequent steps of method 300. Verifying that the images received in step 302 correspond to at least one complete cardiac cycle may include comparing the ultrasound images with corresponding reference images and determining whether a particular characteristic or feature is present in the ultrasound images. In some embodiments, verifying the images received in step 302 may include comparing the number of received ultrasound images with an expected number or threshold number. The expected number or threshold number of images may be calculated based on various factors such as the patient's heart rate, the frame rate of the ultrasound imaging system, or other factors. In some embodiments, a user of the ultrasound imaging system may verify that the images received in step 302 correspond to a complete cardiac cycle, or the ultrasound system may perform this verification automatically, for example, through a machine learning network in the ultrasound imaging system 100. For example, a machine learning network can be trained to analyze and / or compare received ultrasound images and determine whether the images correspond to a complete cardiac cycle.

[0037] In step 306, method 300 includes extracting features from the 2D echocardiographic image received in step 302. Step 306 is described together with step 308. Note that step 306 may be performed optionally. For example, in step 308, the method includes evaluating the probe orientation and apical shortening of the image received in step 302 based on the received image. This evaluation may be performed based on the image itself and / or based on features extracted from the image. For example, when the image is received in step 302, the ultrasound probe is in a specific position. In step 308, the ultrasound system is tasked with determining what the probe position is and whether the probe position corresponds to an ideal probe position with respect to the degree of apical shortening observed in the acquired image. In some embodiments, the ultrasound imaging system can automatically determine the position of the ultrasound probe based solely on the image without extracting specific features from the image. For example, the ultrasound imaging system may include a machine learning network trained to determine the probe position based on the received ultrasound image. For example, the machine learning network may receive the image received in step 302 as input. These images can correspond to different views of the patient's anatomical structure. Some images, for example, can depict the left ventricle of the patient's heart from various angles. A machine learning network may be trained to distinguish between images of the left ventricle from different angles.

[0038] In a configuration in which the ultrasound imaging system performs step 306 and features are extracted from the image received in step 302, the machine learning network of the ultrasound system can receive the extracted features of the received image as input. The ultrasound imaging system 100 may be configured to extract any appropriate features from the received ultrasound image. In particular, the ultrasound imaging system 100 can identify any appropriate features in the image by any appropriate machine learning network or algorithm, or by various image processing techniques. For example, if a particular region of interest of the patient's anatomical structure includes the patient's heart, the extracted features may include the visibility of the mitral valve and apex, the length of the left ventricle, the sphericity of the ventricle, the ratio of the left ventricle to the left atrial area, or any other appropriate features. The presence, shape, or location of any of these features in the image received in step 302 can then be used to determine the direction of the probe related to the received image.

[0039] In some embodiments, the ultrasound system 100 may be configured to receive both the ultrasound image itself and extracted features of the ultrasound image as input for determining the orientation of the ultrasound transducer probe. In some embodiments, determining the orientation of the probe may include determining the intercostal space of the probe. In other words, determining the orientation of the probe may include determining which space between the patient's ribs the probe was directed into during image acquisition. In some embodiments, ensuring that the received ultrasound image includes minimal shortening may include both determining whether the ultrasound probe is positioned in the appropriate intercostal space and determining whether the angle of the ultrasound transducer probe is correct. In some embodiments, as shown in Figure 3, the ultrasound transducer system 100 may first determine whether the ultrasound imaging probe is positioned in the appropriate intercostal space and, if necessary, provide the user with a guide to move the probe into the appropriate intercostal space, and then, as described below, determine whether the probe angle is correct and, if necessary, provide a corresponding guide.

[0040] In step 310, method 300 includes determining whether the ultrasound transducer probe is positioned in the correct intercostal space. In some embodiments, step 310 may include evaluating the input or output of a machine learning network described with reference to step 308. For example, the output of the machine learning network in step 308 may include designation of an intercostal space. This intercostal space may be associated with a specific number, a side of the patient's body, or any other appropriate nomenclature. In some embodiments, if the ultrasound imaging system 100 determines that the intercostal space is incorrect, the system 100 proceeds to step 312 of method 300, as shown in Figure 3. However, if the intercostal space is correct, the ultrasound imaging system 100 can proceed to step 316 of method 300.

[0041] In step 312, method 300 includes outputting guidance to the user of the ultrasound imaging system 100 to adjust the position of the ultrasound imaging probe. In some embodiments, this guidance may include guidance that the position needs to be moved in any direction. In some embodiments, the guidance to the user may include a recommended direction for the ultrasound imaging probe. For example, the ultrasound imaging system 100 may recommend that the user move the probe downward or upward. In some embodiments, the ultrasound imaging system 100 may provide guidance to the user to move the ultrasound imaging probe to the other side of the patient. In some embodiments, the ultrasound imaging system 100 may output guidance to the user to move the ultrasound imaging probe upward or downward and may provide a recommendation of the number of intercostal spaces to move the probe. For example, the probe may determine that the current probe position must be moved upward by two intercostal spaces and may provide guidance on the effect thereof. Any of the guidance output to the user in step 312 may include any guidance shown and described with reference to Figure 9.

[0042] In step 314, method 300 includes updating the apical shortening indicator. Referring to Figure 4A, the apical shortening indicator 440 may be shown on an exemplary graphical user interface 400.

[0043] As previously described, Figure 4A is a schematic diagram of a graphical user interface 400 displaying a user guide for acquiring ultrasound images according to an aspect of the present disclosure. As shown, the graphical user interface 400 may be presented on the display of the device 410. Device 410 may be a suitable apparatus. In the example shown in Figure 4A, device 410 may be a smartphone. However, in other implementations, device 410 may alternatively be a computer including a tablet, mobile laptop or desktop computer, a host system for an ultrasound imaging system, or any other suitable apparatus. In some aspects, method 300 and the corresponding graphical user interface 400 may be particularly suitable for a mobile application used by a novice user in a non-ideal set of procedures, where the guidance from the ultrasound imaging system 100 can help the user acquire ultrasound images of ideal quality and perform minimal apical shortening.

[0044] As shown in the figure, the graphical user interface 400 includes an ultrasound image 420, a measurement indicator 422, and a scale 424. In some embodiments, the ultrasound image 420 may correspond to a live image received at a point of care set in the ultrasound imaging procedure. In some embodiments, the ultrasound image 420 may correspond to a previously acquired image, including an image from a previous procedure or an image received by the ultrasound system 100 earlier in the current procedure. The measurement indicator 422 and scale 424 can assist the user in determining distances within the ultrasound image 420 and can assist the user in performing various diagnoses as needed.

[0045] The graphical user interface 400 also includes annotation buttons 432, image save buttons 434, and measurement buttons 436. In some embodiments, measurement indicators 422 and scales 424 may be presented to the user within the graphical user interface 400 in response to the user selecting the measurement button 436. In response to the user selecting the save image button 434, the ultrasound image 420 may be stored in the device 410's memory or in another memory associated with the device 410. For example, the device 410 may be wired or wirelessly connected to a storage device such as an external memory or cloud storage server that can store the ultrasound image 420 and any associated data, as well as any appropriate data related to the imaging procedure or patient. In some embodiments, the graphical user interface 400 may be modified to include various displays, buttons, or indicators that enable the user to provide notes associated with or overlaid on the ultrasound image 420. In some embodiments, the graphical user interface may also be modified to enable the user to highlight, contour, or otherwise annotate the ultrasound image 420 and / or its corresponding features, metrics, or indicators.

[0046] Furthermore, Figure 4A shows several metrics 426. These metrics may include or correspond to any appropriate data. For example, metric 426 may include metrics or data related to the ultrasound image 420. For example, metric 426 may include measurements of the image 420, including distance measurements, volume measurements, or area measurements. In some embodiments, metric 426 may more broadly include data, measurements, or settings of the ultrasound system 100 during a particular imaging procedure. For example, metric 426 may include frame rate, procedure duration, gain setting of ultrasound imaging system 100, depth setting of ultrasound imaging system 100, probe transducer type, pattern, number, or group of transducers used in the imaging procedure, description of the patient's region of interest, or view of the ultrasound image, description of features extracted from the ultrasound image 420, power setting of ultrasound imaging system 100, or any other appropriate metrics, settings, or characteristics.

[0047] Figure 4A further includes the apical shortening indicator 440. In some embodiments, the apical shortening indicator 440 may inform the user of the system 100 of the degree of apical shortening observed in a received ultrasound image, such as image 420, and which components of the probe direction are correct or incorrect. For example, the apical shortening indicator 440 may include three distinct regions, namely region 442, region 444, and region 446. The apical shortening indicator 440 further includes a graphical element 448 overlaid on any of regions 442, 444, and / or 446. The position of the graphical element 448 can provide the user with different guidance. For example, referring again to Figure 3, if the ultrasound imaging system 100 determines in step 310 that the probe position is not in the correct intercostal muscle position, in step 314 the apical shortening indicator 440 (Figure 4A) may be updated so that the graphical element 448 is positioned within region 442. In this regard, region 442 may accommodate an inaccurate probe position, or the probe may be positioned in an inaccurate intercostal space.

[0048] In some embodiments, the position of the graphical element 448 can provide additional guidance to the user. For example, if the probe position is away from the correct intercostal space (e.g., four or five intercostal spaces), the graphical element 448 may be positioned in the right section of region 442. On the other hand, if the probe position is incorrect but close to the correct intercostal space (e.g., one intercostal space away), the graphical element 448 may be positioned in the left section of region 442. In this regard, if the user of the ultrasound imaging system 100 initially positions the probe in the wrong intercostal space, the ultrasound imaging system 100 can update the graphical user interface using the graphical element 448 in region 442. When the user moves the probe to a different intercostal space, the graphical element 448 may move in real time so that if the user observes the graphical element 448 moving to the right in region 442, the user knows that they are moving the probe in the wrong direction and can reverse the direction. When he or she does so, the graphical element 448 may be updated to move to the left.

[0049] Figure 3 also shows and illustrates this real-time update of the apical shortening indicator 440, including the position of the graphical element 448. In particular, after the apical shortening indicator 440 is updated in step 314 to move the graphical element 448 to the appropriate position, the ultrasound imaging system 100 returns to step 302 of method 300, where additional images are received. Steps 304 to 310 are then performed again, and the ultrasound imaging system 100 may update the apical shortening indicator 440 again according to the new position of the ultrasound imaging probe. It should be noted that in some embodiments, the apical shortening indicator 440 may be of any suitable appearance or type. For example, additional embodiments are shown and described with reference to the apical shortening indicator 640 in Figures 6A to 6C below. However, additional types of apical shortening indicators are expected. For example, the graphical element 448 may be of any suitable appearance, or the appearance of the graphical element 448 may be modified to reflect the probe position in any suitable way. In some embodiments, the apical shortening indicator 440 may alternatively not include the graphical element 448. For example, if the ultrasound imaging system 100 determines in step 310 that the probe is not in the correct intercostal muscle position, region 442 may be highlighted over regions 444 and 446, or otherwise may be highlighted, so that the user knows that the probe is not in the correct intercostal muscle position.

[0050] Referring again to Figure 3, if the ultrasound system 100 determines in step 310 that the probe is positioned in the correct intercostal space, method 300 proceeds to step 316. In step 316, method 300 includes outputting a guide to the user to maintain the probe position. This guide may be of any appropriate type, including visual elements such as text, shapes, symbols, colors, or any other visual appearance, as well as any appropriate type of auditory or tactile feedback. In some embodiments, the guide may include any guide shown and described with reference to Figure 9. In some embodiments, the guide for maintaining the probe position may be provided in the form of updating the apical shortening indicator in step 322 or 324 as described below.

[0051] In step 318, method 300 includes determining whether the ultrasonic transducer probe is held at the correct angle. In some embodiments, step 318 may include evaluating the input or output of a machine learning network described with reference to step 308, similar to step 310 described above. For example, the output of the machine learning network in step 308 may include specifying or quantifying the angle of the ultrasonic imaging probe. This quantification may include an angle in degrees or radians, or may include a set of coordinates corresponding to the angle, tilt, fan, or rock of the ultrasonic transducer probe in any direction. In some embodiments, determining whether the probe angle is correct may include comparing the determined probe angle with a previously stored ideal angle. For example, the ultrasonic imaging transducer may determine that the probe angle is within an acceptable range of the ideal angle, which includes, for example, a certain number of degrees or radians above or below the ideal angle. In some embodiments, if the ultrasonic imaging system 100 determines that the angle is incorrect, for example, by falling outside this range, the system 100 proceeds to step 320 of method 300, as shown in Figure 3. However, if the probe angle is correct, the ultrasound imaging system 100 can proceed to step 324 of method 300.

[0052] In step 320, method 300 includes outputting a guide to the user of the ultrasound imaging system 100 for adjusting the angle of the ultrasound imaging probe. In some embodiments, this guide may include a guide stating that the angle should be adjusted in any direction. In some embodiments, the guide to the user may include a recommended direction for angle adjustment for the ultrasound imaging probe. In some embodiments, the ultrasound imaging system 100 may output a guide to the user for adjusting the probe angle by a specified amount, such as a specified number of degrees or radians. This guide can be output in any suitable manner. For example, this guide may be output as text overlaid on any feature of the graphical user interface 400 shown in Figures 4A–4C. The guide provided in step 320 may include any of the guides shown and described with reference to Figure 10.

[0053] In step 322, method 300 includes updating the apical shortening indicator. Referring to Figure 4B, the updated apical shortening indicator 440 in step 322 is shown. If the probe position is determined to be correct (for example, in step 310), but the probe angle is determined to be incorrect (for example, in step 318), the apical shortening indicator 440 can be updated to move the graphical element 448 to area 444, as shown in Figure 4B. In this regard, area 444 may correspond to an instruction to the user that the probe is in the correct intercostal muscle position (and the probe position should be maintained), but the angle is incorrect and should be adjusted.

[0054] Similar to region 442 in Figure 4A, in some embodiments, the position of the graphical element 448 within region 444 can provide additional information regarding the probe direction, particularly the probe angle. For example, if the current probe angle is significantly inaccurate, the position of the graphical element 448 may be moved to the right section of region 444. However, if the position of the graphical element 448 is close to the ideal probe angle but outside the threshold range, the graphical element 448 may be moved to the left section of region 444. Thus, the user can quickly determine in real time whether they are adjusting the probe angle in the correct direction and adjust accordingly. Similarly, if the apical shortening indicator 440 does not include the graphical element 448, region 444 may be highlighted alternatively or in other ways, as described with reference to region 442.

[0055] Referring again to Figure 3, after the apical shortening indicator is updated in step 322, for example by moving the graphical element 448 into region 444, method 300 returns to step 302 to acquire additional images. After the subsequent steps are performed, if the ultrasound imaging system 100 determines that the probe position is maintained in the correct intercostal space in step 310 and the probe angle is correct in step 318, the ultrasound imaging system 100 can proceed to step 324.

[0056] In step 324, method 300 includes outputting a guide to the user to maintain the probe angle. This guide may be of any suitable type, including visual elements such as text, shapes, symbols, colors, or any other visual appearance, as well as any suitable type of auditory or tactile feedback. In some embodiments, the guide provided may include any of the guides shown and described with reference to Figure 10. In some embodiments, the guide for maintaining the probe angle may be provided in the form of updating the apical shortening indicator in step 326.

[0057] In step 326, method 300 includes updating the apical shortening indicator 440. Referring to Figure 4C, if both the probe position and probe angle are correct, the apical shortening indicator 440 may be updated so that the graphical element 448 moves within region 446. In this regard, region 446 may correspond to the correct orientation of the probe, including its position and angle. In embodiments where the apical shortening indicator 440 does not include the graphical element 448, as previously described with respect to regions 444 and 442, region 446 may be highlighted or emphasized in any appropriate manner to indicate that both the probe position and angle are correct.

[0058] In some embodiments, regions 442, 444, and 446 can be visually distinguished from one another in any suitable way. For example, regions 442, 44, and 446 may be represented by different colors. In some embodiments, region 442 may correspond to red, region 44 to yellow, and region 446 to green. Any other suitable color may also be used. In some embodiments, regions 442, 444, and 446 can be visually distinguished from one another by any other alternative way, for example, by using different patterns, contours, or any other visual characteristics.

[0059] In some embodiments, Method 300 may include performing additional processing of ultrasound images obtained with minimal apical shortening (for example, according to steps 302 to 326). Additional processing of ultrasound images may include automatically identifying anatomical structures in the images and quantifying anatomical values ​​in the images (such as ejection fraction (EF), stroke volume (SV), and global longitudinal strain (GLS)). In some embodiments, these steps are performed automatically after the ultrasound images with minimal apical shortening are stored in memory, without receiving user input for the processor to initiate identification of anatomical structures and / or quantify anatomical values. The Method may also include outputting graphic / visual representations related to the identified anatomical structures and / or quantified anatomical values. Using ultrasound images with minimal apical shortening is advantageous in improving the accuracy of identifying anatomical structures and / or quantifying anatomical values. Embodiments for identifying anatomical structures and / or quantifying anatomical values ​​are described by underlined sections, which are incorporated herein by reference.

[0060] Figure 5 is a flowchart of a method 500 according to an aspect of the present disclosure for acquiring an ultrasound image and guiding a user of an ultrasound imaging system to position an ultrasound imaging probe. In some aspects, method 500 may be similar to method 300. As shown in the figure, method 500 includes several enumerated steps, but aspects of method 500 may include additional steps before, after, or between the enumerated steps. In some aspects, one or more of the enumerated steps may be omitted, performed in a different order, or performed simultaneously. The steps of method 500 may be performed by appropriate components within system 100, and not all steps need to be performed by the same component. In some aspects, one or more steps of method 500 may be performed by, or in that manner by, a processor circuit including, for example, processor 116 (Figure 1), processor 134 (Figure 1), processor 260 (Figure 2), or any other appropriate component.

[0061] Aspects of Method 500 will be described with reference to Figures 6A to 6C. Figures 6A to 6C are schematic diagrams of a graphical user interface 600 that displays a user guide for acquiring ultrasound images, according to an aspect of this disclosure.

[0062] In step 502, method 500 includes receiving 2D ultrasound / echocardiography images from multiple intercostal spaces. Receiving ultrasound images may include a processor that controls a transducer array to acquire ultrasound images. In some embodiments, the ultrasound images show anatomical structures of the heart, such as the cardiac chambers (left ventricle, left atrium, right ventricle, right atrium). The ultrasound images may be a time-series of image frames (e.g., ultrasound video or video clip). The ultrasound images may be live ultrasound images received during live acquisition (e.g., in real time or near real time). The images received in step 302 may include any suitable type of ultrasound image. For example, in some embodiments, the received images may include B-mode images, Doppler images, and / or combinations thereof. Furthermore, in step 502, any suitable type of ultrasound imaging system may be used to acquire ultrasound images. For example, the ultrasound imaging system may be similar to the ultrasound imaging system 100 described with reference to Figure 1.

[0063] In some embodiments, the ultrasound imaging system 100 instructs the user to acquire images from multiple intercostal muscle spaces. For example, the ultrasound imaging system 100 can output a guide to the user for acquiring images from multiple intercostal muscle spaces on a screen display that communicates with a processor. This guide may be of any suitable type, including, for example, text, graphical elements, or any other suitable type of guide.

[0064] In some embodiments, the ultrasound imaging system 100 may perform any of the steps of the method 300 described above to identify the intercostal spaces of all images received in step 502. For example, after determining that a received image or a set of received images were acquired in a particular intercostal space, the ultrasound imaging system 100 may provide a guide to the user to move the ultrasound probe upward or downward, such as moving the probe upward or downward through one intercostal space, to acquire further images. In some embodiments, the ultrasound imaging system 100 may instruct the user to acquire images from a predetermined number of intercostal spaces, such as two, three, four, or more. These intercostal spaces may be directly adjacent to each other or may be spaced apart according to any suitable pattern or plane.

[0065] In step 504, method 500 includes verifying that the images received in step 502 correspond to at least one complete cardiac cycle. In some embodiments, step 504 may also include verifying that sufficient images were received in step 502 to confirm that images of a complete cardiac cycle value were received in each intercostal space. In step 504, the ultrasound imaging system 100 can ensure that the images received in step 502 are sufficient to perform the subsequent steps of method 500. Verifying that the images received in step 502 correspond to at least one complete cardiac cycle may include comparing the ultrasound images to corresponding reference images and determining whether a particular characteristic or feature is present in the ultrasound images. In some embodiments, verifying the images received in step 502 may include comparing the number of received ultrasound images to an expected number or threshold number. The expected number or threshold number of images may be calculated based on various factors such as the patient's heart rate, the frame rate of the ultrasound imaging system, or other factors. In some embodiments, the user of the ultrasound imaging system can verify that the image received in step 502 corresponds to a complete cardiac cycle, or the ultrasound system can perform this verification automatically, for example, through a machine learning network in the ultrasound imaging system 100. For example, the machine learning network can be trained to analyze and / or compare the received ultrasound images and determine whether the image corresponds to a complete cardiac cycle.

[0066] In step 506, method 500 includes extracting features from the 2D echocardiographic image received in step 502. Step 506 is described together with step 508. It should be noted that step 506 may be performed optionally. For example, in step 508, the method includes calculating an apical shortening indicator metric (AFIM) for the image received in step 502. This calculation can be performed based on the image itself and / or on features extracted from the image. For example, calculating the AFIM may include any suitable technique or method described with reference to method 300. In this regard, calculating the AFIM may be based at least in part on the position of the ultrasound probe and / or the angle of the ultrasound probe as determined by the ultrasound imaging system 100.

[0067] In some embodiments, AFIM can be any appropriate value. For example, AFIM may be a range value, such as a number between 1 and 5, as an unrestricted example. In some embodiments, AFIM may be a percentage, a ratio, a rank such as a letter or number rank, or any other appropriate type of metric.

[0068] In some embodiments, in step 508, the ultrasound imaging system 100 may be configured to calculate AFIM for each intercostal space. For example, in step 502, images received in the same intercostal space may be designated as such by annotation and / or by grouping these images together and storing them in memory. The ultrasound imaging system 100 can calculate AFIM for each set of images in the group corresponding to each intercostal space. In some embodiments, the ultrasound imaging system 100 may calculate AFIM for each received image. In some embodiments, the ultrasound imaging system 100 may calculate AFIM for each received image and calculate AFIM for all received images corresponding to one intercostal space. In this regard, AFIM may be assigned to an intercostal space.

[0069] In some embodiments, the AFIM value may correspond to the amount of apical shortening observed in the corresponding image or set of images. In this regard, a high AFIM value may correspond to a large amount of apical shortening observed in the corresponding image or set of images, which may be undesirable, while a low AFIM value may correspond to a small amount of apical shortening observed in the corresponding image or set of images.

[0070] In step 510, method 500 includes selecting a 2D echocardiographic image having the least apical shortening as a reference image. In this regard, the AFIM calculated for all images received in step 502 can be compared, regardless of the intercostal space from which each image was acquired. The ultrasound system 100 may select the image with the minimum AFIM as the image with the least apical shortening. The ultrasound imaging system 100 may then annotate the selected image or otherwise designate it as a reference image. In some embodiments, the reference image may be alternatively referred to as a hierarchical image.

[0071] In some embodiments, the ultrasound imaging system 100 can alternatively compare AFIM for each intercostal space. In this regard, the AFIM can be compared for each set of images corresponding to each intercostal space. The ultrasound imaging system 100 can then select the lowest AFI as the reference AFIM and designate the corresponding intercostal space as the reference intercostal space or target intercostal space. The reference AFIM is sometimes alternatively referred to as the hierarchical AFIM.

[0072] In step 512, method 500 includes outputting a reference AFIM. The ultrasound imaging system 100 may further output a selected reference image, reference AFIM, or reference intercostal space to the display. This output may be of any appropriate type.

[0073] Figure 6A provides an example of a baseline AFIM output. Referring to Figure 6A, an exemplary graphical user interface 600 is shown. The graphical user interface 600 includes an apical shortening indicator 640. The apical shortening indicator 640 may be similar to the apical shortening indicator 440 described above. In this regard, the apical shortening indicator 640 includes a graphical element 646. The graphical element 646 may represent a baseline AFIM. For example, the apical shortening indicator 640 may correspond to a spectrum of AFIM values. In some embodiments, the right region 642 of the apical shortening indicator 640 may correspond to a high AFIM value. Conversely, the left region 644 may correspond to a low AFIM value, and the position between these two regions may correspond to a continuous spectrum of AFIM values ​​between two extreme values. In some embodiments, the apical shortening indicator 640 may be accompanied by a scale such that the user can determine the absolute AFIM value corresponding to the graphical element 646 by observing the position of the graphical element 646 along the scale.

[0074] In step 514, method 500 includes outputting a guide to the user for directing the ultrasound imaging probe to the intercostal space from which a reference image has been acquired. In this regard, the ultrasound imaging system 100 may output a visual guide, for example, by means of an image, stylized graphics, or any other suitable visual element that points to the patient's intercostal space. Such output may also include a text guide. The guide provided in step 514 may include any of the guides shown and described with reference to Figure 9.

[0075] In step 516, method 500 includes receiving a new 2D echocardiographic image from the intercostal space of a reference image. After the user positions the probe in the selected intercostal space according to the guidance of step 514, the ultrasound imaging system 100 may begin receiving one or more additional images.

[0076] In step 518, method 500 includes extracting features from the newly received 2D echocardiographic image received in step 516. In some embodiments, extracting features from the new image in step 518 may include any of the same procedures or methods described in step 500 of method 300 and / or step 506 of step 306.

[0077] In step 520, method 500 includes calculating AFIM for newly received 2D echocardiographic images. The manner in which AFIM is calculated in step 520 may include any of the procedures or methods described in step 508 of method 500.

[0078] In step 522, method 500 includes determining whether the new AFIM value is less than the AFIM value of the reference image. Step 522 may include comparing the AFIM value calculated in step 520 with the reference AFIM value calculated in step 508. If the new AFI value calculated in step 520 is greater than the reference AFI value calculated in step 508, the apical shortening indicator 640 may be updated, and method 500 returns to step 516.

[0079] Figure 6B provides an exemplary graphical user interface that may be displayed to the user after the ultrasound imaging system 100 determines that the new AFIM value is greater than the reference AFIM value. Referring to Figure 6B, since the new AFIM value was greater than or equal to the reference AFIM value, the graphical element 646 corresponding to the reference AFIM value remains unchanged. The graphical element 646 remains in the same position along the apical shortening indicator 640.

[0080] In some embodiments, the apical shortening indicator may be updated to include a graphical element 648. The graphical element 648 may be positioned along the apical shortening indicator 640 to show the AFIM value of the image received in step 516. As shown in Figure 6B, the graphical element 648 is positioned to the right of the graphical element 646 because the AFIM value of the new image is smaller than the reference AFIM value. After the graphical user interface 600 has been updated, steps 516 to 522 may be performed again.

[0081] In step 522, if the ultrasound imaging system 100 determines that the AFIM value of the newly received image (for example, the one received in step 516) is less than the reference AFIM value, the apical shortening indicator 640 is updated again, and method 500 proceeds to step 524.

[0082] Figure 6C provides an example of the graphical user interface 600 after the ultrasound imaging system 100 determines in step 522 that the AFIM value of the newly received image is less than the reference AFIM value. Referring to Figure 6C, the graphical element 646 may be moved to the left compared to the position of the graphical element 646 in Figure 6A or 6B. In this regard, the ultrasound imaging system 100 can designate the newly received image as a new reference image and designate the AFIM value of the newly received image as a new reference AFIM value.

[0083] As shown in Figure 5, after step 524 is performed, method 500 returns to step 516, and additional images may be received. In this regard, the ultrasound imaging system 100 can iteratively perform steps 516 to 524 of method 500 and thus improve the reference AFIM value. This may proceed until the user terminates the procedure. In some embodiments, the procedure may terminate when the reference AFIM value is below a predetermined threshold. In some embodiments, the ultrasound imaging system 100 may be configured to specify a reference image as an image to be used to perform any appropriate calculations or measurements for the ultrasound imaging procedure. In this regard, the reference image may be an image with the shortest apical shortening that yields the most accurate measurement possible despite the user's lack of experience or less-than-ideal procedure settings.

[0084] In some embodiments, Method 500 may include performing additional processing of ultrasound images obtained with minimal apical shortening (for example, according to steps 502 to 524). Additional processing of ultrasound images may include automatically identifying anatomical structures in the images and quantifying anatomical values ​​in the images (such as ejection fraction (EF), stroke volume (SV), and global longitudinal strain (GLS)). In some embodiments, these steps are performed automatically after the ultrasound images with minimal apical shortening are stored in memory, without receiving user input for the processor to initiate identification of anatomical structures and / or quantify anatomical values. The Method may also include outputting graphic / visual representations related to the identified anatomical structures and / or quantified anatomical values. Using ultrasound images with minimal apical shortening is advantageous in improving the accuracy of identifying anatomical structures and / or quantifying anatomical values. Embodiments for identifying anatomical structures and / or quantifying anatomical values ​​are described by underlined sections, which are incorporated herein by reference.

[0085] Figure 7 is a schematic diagram of a machine learning algorithm 700 according to an aspect of the present disclosure. The machine learning algorithm 700 may also be called an artificial intelligence framework. In this regard, the artificial intelligence framework 700 may include a machine learning network, and various aspects of the framework may be executed by the processor 134 and / or processor circuit 210, and may include instructions similar to the instructions 266 described above. The machine learning algorithm 700 may also be a deep learning algorithm in some embodiments. The processor circuit 210 may be configured to use the machine learning network to determine the intercostal space in which the ultrasound image is received, to determine the probe angle in which the ultrasound image is received, and / or to calculate the apical shortening metric. The embodiment of the artificial intelligence framework 700 shown in Figure 7 includes various input ultrasound images 710. The ultrasound images 710 may be received from various sources such as an ultrasound imaging system 100, a picture archiving and communication system (PACS), or other image storage systems. The ultrasound images 710 may be received or arranged in chronological order. For example, the ultrasound images 710 may be acquired by the imaging system 100 during the ultrasound imaging procedure. The framework 700 may include an analysis module 720 having a preprocessor 722 and a deep learning network 724. In some embodiments, the analysis module 720 may be implemented in a host 130 (Figure 1) and / or a processor circuit 210 (Figure 2). The artificial intelligence framework 700 may generate a plurality of outputs 730. The machine learning algorithm 700 may be trained to calculate an apical shortening metric as output 732, an intercostal space indicator as output 734, and an angle indicator as output 736.

[0086] Training of the machine learning algorithm 700 can be achieved using a variety of different techniques. In one embodiment, training the deep learning network can be achieved by creating a large dataset of sample ultrasound images showing changes in apical shortening, including variations in intercostal space and probe angle. Sample images can further be obtained from a large number of patients. The deep learning network can be trained using multiple annotated ultrasound images. Each of the multiple annotated ultrasound images contains an annotation corresponding to one of outputs 732, 734, and / or 736.

[0087] The analysis module 720 may include a preprocessor 722 which may include hardware (e.g., electrical circuit components) and / or software algorithms (e.g., executed by a processor). The preprocessor 722 may perform various functions to adjust, filter, or otherwise modify the received input image 710 before sending it to the deep learning network 724. For example, the preprocessor 722 may manipulate the incoming image. The preprocessor 722 may perform various image processing steps for both training and prediction purposes. The preprocessor 722 may perform various tasks such as modifying the contrast, size, resolution, orientation, geometry, cropping, spatial transformation, resizing, normalization, histogram correction, or various other procedures to help the deep learning network 724 operate more efficiently or accurately.

[0088] The deep learning network 724 can receive the processed image 710 as input from the preprocessor 722. The deep learning network 724 may include hardware (e.g., electrical circuit components) and / or software algorithms (e.g., executed by the processor). The deep learning network 724 can then identify various parameters associated with the received ultrasound image 710.

[0089] The deep learning network 724 may include a convolutional neural network (CNN) in some embodiments. For example, the CNN may be or may include a multi-class classification network or an encoder-decoder type network. In some cases, the analysis module may implement any type of classification process, such as a random forest algorithm, a classification tree approach, a convolutional neural network, or any type of deep learning network. In some cases, instead of a deep learning network, the analysis module may implement a statistical model or a random forest algorithm based on image or ultrasound backscatter derivation parameters.

[0090] Figure 8 is a schematic diagram of a convolutional neural network (CNN) configuration 800 according to an aspect of the present disclosure. For example, the CNN configuration 800 can be implemented as a deep learning network 724 (Figure 7). In one embodiment, configuration 800 can perform a classification task. For example, the convolutional neural network (CNN) can provide a classification label for each pixel of an ultrasound image or signal. Configuration 800 may be of any suitable type and may include, but not be limited to, convolutional layers, fully connected layers, flattening vectors, or any other technique or implementation of an artificial intelligence system, any suitable type or number of layers. Embodiments shown and / or described with reference to Figure 8 can be scaled to include any suitable number of CNNs (e.g., about 2, 3, or more). Configuration 800 can be trained to identify any of the outputs described above.

[0091] A CNN may include a set of N convolutional layers 810, where N is any positive integer, followed by a pooling layer 815. A CNN may also include a set of K fully connected layers 820, where K is any positive integer. In one embodiment, the fully connected layers 820 include at least two fully connected layers 820. The convolutional layers 810 are denoted as 810(1) through 810(N). The pooling layers 815 are denoted as 815(1) through 815(N). The fully connected layers 820 are denoted as 820(1) through 820(K). Each convolutional layer 810 may include a set of filters 812 configured to extract features from an input 805 (e.g., an ultrasound image or other additional data). The convolutional layers 810 may include convolutional kernels of different sizes and strides. The values ​​of N and K, as well as the sizes of the filters 812, may vary depending on the embodiment. In some embodiments, the convolutional layers 810(1) to 810(N), the pooling layers 815(1) to 815(N), and the fully connected layers 820(1) to 820(K-1) may utilize sigmoid, rectified nonlinear (ReLU), leak ReLU, softmax, or hyperbolic tangent activation functions. The pooling layer 815 may include max pooling or average pooling techniques. The fully connected layer 820 can gradually reduce the high-dimensional output to the dimension of the prediction result (e.g., the classification output 830). Thus, the fully connected layer 820 may also be called a classifier. In some embodiments, the fully convolutional layer 810 may be further called a perception or perceptive layer. The fully connected layer 820 may downsample the received information and map it to a finite number of classes 832. In one embodiment, a final classification layer, such as a softmax layer, may follow the final fully connected layer 820(K) to convert the net activations in the final output layer into a set of values ​​that can be interpreted as probabilities.

[0092] The classification output 830 may indicate a confidence score or probability for each of several classes 832 based on the input image 805. In this regard, the CNN 800 may be a multi-class classification network. In exemplary embodiments, several classes 832 may include apical shortening metric, intercostal space, and probe angle.

[0093] In embodiments where the deep learning network includes an encoder-decoder network, the network may include two paths. One path may be a shortened path in which a large image, such as image 805, can be convolved by several convolutional layers 810, such that the size of image 805 changes with the depth of the network. Image 805 can then be represented in a lower-dimensional space, or flattened space. From this flattened space, an additional path can extend the flattened space to the original size of image 805. In some embodiments, the implemented encoder-decoder network may also be called a principal component analysis (PCA) method. In some embodiments, the encoder-decoder network may segment image 805 into patches.

[0094] In some embodiments, multiple convolutional neural networks may be implemented to identify different characteristics of the received ultrasound image. For example, one CNN may be trained to identify the apical shortening metric, another CNN may be trained to identify the intercostal muscle space, and an additional CNN may identify the probe angle. Any CNN may be trained to identify any one of these characteristics in the received ultrasound image, including simply one, some, or all of these characteristics.

[0095] In some embodiments, the machine learning network configuration may include a post-processing step. This post-processing step may combine the results of one or more classification or learning algorithms and / or apply logic based on the size and spacing of pixels in an ultrasound image, and this post-processing step may be an additional algorithm to all other algorithms. This post-processing step may be based on both quantitative analysis of label clusters and published reviews, literature, or empirical results from experts in the field, for scoring the ultrasound image or individual pixels of the ultrasound image. In some embodiments, this post-processing step may be the artificial algorithm itself.

[0096] Figure 9 is a schematic diagram of a graphical user interface 900 displaying a user guide for probe position according to an aspect of the present disclosure. As shown, the graphical user interface 900 may be displayed on the screen of the apparatus 410. The graphical user interface 900 may include a graphical representation of the patient 910 and a graphical representation of the ultrasound imaging probe 920. In some aspects, the position of the imaging probe 920 may be selected by the imaging system relative to the representation of the patient 910 so that the position of the probe 920 indicates to the user the correct position of the probe relative to the patient. As also shown in Figure 9, the graphical user interface 900 may include directional arrows 922 and 924. In some aspects, if the ultrasound imaging system determines that the probe 920 should be moved upward, an arrow 922 may be shown on the display. However, if the ultrasound imaging system determines that the probe 920 should be moved downward, an error 924 may be displayed. Similarly, various text guides 930 may be included in the display. The text guides 930 may indicate to the user the type of movement of the ultrasound imaging probe 920. In the example shown in Figure 9, the type of movement may be adjusting the probe position. In some examples, text 930 may include the direction in which the ultrasound imaging probe should be moved. Specifically, text 930 may include the term "up" when the probe is moved upward, and the term "down" when the probe is moved downward. In addition, text 930 may include several intercostal spaces in which the user should move the ultrasound imaging probe. For example, if the ultrasound imaging system determines that probe 920 is not properly positioned and should be moved upward by two intercostal spaces, text 930 may include the term "up" and the number 2, or the term "two intercostal spaces," and similar instructions may be included within text 930. Also, while Figure 9 shows arrows corresponding to downward and upward, additional directions may be indicated by similar arrows. For example, arrows indicating probe movement side by side, or to the opposite side of the patient, may be placed next to probe 920.

[0097] Figure 10 is a schematic diagram of a graphical user interface displaying a user guide for probe angle according to an aspect of the present disclosure. As shown, the graphical user interface 1000 may be displayed on the screen of the apparatus 410. The graphical user interface 1000 may also include a graphical representation of the patient 910, as well as a graphical representation of the ultrasound imaging probe 920. In some aspects, the angle of the imaging probe 920 may be selected by the imaging system relative to the representation of the patient 910 so that the angle of the probe 920 indicates to the user the correct angle of the probe relative to the patient. As also shown in Figure 10, the graphical user interface 1000 may include indicator arrows 1022 and 1024. In some aspects, if the ultrasound imaging system determines that the angle of the probe 920 should be adjusted in the direction corresponding to arrow 1022, this may be indicated on the display. In some aspects, the direction corresponding to arrow 1022 may be selected as the positive direction. In some aspects, the direction corresponding to arrow 1024 may be selected as the negative direction. In this regard, if the ultrasound imaging system determines that the angle of the probe 920 should be adjusted in the opposite direction, an arrow 1024 may be displayed. Similarly, various text guides 1030 may be included in the display. The text guides 1030 may indicate to the user a type of movement of the ultrasound imaging probe 920. In the example shown in Figure 10, the type of movement may be adjusting the probe angle. In some examples, the text 1030 may include a rotational direction for moving the ultrasound imaging probe. Specifically, the text 1030 may include positive or negative values ​​in degrees or radians. In some embodiments, multiple angles may be provided corresponding to multiple axes of rotation. Although arrows 1022 and 1024 are shown in Figure 10, it is expected that further directions may be indicated by similar arrows.

[0098] Those skilled in the art will recognize that the apparatus, systems, and methods described above can be modified in various ways. Accordingly, those skilled in the art will understand that the embodiments covered by this disclosure are not limited to the specific exemplary embodiments described above. In this regard, while exemplary embodiments have been shown and described, a wide range of modifications, changes, and substitutions are contemplated in the aforementioned disclosure. It will be understood that such modifications can be made without departing from the scope of this disclosure. Accordingly, it is appropriate that the appended claims be interpreted broadly to be consistent with this disclosure.

Claims

1. It is an ultrasonic system, A processor configured to communicate with a display, a transducer array of a handheld ultrasound probe, and memory, wherein the processor The steps include controlling the transducer array to acquire multiple ultrasound images corresponding to one or more views of the patient's anatomical structure, The steps include determining a shortened metric corresponding to one or more ultrasound images from the aforementioned plurality of ultrasound images, The steps include outputting a shortened indicator representing the shortened metric to the display, The steps include comparing the shortened metric with one or more predetermined criteria, Steps include outputting guidance to the user of the ultrasound system on the display to adjust the direction of the handheld ultrasound probe in response to the shortening metric not meeting one or more predetermined criteria, and A processor and An ultrasonic system having

2. The aforementioned shortened indicator is Multiple discrete regions associated with different values ​​of the aforementioned shortened metric, Based on the value of the shortened metric, a graphical element is aligned with one of the multiple regions. The ultrasonic system according to claim 1, comprising:

3. The aforementioned shortened indicator is A continuous spectrum of different values ​​of the shortened metric, Graphical elements at positions along the continuous spectrum based on the value of the shortening metric and The ultrasonic system according to claim 1, comprising:

4. The ultrasound system according to claim 1, wherein the processor is configured to store the one or more ultrasound images in the memory in response to a shortened metric that satisfies one or more predetermined criteria.

5. In response that the shortened metric does not meet one or more predetermined criteria, the processor: The steps include controlling the transducer array to acquire multiple additional ultrasound images, The steps include calculating an updated shortening metric corresponding to the further multiple images, A step of modifying the shortened indicator based on the updated shortened metric. The ultrasonic system according to claim 1, configured to perform the following:

6. The aforementioned processor, The steps include comparing the updated shortened metric with one or more predetermined criteria, A step of modifying the user guidance based on a comparison between the updated shortened metric and one or more predetermined criteria. The system according to claim 5, configured to perform the following:

7. The ultrasonic system according to claim 1, wherein the shortening metric comprises a first component relating to the probe position and a second component relating to the probe tilt.

8. In order to compare the shortened metric with one or more predetermined criteria, the processor further: A step of comparing the first component relating to the probe position with a desired probe position, A step of comparing the second component relating to the probe tilt with a desired probe tilt. The ultrasonic system according to claim 7, configured to perform the following:

9. The ultrasonic system according to claim 8, wherein, in response to the first component relating to the probe position not coinciding with the desired probe position, the user guidance includes a message for adjusting the position of the handheld ultrasonic probe.

10. The ultrasound system according to claim 9, wherein the message has the movement of the handheld ultrasound probe to different intercostal spaces in an upward or downward direction.

11. The ultrasonic system according to claim 8, wherein, in response to the first component relating to the probe position coinciding with the desired probe position and the second component relating to the probe tilt not coinciding with the desired probe tilt, the user guidance has a message for adjusting the angle of the handheld ultrasonic probe while maintaining the position of the handheld ultrasonic probe.

12. The ultrasonic system according to claim 8, wherein satisfying one or more of the aforementioned predetermined criteria comprises a first component that matches the desired probe position and a second component that matches the desired probe inclination.

13. The ultrasonic system according to claim 1, wherein the one or more predetermined criteria have a threshold for the shortened metric.

14. To control the transducer array to acquire multiple ultrasound images corresponding to one or more views of the patient's anatomical structure, the processor further: The steps include controlling the transducer array to acquire a first set of ultrasound images while the handheld ultrasound probe is in a first position corresponding to a first view of the patient's anatomical structure, The steps include controlling the transducer array to acquire a second set of ultrasound images while the handheld ultrasound probe is in a second position corresponding to a second view of the patient's anatomical structure, The steps include controlling the transducer array to acquire a third set of ultrasound images while the handheld ultrasound probe is in a third position corresponding to a third view of the patient's anatomical structure, and The ultrasonic system according to claim 1, configured to perform the following:

15. The ultrasound system according to claim 14, wherein the anatomical structure of the patient has a heart, the first view corresponds to a first intercostal space, the second view corresponds to a second intercostal space above the first intercostal space, and the third view corresponds to a third intercostal space below the first intercostal space.

16. In order to calculate the shortened metric corresponding to one or more of the plurality of ultrasound images, the processor further: A step of calculating a first shortening metric for the first plurality of ultrasound images, A step of calculating a second shortening metric for the second set of ultrasound images, The steps include: calculating a third shortening metric for the third set of ultrasound images; The ultrasonic system according to claim 14, configured to perform the following:

17. The aforementioned processor further, A step of comparing the first shortened metric, the second shortened metric, and the third shortened metric, The steps include assigning one of the first, second, or third abbreviation metrics to become a historical abbreviation metric, and The ultrasonic system according to claim 16, configured to perform the following:

18. The ultrasonic system according to claim 17, wherein one or more predetermined criteria have the hysteresis shortening metric.

19. An ultrasound system for guiding the user to obtain optimized ultrasound images, A handheld ultrasound probe with a transducer array, The display and Memory and A processor configured to communicate with the transducer array, the display, and the memory, wherein the processor The steps of controlling the transducer array to acquire a first plurality of ultrasound images corresponding to one or more views of the patient's anatomical structure, A step of calculating a shortening metric corresponding to one or more ultrasound images from the aforementioned plurality of ultrasound images, The steps include outputting a shortened indicator representing the shortened metric to the display, The steps include comparing the shortened metric with one or more predetermined criteria, Steps include: repeatedly outputting guidance to the user to adjust the orientation of the handheld ultrasound probe in response to the shortening metric not meeting one or more predetermined criteria, acquiring a further set of ultrasound images, calculating a further shortening metric corresponding to the further set of ultrasound images, updating the shortening indicator based on the further shortening metric, and comparing the further shortening metric to the one or more predetermined criteria until the updated shortening metric meets one or more predetermined criteria; An ultrasonic system configured to perform an action.

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