System for cross-sectional display for three-dimensional ultrasound imaging

Abstract

An ultrasound system (100) includes a transducer configured to transmit and receive ultrasound signals in a three-dimensional volume. The system (100) includes processing circuitry (114) having a processor (116) and a memory (118) device storing instructions that, when executed, cause the processing circuitry (114) to perform operations including identifying a first anatomical feature based on image data obtained from the three-dimensional volume, wherein the first anatomical feature is along a cross-sectional plane; identifying at least one second anatomical feature based on the first image data; determining algorithmic outputs including at least one of a number of the identified second anatomical features, an area of at least one of the second anatomical features, or the area of at least one of the second anatomical features divided by an area of the first anatomical feature; and displaying an output image based on at least one of the algorithmic outputs or the first image data.

Description

[0001] Statement on Federal Government Funding for Research and Development

[0002] This invention was made with government support under license number 75A50123C00035 granted by the Biomedical Advanced Research and Development Authority (BARDA). The government owns certain rights to the invention. Technical Field

[0003] The embodiments of the subject matter disclosed herein relate to ultrasound imaging, and more specifically to the use of three-dimensional ultrasound imaging for cross-sectional display to detect anatomical features. Background Technology

[0004] During a lung ultrasound scan, the ultrasound probe is positioned by a technician (such as a sonographer) in a primary orientation (e.g., a sagittal orientation toward the patient's head). Identifying multiple B-lines during the scan is challenging, and the technician may move the ultrasound probe from the primary orientation to a secondary orientation to capture a better scan of the B-lines, which may vary depending on time, probe angle, and position. Images acquired during a B-line ultrasound scan may result in missed B-lines or double counting of B-lines, leading to misdiagnosis or missed diagnosis if the image is not optimal. Summary of the Invention

[0005] The embodiment relates to an ultrasound imaging system. The ultrasound imaging system includes a transducer configured to transmit and receive ultrasound signals in a three-dimensional volume. The three-dimensional volume includes a plurality of planes, including cross-sectional planes. The ultrasound imaging system also includes processing circuitry. The processing circuitry includes a processor coupled to a memory device storing instructions that, when executed, cause the processing circuitry to perform operations including: identifying a first anatomical feature based on initial image data obtained from the three-dimensional volume by an ultrasound probe along a first plane intersecting with a surface of the ultrasound probe, the first anatomical feature being determined to be along the cross-sectional plane; receiving the first image data parallel to the cross-sectional plane from the three-dimensional volume; identifying at least one second anatomical feature based on at least one of the first image data; and determining an algorithm output based on the at least one first image data. The algorithm output includes at least one of the following: the number of identified second anatomical features, the area of ​​the second anatomical feature, or the area of ​​the second anatomical feature divided by the area of ​​the first anatomical feature. These operations include displaying at least one of the algorithm output or an output image based on the first image data on a display screen of the ultrasound imaging system.

[0006] Another embodiment relates to an ultrasound imaging system. The ultrasound imaging system includes a transducer configured to transmit and receive ultrasound signals in a three-dimensional volume. The three-dimensional volume includes a plurality of planes, including cross-sectional planes. The ultrasound imaging system includes image processing circuitry configured to identify a first anatomical feature based on initial image data and to receive the first image data from the three-dimensional volume parallel to the cross-sectional planes. The ultrasound imaging system includes control circuitry configured to determine an algorithm output based on the first image data. The algorithm output includes at least one of the following: the number of identified second anatomical features, the area of ​​at least one of the second anatomical features, or the area of ​​at least one of the second anatomical features divided by the area of ​​the first anatomical feature. The ultrasound imaging system is configured to display an output image based on at least one of the algorithm output or the first image data on a display screen of the ultrasound imaging system.

[0007] Another embodiment relates to a method. The method includes identifying a first anatomical feature by processing circuitry based on initial image data obtained from a three-dimensional volume by an ultrasound probe along a first plane intersecting the surface of the ultrasound probe. The first anatomical feature is determined to be along a cross-sectional plane. The method includes receiving the first image data from the three-dimensional volume by processing circuitry parallel to the cross-sectional plane. The method includes identifying at least one second anatomical feature by processing circuitry based on the first image data. The method includes determining algorithmic outputs by processing circuitry based on the first image data, these algorithmic outputs including at least one of the following: the number of identified second anatomical features, the area of ​​at least one of the second anatomical features, or the area of ​​at least one of the second anatomical features divided by the area of ​​the first anatomical feature. The method includes displaying an output image based on at least one of the algorithmic outputs or the first image data on a display screen of an ultrasound imaging system.

[0008] This overview is merely illustrative and is not intended to be limiting in any way. Other aspects, inventive features, and advantages of the apparatus or process described herein will become apparent from the detailed description set forth herein in conjunction with the accompanying drawings, wherein like reference numerals refer to like elements. Attached Figure Description

[0009] Figure 1 This is a block diagram of an ultrasound imaging system according to an example implementation.

[0010] Figure 2 It is based on the example implementation plan. Figure 1 A schematic diagram of the desired orientation of the ultrasound probe used in an ultrasound imaging system.

[0011] Figure 3 It is based on the example implementation plan. Figure 1A diagram illustrating the configuration of the ultrasound probes used in an ultrasound imaging system.

[0012] Figure 4 This is an example of the use of the example implementation scheme. Figure 1 A flowchart of a method for performing ultrasound imaging using an ultrasound imaging system.

[0013] Figure 5A This is a diagram of an ultrasound image acquired using an ultrasound probe according to an example implementation.

[0014] Figure 5B This is an illustration of another ultrasound image obtained using an ultrasound probe according to an example implementation.

[0015] Figure 6A It is based on the example implementation scheme after processing. Figure 5A Illustration of ultrasound images.

[0016] Figure 6B It is based on the example implementation scheme after processing. Figure 5B Illustration of ultrasound images.

[0017] Figure 7A This is an illustration of another ultrasound image obtained from a three-dimensional volume using an ultrasound probe according to an example implementation.

[0018] Figure 7B It is based on the example implementation plan from Figure 7A Illustration of another ultrasound image obtained from the first cross-sectional plane of the ultrasound image.

[0019] Figure 7C It is based on the example implementation plan from Figure 7A Illustration of another ultrasound image obtained from the second cross-sectional plane of the ultrasound image.

[0020] Figure 7D It is based on the example implementation plan from Figure 7A Illustration of another ultrasound image obtained from the third cross-sectional plane of the ultrasound image.

[0021] Figure 8A This is an illustration of another ultrasound image obtained from a three-dimensional volume using an ultrasound probe according to an example implementation.

[0022] Figure 8B It is based on the example implementation plan from Figure 8A Illustration of another ultrasound image obtained from the first cross-sectional plane of the ultrasound image.

[0023] Figure 8C It is based on the example implementation plan from Figure 8A Illustration of another ultrasound image obtained from the second cross-sectional plane of the ultrasound image.

[0024] Figure 8D It is based on the example implementation plan from Figure 8A Illustration of another ultrasound image obtained from the third cross-sectional plane of the ultrasound image.

[0025] Figure 9A This is an illustration of another ultrasound image obtained from a three-dimensional volume using an ultrasound probe according to an example implementation.

[0026] Figure 9B It is based on the example implementation plan from Figure 9A Illustration of another ultrasound image obtained from the first cross-sectional plane of the ultrasound image.

[0027] Figure 9C It is based on the example implementation plan from Figure 9A Illustration of another ultrasound image obtained from the second cross-sectional plane of the ultrasound image.

[0028] Figure 9D It is based on the example implementation plan from Figure 9A Illustration of another ultrasound image obtained from the third cross-sectional plane of the ultrasound image.

[0029] Figure 10A This is an illustration of another ultrasound image obtained from a three-dimensional volume according to an example implementation.

[0030] Figure 10B It is based on the example implementation plan from Figure 10A Illustration of another ultrasound image obtained from the first cross-sectional plane of the ultrasound image.

[0031] Figure 10C It is based on the example implementation plan. Figure 10A Another illustration of the ultrasound image.

[0032] Figure 10D It is based on the example implementation plan from Figure 10C Illustration of another ultrasound image obtained from the first cross-sectional plane of the ultrasound image.

[0033] Figure 10E It is based on the example implementation plan. Figure 10A Another illustration of the ultrasound image.

[0034] Figure 10F It is based on the example implementation plan from Figure 10E Illustration of another ultrasound image obtained from the first cross-sectional plane of the ultrasound image.

[0035] Figure 11A This is an illustration of another ultrasound image obtained from a three-dimensional volume according to an example implementation.

[0036] Figure 11BThis is an illustration of another ultrasound image obtained from a three-dimensional volume according to an example implementation.

[0037] Figure 11C It is based on the example implementation plan from Figure 11B An illustration of another ultrasound image obtained from a cross-sectional plane of the ultrasound image.

[0038] Figure 12A It is a diagram of a cross-sectional plane of an acquired ultrasound image showing anatomical features according to an example embodiment.

[0039] Figure 12B It includes, according to the example implementation plan Figure 12A An illustration of the averaged cross-sectional plane of the ultrasound image. Detailed Implementation

[0040] Referring generally to the accompanying drawings, systems and methods for detecting anatomical features using three-dimensional ultrasound imaging for cross-sectional plane display are disclosed. The systems disclosed herein are used to acquire multiple image data along a cross-sectional plane. The systems disclosed herein use multiple images to identify at least one anatomical feature. These systems and methods use multiple images such that a user interface can display an image based on first image data or second image data obtained by determining a plane perpendicular to the cross-sectional plane in which the anatomical feature is displayed.

[0041] During lung ultrasound scans, the ultrasound probe is routinely positioned in a sagittal orientation (e.g., pointing towards the patient's head). However, identifying the number and volume of B-lines present between a pair of ribs is often challenging, and images acquired in this orientation may not be of sufficient quality to adequately identify all B-lines or make a correct diagnosis. Visualization of B-lines is important for estimating lung condition and identifying pathology surrounding the pleura. For example, the presence of more than three B-lines may indicate a medical condition (e.g., pulmonary edema). Poor image quality can be due to artifacts, inadequate visualization of the pleura, and probe orientation. Therefore, it is desirable to orient the probe so that the resulting image data from the ultrasound beam from the probe captures a consistent number of B-lines. In this orientation, the pleura is in optimal view, and the B-lines are clearly visible. Manually orienting the ultrasound probe without assistance to capture a consistent number of B-lines in the resulting image data is time-consuming and depends on the skill and expertise of the operator (e.g., sonographer, technician, clinician, etc.), leading to inconsistencies due to operator inefficiency and inconsistent acquisition of consistent, high-quality ultrasound images.

[0042] Consistency in the detection of B-line counts is crucial for accurate diagnosis and treatment of patients. For example, when treating patients for medical conditions related to B-line count (e.g., pulmonary edema), it is important to visualize the B-lines to ensure their reduction. However, if a consistent number of B-lines cannot be captured due to various reasons such as operator skill or expertise, it may be difficult to properly ensure accurate detection of the B-lines. For instance, in such cases, some B-lines may be missed in the captured images, or B-lines may be counted twice.

[0043] The system and method described in this paper provide a technical solution for existing ultrasound imaging systems employing three-dimensional ultrasound imaging. The use of three-dimensional ultrasound imaging allows for the acquisition of cross-sectional planes (e.g., planes substantially parallel to the patient's skin that do not intersect the probe's surface / coverage area) to visualize B-lines. The use of three-dimensional imaging allows for the acquisition of cross-sectional planes. This differs from two-dimensional imaging, in which only slices extending into the body can be obtained. Defining the Cartesian coordinate system as the XYZ coordinate system, where Z is the axis extending into the patient's body, two-dimensional ultrasound imaging obtains scans from either the XZ plane or the YZ plane. In other words, scans are obtained from planes extending into the body, as referenced... Figure 5A and Figure 5B As shown, etc. In the case of three-dimensional imaging, images from the XY plane (i.e., the cross-sectional plane) can be obtained. As described in this article, the XY plane can provide more information about anatomical features, such as the B-line.

[0044] Using a cross-sectional plane, the number and area of ​​B-lines can be determined. Furthermore, as the B-lines move with the pleura, the system can perform the determination of a cross-sectional plane using the pleura. The cross-sectional plane can then be used to determine a plane perpendicular to the cross-sectional plane in which the B-lines are manifested (e.g., a plane including depth). The system and method described herein can be used to generate visualizations of multiple anatomical features, such as B-lines and Z-lines, etc.

[0045] Therefore, the systems and methods described herein reduce reliance on operator expertise and skills by automatically determining the plane in which anatomical features are displayed through the use of a cross-sectional plane. Additionally, the system can use a second anatomical feature to determine the cross-sectional plane, thereby reducing the need for the operator to manually manipulate the probe to reach that view. Furthermore, the systems and methods described herein help operators detect pathology and complete ultrasound examinations in a shorter amount of time (e.g., because the operator does not need to manually adjust the probe to achieve the desired orientation in which anatomical features are displayed).

[0046] The specific implementations described herein address technical problems by providing enhanced data integration and analysis capabilities, offering specific technical solutions for simplifying and refining image generation during lung ultrasound. The systems described herein are implemented to improve how data from various sources provides information related to the optimal orientation for capturing images during ultrasound scans. By evaluating specific anatomical features and automatically determining cross-sectional images and anatomical features based on that evaluation, these systems provide real-time guidance for image generation during ultrasound scans. Therefore, the methods disclosed herein offer specific technical improvements to various technical problems, including those described herein. The systems described herein also reduce processing power by simultaneously performing various processing operations to generate images during ultrasound scans, rather than performing multiple processing operations individually and consuming unnecessary processing power.

[0047] Before turning to the accompanying drawings, which detail certain exemplary embodiments, it should be understood that this disclosure is not limited to the details or methods set forth in the specification or illustrated in the drawings. It should also be understood that the terminology used herein is for descriptive purposes only and should not be construed as limiting.

[0048] refer to Figure 1 A schematic diagram of an ultrasound imaging system 100 is shown. The ultrasound imaging system 100 can be used in medical settings (e.g., hospitals, clinics, etc.), for example by ultrasound physicians, technicians, or other clinicians certified to collect ultrasound data from patients.

[0049] An example of a procedure performed using the ultrasound imaging system 100 could be lung ultrasound. Lung ultrasound can be performed to detect various lung pathologies, such as pneumonia, pulmonary edema, pleural effusion, pneumothorax (e.g., collapsed lung), pulmonary embolism, lung cancer, etc. These pathologies are detected by collecting and processing ultrasound data (e.g., using the ultrasound imaging system 100 described herein). During a lung ultrasound, the sonographer collects ultrasound data by navigating a probe (e.g., probe 106, described below) above the patient's chest until a sufficient amount of ultrasound images has been collected. In the case of three-dimensional imaging, the three-dimensional volume of the ultrasound image can be obtained using the ultrasound imaging system 100. The sonographer can collect ultrasound images with the ultrasound probe in sagittal, transverse, and coronal orientations. Referring to the Cartesian coordinate system described herein, transverse orientation refers to the XZ plane or the YZ plane. Sagittal orientation refers to the other of the XZ plane or the YZ plane. Coronal orientation refers to the XY plane. As described herein, coronal orientation can be particularly advantageous when a lung pathology is detected. The collected images are stored in a central storage device (e.g., memory 118) and analyzed by the sonographer. The sonographer generates a set of measurements based on the images (e.g., 50 to 100 records), and these images and measurements are reviewed collaboratively by a medical expert (such as a pulmonologist). The pulmonologist provides any clinical findings / conclusions in the report submitted to the patient's medical record.

[0050] like Figure 1 As shown, the ultrasound imaging system 100 includes a transmit beamformer 102, a transmitter 104, a probe 106, a receiver 110, and a receive beamformer 112. The ultrasound imaging system 100 may also include a matching layer and a damping block. The matching layer improves ultrasound image quality by having acoustic impedance between the tissue to be imaged and the transducer material of the ultrasound imaging system 100. The damping block is configured to absorb ultrasound energy to improve ultrasound image quality.

[0051] Transmit beamformer 102 can be a hardware beamformer or a software beamformer. In embodiments where transmit beamformer 102 is a hardware beamformer, transmit beamformer 102 may include one or more of a graphics processing unit (GPU), a microprocessor, a central processing unit (CPU), a digital signal processor (DSP), or any other type of processor capable of performing logical operations. Transmit beamformer 102 may be configured to perform conventional beamforming techniques as well as techniques such as backtrack transmit beamforming (RTB). Alternatively, in embodiments where transmit beamformer 102 is a software beamformer, a processor (e.g., processor 116 as described below) may be configured to perform some or all of the functions associated with transmit beamformer 102.

[0052] Probe 106 may be a linear array probe, a curved array probe, a sector probe, a row-column array probe, or any other type of probe configured to acquire ultrasound data (e.g., B-mode data, color Doppler flow data, etc.). More specifically, probe 106 may include a matrix transducer array (e.g., matrix configuration 300, as referred to below) configured to acquire three-dimensional (3D) ultrasound data. Figure 3 Any type of probe (as described). In some embodiments, probe 106 may include a positioning sensor configured to detect the positioning of probe 106 relative to one or more reference locations. That is, when identifying the anatomical structure being imaged, the positioning sensor may continuously track the movement (e.g., rotation, translation, orientation, etc.) of probe 106 relative to its position. For example, the anatomical structure being imaged may be identified as the pleura at a first location of probe 106 (e.g., represented by pleura 502, as described below). The positioning sensor may then track the movement of probe 106 relative to the pleura to identify successive positions of probe 106. In some embodiments, the positioning sensor may transmit position data to be stored within the ultrasound imaging system 100 (e.g., in memory 118).

[0053] The probe 106 may include a transducer configured to transmit and receive ultrasonic signals. In some embodiments, such as Figure 1 As shown, probe 106 includes signal element 108. Signal element 108 can be arranged in a transducer array, and in some embodiments, it can be arranged in a 2D array (e.g., as shown in Figure 108). Figure 3 The matrix configuration 300 shown is illustrated. As described herein, the 2D array of signal elements 108 allows for 3D ultrasound imaging (e.g., such that the transducer is configured to transmit and receive 3D ultrasound signals). Transmit beamformer 102 and transmitter 104 drive signal elements 108 to transmit pulsed ultrasound signals into the body of a subject (e.g., a patient). For example, during lung ultrasound, an ultrasound physician or other clinician may navigate probe 106 above the patient's chest, such that signal elements 108 in probe 106 transmit pulsed ultrasound signals into the patient's pleural cavity. The pulsed ultrasound signals are then backscattered from anatomical structures within the body, such as blood cells or muscle tissue, to produce an echo returning to signal elements 108. However, during lung ultrasound, there is an acoustic impedance mismatch between the lungs (e.g., because the lungs are filled with air) and the transducer material, meaning that the ultrasound signal cannot be backscattered from the air-filled lungs. Instead, the ultrasound signal is backscattered from the pleura (e.g., resulting in the generation of pleura 502 in the ultrasound image of the lungs, as described below with reference to Figure 5).

[0054] Furthermore, in an ultrasound imaging system with an electronically steerable beam, the transmit beamformer 102 can be adjusted to shift the angle of the ultrasound beam, thereby shifting the transmitted ultrasound signal. For example, the transmit beamformer 102 can adjust the timing and phase shift of the ultrasound signal transmitted to the subject's body to change the angle of the ultrasound signal. In various embodiments, adjustment can be made without the operator having to move the transducer.

[0055] Receiver 110 receives the echo from probe 106 and converts the echo into an electrical signal. The electrical signal then passes through receiver beamformer 112, which generates ultrasound data based on the electrical signal. As described above with reference to transmit beamformer 102, receiver beamformer 112 can be a hardware beamformer or a software beamformer. In embodiments where receiver beamformer 112 is a hardware beamformer, receiver beamformer 112 may include one or more of a GPU, microprocessor, CPU, DSP, or any other type of processor capable of performing logical operations. Receiver beamformer 112 may be configured to perform conventional beamforming techniques as well as techniques such as backtrack transmit beamforming (RTB). Alternatively, in embodiments where receiver beamformer 112 is a software beamformer, a processor (e.g., processor 116 described below) may be configured to perform some or all of the functions associated with receiver beamformer 112.

[0056] Although the transmitting beamformer 102, transmitter 104, receiver 110 and receiving beamformer 112 are in Figure 1 While components of the ultrasound imaging system 100, which are shown as different from probe 106, are included, it should be understood that in some embodiments, probe 106 may include electronic circuitry configured to perform the functions of each of the transmit beamformer 102, transmitter 104, receiver 110, and / or receive beamformer 112. That is, all or part of the transmit beamformer 102, transmitter 104, receiver 110, and / or receive beamformer 112 may be located within probe 106.

[0057] Still referencing Figure 1 An ultrasound imaging system 100 is shown as including processing circuitry 114. As shown, processing circuitry 114 may include at least one processor 116, memory 118, image processing circuitry 120, artificial intelligence (AI) circuitry 122, and control circuitry 124. In this way, processing circuitry 114 may be configured or constructed to execute or implement the instructions, commands, and / or control processes described herein with respect to processor 116, memory 118, image processing circuitry 120, AI circuitry 122, and control circuitry 124. Although in Figure 1While shown separately from probe 106, it should be understood that processing circuitry 114 may be part of probe 106. For example, processing circuitry 114 may be housed in the handheld housing of probe 106 (e.g., in the case where probe 106 is a wireless probe).

[0058] Processor 116 may include a CPU, GPU, microprocessor, DSP, general-purpose single-chip or multi-chip processor, field-programmable gate array (FPGA), or any other type of processor capable of performing logical operations. A general-purpose processor may be a microprocessor or any conventional processor or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors incorporating a DSP core, or any other such configuration. In some embodiments, processor 116 may be shared by multiple circuits (e.g., the circuitry of processor 116 may include or otherwise share the same processor, and in some example embodiments, the processor may execute instructions stored or otherwise accessed via different regions of memory 118). Alternatively or otherwise, processor 116 may be configured to perform or otherwise perform certain operations independently of one or more coprocessors. In some embodiments, two or more processors may be bus-coupled to enable independent, parallel, pipelined, or multi-threaded instruction execution. All such variations are intended to fall within the scope of this disclosure.

[0059] Processor 116 can be configured to control transmit beamformer 102, transmitter 104, receiver 110, and receive beamformer 112. Processor 116 can also communicate electronically with probe 106. For the purposes of this disclosure, the term "electronic communication" can be defined to include both wired and wireless communication.

[0060] In some implementations, processor 116 may be configured to control probe 106 during data acquisition. That is, processor 116 can control data acquisition by controlling which signal element in signal elements 108 is active and by controlling the shape of the beam emitted from probe 106. For example, using... Figure 3 The matrix configuration 300 of the signal elements 108 shown allows the processor 116 to be configured to control the transmission direction of the probe 106, enabling data acquisition from the sagittal plane (e.g., azimuth plane 210) and / or lateral plane (e.g., elevation plane 215) of the probe 106. According to other embodiments, the transmission direction of the probe 106 can be controlled by another component of the ultrasound imaging system 100 (e.g., control circuitry 124).

[0061] Alternatively or otherwise, processor 116 may include a composite demodulator configured to demodulate radio frequency (RF) data acquired by probe 106 and generate raw data. According to other embodiments, demodulation of the RF data may be performed by another component of the ultrasound imaging system 100. Processor 116 may perform the processing operations described herein according to a variety of selectable ultrasound modes.

[0062] Depending on the operating mode of the ultrasound imaging system 100, the processor 116 can process ultrasound data acquired by the probe 106 to generate image data. For example, operating modes may include B-mode, color Doppler mode, M-mode, color M-mode, spectral Doppler, elastography, TVI, strain, strain rate, etc. Various modes among these operating modes can be configured to, for example, convert ultrasound data from beam space coordinates (e.g., received from the receiving beamformer 112) to display space coordinates (e.g., such that the ultrasound data can be displayed as image data). In some embodiments, the operating modes may allow video processing by the processor 116, enabling the real-time display of a series of images (e.g., processed ultrasound data) during a scan session / procedure performed on a patient. The operator of the ultrasound imaging system 100 (e.g., an ultrasound physician) can switch between various modes to acquire various ultrasound data and perform a complete scan of the anatomical region of interest. For example, the operator can switch between modes using a user interface 130 (e.g., using physical controls, interface input representing physical controls, etc.).

[0063] When receiver 110 receives an echo signal from probe 106, processor 116 performs processing operations in real time. For the purposes of this disclosure, the term "real time" is defined as including procedures performed without any intentional delay. As an illustrative and non-limiting example, in some cases, ultrasound imaging system 100 may acquire images at a real-time volumetric rate of 7 to 20 volumes per second. However, it should be understood that the real-time volumetric rate may depend on the length of time taken to acquire each volume of data used for display. Thus, ultrasound imaging system 100 may be configured to acquire 2D data of an anatomical region at a faster rate than 3D data of the same anatomical region, since acquiring the volume of 3D data takes longer than acquiring the same volume of 2D data. Similarly, when ultrasound imaging system 100 acquires a relatively large amount of data, the real-time volumetric rate may be slower than a smaller amount of data. For example, during an abdominal scan, the real-time volumetric rate may be slower if the patient is an adult than if the patient is an infant, because the amount of data for an adult is larger than that for an infant (e.g., because the abdomen of an adult is larger than that of an infant). Therefore, some specific embodiments of the ultrasound imaging system 100 may have a real-time volume rate faster than 20 volumes / second, while other specific embodiments of the ultrasound imaging system 100 may have a real-time volume rate slower than 7 volumes / second.

[0064] In some embodiments, the ultrasound imaging system 100 may include multiple processors configured to perform the processing operations / functionality described in reference processor 116. For example, in such embodiments, a first processor among the multiple processors may be configured to demodulate and decimate RF signals, while a second processor among the multiple processors may be configured to further process the RF data before displaying an image representing the data. It should be understood that other embodiments may use different processor arrangements.

[0065] Processor 116 can also communicate electronically with display device 132, enabling processor 116 to process ultrasound data obtained by probe 106 and generate images for display on display device 132 (e.g., first ultrasound image 500a, second ultrasound image 500b, first ultrasound image 700a and second ultrasound image 700b, as referred to below). Figure 5A , Figure 5B , Figure 7A and Figure 7B (As described).

[0066] like Figure 1 As shown, the processing circuitry 114 also includes a memory 118. The memory 118 may be configured to, for example, store a processed amount of data acquired by the ultrasound imaging system 100 (e.g., ultrasound data collected by probe 106). For example, the memory 118 may be a hospital picture archiving and communication system (PACS). The memory 118 (e.g., memory, memory cell, storage device, etc.) may include one or more devices (e.g., RAM, ROM, flash memory, hard disk storage, etc.) for storing data and / or computer code to perform or facilitate the processes, layers, and modules described in this application. The memory 118 may be or include tangible non-transient volatile memory or non-volatile memory. The memory 118 may also include database components, object code components, script components, or any other type of information structure for supporting the activities and information structures described in this application.

[0067] In various embodiments, the memory 118 may have different capacities (e.g., storage space) in the implementation of the ultrasound imaging system 100. For example, the memory 118 may be configured to store at least 60 minutes of ultrasound data value. Ultrasound data may be stored in the memory 118 such that the ultrasound data can be retrieved according to the order / time of data acquisition. That is, the ultrasound data may be stored together with a timestamp indicating the time when the ultrasound data was collected, and can be retrieved starting from the earliest time the ultrasound data was collected.

[0068] The processing circuit 114 also includes an image processing circuit 120, an AI circuit 122, and a control circuit 124. Each of the image processing circuit 120, the AI ​​circuit 122, and the control circuit 124 is configured to facilitate the determination of a second transmission direction using the probe 106 during an ultrasound scan.

[0069] Image processing circuitry 120 is configured to analyze (e.g., acquired using probe 106, stored in memory 118, etc.) ultrasound image data and identify anatomical structures, scanning planes, transmission directions, pathology, and / or other features depicted / included within the image data. In some cases, image processing circuitry 120 may include multiple deep learning-based models configured to analyze the image data. Alternatively or additionally, image processing circuitry 120 may use multiple deep learning-based models included in AI circuitry 122 to analyze the image data, as described herein.

[0070] In some embodiments, image processing circuitry 120 may be configured to identify anatomical structures, features, regions, etc., captured by image data. For example, during lung ultrasound, image processing circuitry 120 may be configured to identify B-lines, Z-lines, pleura, ribs, rib shadows, and / or other lung structures / features / regions depicted in the image data. Image processing circuitry 120 may be configured to use one or more algorithms (e.g., image processing algorithms such as edge detection, machine learning models, deep neural networks, etc.) to identify anatomical structures. In some embodiments, image processing circuitry 120 may be configured to apply one or more algorithms used by AI circuitry 122 and / or retrieved from external database 128. For example, see the following references. Figure 7A and Figure 7B As described, the image processing circuitry 120 can use one or more algorithms to evaluate the pleura 502 captured by the image data (e.g., using a segmentation model to segment the pleura 502, using linear regression to estimate the pleural line). In this way, the image processing circuitry 120 can be configured to estimate the position and / or plane of the pleura 502, and can adjust the transmission direction of the probe 106 based on the estimated direction relative to the ultrasound beam, such that the pleura can be scanned at a desired angle (e.g., at an angle perpendicular to the ultrasound beam), as described herein. The ultrasound beam refers to the transmitted ultrasound signal. In another example, see reference below. Figures 8B to 8D As described above, the image processing circuit 120 can use one or more algorithms to evaluate the B-line 702 captured by the image data. In this way, the image processing circuit can be configured to determine the B-line 702 using AI models and other models.

[0071] In some embodiments, image processing circuitry 120 may identify anatomical features, such as bones, blood vessels, organs, etc., based on the shape, relative proximity, apparent depth, orientation, etc., of the features described in the image data. Then, based on the identified anatomical features, image processing circuitry 120 may be configured to determine the anatomical structures depicted in the image data. For example, because the lungs cannot be directly imaged (e.g., due to the lungs being filled with air and the acoustic impedance mismatch between the air and the transducer of probe 106), image processing circuitry 120 may determine that the lungs are being imaged based on the movement and / or orientation of the lungs relative to surrounding structures (such as the pleura) via a deep learning classification model trained to recognize the movement and / or orientation of the lungs and other anatomical structures. In some embodiments, image processing circuitry 120 may detect movement of structures in image data by comparing the position, shape, size, etc., of identified structures across a set of images (e.g., movie playback).

[0072] According to some embodiments, the image processing circuit 120 may be configured to identify the view or scanning plane from which ultrasound data is acquired. For example, the image processing circuit 120 may be configured to identify whether the image data depicts a sagittal view (e.g., along azimuth plane 210, XZ plane, YZ plane), a lateral view (e.g., along elevation plane 215, XZ plane, YZ plane), and / or a coronal view (e.g., parallel to the patient's body / skin, XY plane). Additionally, the image processing circuit 120 may be configured to identify the transmission direction of the ultrasound data acquired. For example, the image processing circuit 120 may be configured to determine the transmission direction of the probe 106 relative to the identified anatomical feature using position data of the probe 106.

[0073] Image processing circuitry 120 can also be configured to determine the presence of pathology (e.g., lesion, disease, abnormality, etc.) in image data. In some embodiments, image processing circuitry 120 may use a deep learning classification model trained to identify various pathologies in anatomical structures represented by image data to designate the presence of a pathology. Continuing with the example of lung ultrasound, image processing circuitry 120 may use a deep learning classification model trained to identify lung pathologies to determine the presence of a pathology in image data from lung ultrasound. For example, image processing circuitry 120 may identify pleural effusion (e.g., fluid accumulation) in the pleura as a lung pathology present in the image data. In some embodiments, AI circuitry 122 may be configured to perform any of the functions of image processing circuitry 120 described herein using multiple deep learning-based models configured to analyze image data.

[0074] Based on the analysis of image data, processing circuitry 114 (e.g., AI circuitry 122) can be configured to determine adjustments to probe 106. In some embodiments, adjustments to probe 106 may include changes in the angle of probe 106. For example, during lung ultrasound, image processing circuitry 120 may (e.g., using multiple images obtained from sagittal, coronal, and lateral planes) determine an estimated angle of the pleura relative to the ultrasound beam, and processing circuitry 114 may determine the transmission direction to which the angle of probe 106 is adjusted (e.g., by adjusting the transmission beamformer 102). As another example, control circuitry 124 may determine the plane in which the anatomical feature is located, as described below in step 405 of reference method 400. As another example, control circuitry 124 may determine a plane perpendicular to the coronal plane in which the second anatomical feature is manifested, as described below in step 420 of reference method 400. In this way, since the image obtained from the cross-sectional plane by probe 106 is analyzed to determine a certain number (e.g., a maximum number) of second anatomical features appearing in the plane (e.g., from the transverse and / or sagittal views), the captured image of the second anatomical feature (e.g., line B 702) can be improved (e.g., resulting in improved detection, such as...). Figure 10C and Figure 10D (As shown). The cross-sectional plane can be derived from the coronal view, or it can be any plane that does not intersect the surface or coverage area of ​​the probe 106. For example, the cross-sectional plane can be derived from a plane parallel to the coronal view plus a deviation.

[0075] In some implementations, processing circuitry 114 (e.g., AI circuitry 122) may be configured to automatically generate control signals prompting adjustments to probe 106 based on analysis of image data performed by image processing circuitry 120. These control signals may be received by control circuitry 124, which may be configured to automatically align the transmission direction of probe 106 such that probe 106 is configured to acquire image data according to the transmission direction specified by the control signals. For example, the control signals may prompt control circuitry 124 to adjust transmit beamformer 102, thereby adjusting the ultrasonic beam to shift the transmission direction of probe 106.

[0076] The ultrasound imaging system 100 may also include an external database 128 and a user interface 130. The external database 128 refers to a database from which the processing circuitry 114 (e.g., image processing circuitry 120, AI circuitry 122) retrieves information for performing three-dimensional imaging during lung ultrasound. For example, the external database 128 may be a medical information database. The medical information database may store clinical guidelines, standard practices, medical literature, medical textbooks, published studies, previous case studies, etc. Depending on the specific implementation of the ultrasound imaging system 100 and / or the procedures performed therefrom, the processing circuitry 114 may retrieve clinical guidelines, standard practices, medical literature, medical textbooks, published studies, and previous case studies relevant to the specific implementation and / or procedures. For example, if the ultrasound imaging system 100 is used to perform lung ultrasound in a hospital setting, the processing circuitry 114 may retrieve clinical guidelines and standard practices relevant to the hospital setting and lung ultrasound. Continuing this example, the processing circuitry 114 may also retrieve information from medical literature, medical textbooks, published studies, and previous case studies relevant to lung anatomy and lung ultrasound. In some cases, such as reference Figure 6A , Figure 6B and Figures 8A to 8D As described above, the information retrieved from external database 128 may include one or more algorithms for segmenting anatomical features and determining planes representing anatomical features (e.g., segmenting the pleura 502 and using algorithms to determine the planes).

[0077] Ultrasound examiners or other clinicians can use the user interface 130 to control the operation of the ultrasound imaging system 100. For example, ultrasound examiners can use the user interface 130 to control patient data input, change scan or display parameters, and / or select various other modes, operations, parameters, etc., of the ultrasound imaging system 100. In some embodiments, the user interface 130 may include off-the-shelf consumer electronics devices, such as smartphones, tablets, laptops, etc. For the purposes of this disclosure, the term "off-the-shelf consumer electronics device" is defined as an electronic device designed and developed for general consumer use rather than specifically designed for use in a medical setting. Alternatively, in other embodiments, the user interface 130 may be an electronic device designed and developed for use in a medical setting.

[0078] According to some embodiments, the user interface 130 may be physically separated from the rest of the ultrasound imaging system 100 (e.g., transmit beamformer 102, transmitter 104, probe 106, receiver 110, receive beamformer 112, processing circuitry 114, and / or external database 128). The user interface 130 may communicate with the processor 116 via wireless protocols such as Wi-Fi, Bluetooth, wireless local area network (WLAN), near-field communication, etc. According to some embodiments, the user interface 130 may communicate with the processor 116 via an application programming interface (API).

[0079] In some implementations, the user interface 130 may include one or more physical controls, such as buttons, sliders, knobs, mice, keyboards, trackballs, hard keys linked to specific actions, and soft keys configurable to control different functions. Figure 1 As shown, the user interface 130 may also include a display device 132. In some embodiments, the display device 132 may be configured to display a graphical user interface (GUI) based on instructions from the memory 118. The GUI may include user interface icons representing commands and instructions related to the operation of the ultrasound imaging system 100. The user interface icons of the GUI may be configured such that a user (e.g., an ultrasound physician, clinician, etc.) can select a specific user interface icon to activate a specific function controlled by the GUI. For example, various user interface icons may be used to represent windows, menus, buttons, cursors, scroll bars, etc. That is, the physical controls of the user interface 130 may be included as separate hardware components, as user interface icons displayed on the display device 132, or as a combination of hardware components and user interface icons.

[0080] In some embodiments, display device 132 may include a touch-sensitive display device or a touchscreen. According to such embodiments, the touchscreen may be configured to interact with a GUI displayed by display device 132, allowing a user (e.g., an ultrasound physician) to interact with the GUI via the touchscreen. The touchscreen may be a single-point touchscreen configured to detect a single contact point at a time, or a multi-point touchscreen configured to detect multiple contact points at a time. For embodiments where the touchscreen is a multi-point touchscreen, the touchscreen may be configured to detect multi-point gestures involving contact from two or more fingers of the user at a time. The touchscreen may be a resistive touchscreen, a capacitive touchscreen, or any other type of touchscreen configured to receive input from a stylus or one or more fingers of the user. According to some embodiments, the touchscreen may be an optical touchscreen that uses techniques such as infrared light or light of other frequencies to detect one or more contact points initiated by the user. In some embodiments, the touchscreen may be incorporated as part of display device 132 or may be separate from display device 132. User interface 130 may also include a proximity sensor configured to detect objects and / or gestures within a predetermined distance (e.g., five feet, six inches, ten centimeters, etc.) of the proximity sensor. In various implementations, the proximity sensor may be located on the display device 132 or as part of a touchscreen separate from the display device 132.

[0081] refer to Figure 2 The orientation 200 of probe 106 is shown. Figure 2 Orientation 200, as illustrated, refers to the desired orientation of probe 106 during lung ultrasound, as described herein. More specifically, orientation 200 refers to the orientation of probe 106 relative to a field of view 202 including pleura 204. As shown, orientation 200 can be defined by three orthogonal planes including azimuth plane 210, elevation plane 215, and coronal plane. Azimuth plane 210 captures image data from a sagittal view of ultrasound probe 106, and elevation plane 215 captures image data from a lateral view of ultrasound probe 106. The coronal plane captures image data from a view parallel to probe 106. For example, in a Cartesian coordinate system where the Z-axis is defined as extending through the body, the coronal plane can be defined as the XY plane. According to orientation 200, azimuth plane 210 and elevation plane 215 are shown as perpendicular to pleura 204, while the coronal plane is parallel to pleura 204. During lung ultrasound, it may be desirable to orient the probe 106 such that the ultrasound beams in the azimuth plane 210 and elevation plane 215 are perpendicular to the pleura 204, thereby producing images of optimal quality. In other words, for three-dimensional ultrasound imaging as described herein, it may be desirable to orient the probe 106 such that the coronal plane is parallel to the pleura 204.

[0082] When probe 106 is positioned on the patient, its position is naturally perpendicular to the patient's body. However, the pleura 204 may not be parallel to the body, causing the pleura 204 to be non-perpendicular to probe 106, such as... Figure 2 As indicated, it can be shifted by an angle (e.g., as shown). Figure 5A and Figure 5B (As indicated). When the acoustic transmission is parallel to probe 106, the captured image may have degraded image quality depending on the angle of the pleura 502, as described herein. Furthermore, the location of the patient's pleura can be unique for the individual anatomy of the patient, meaning that the location of the pleura in one patient may differ from that in another patient, and so on. This can be corrected by reorienting the acoustic transmission of probe 106, thus adjusting the transmitted ultrasound signal to be perpendicular to the pleura. This can also be corrected by the user reorienting probe 106. Instead of relying on the expertise / skill of the sonographer to orient probe 106 so that the pleura 502 is perpendicular to the ultrasound beam in the azimuth plane 210 and elevation plane 215, the transmission direction of probe 106 can be automatically adjusted, as described herein, such that probe 106 is configured to capture ultrasound images from a direction substantially perpendicular to the ultrasound beam from the pleura.

[0083] When capturing data of a second anatomical feature that has moved relative to a first anatomical feature (e.g., a B-line that has moved relative to the pleura, as described herein), it may be desirable to determine the plane in which the first anatomical feature lies. Continuing with this example, for the pleura, the pleura may be parallel to the coronal plane, but in many cases, the pleura may be displaced such that it is not parallel (e.g., the pleura may be substantially parallel or not parallel to the coronal plane). Determining the plane of the pleura allows for the extraction of multiple slices along a second plane parallel to the plane that allows the best view of the B-line, as described below in reference method 400. Figures 7A to 7D As described.

[0084] refer to Figure 3 A matrix configuration 300 of an ultrasound probe 106 is illustrated. Matrix configuration 300 refers to the arrangement of signal elements 108 within the ultrasound probe 106. More specifically, matrix configuration 300 may include a 2D array of signal elements 108, such that the probe 106 is configured to capture image data from several planes (e.g., sagittal, coronal, transverse, etc.). In other words, using matrix configuration 300, the probe 106 can acquire ultrasound images from multiple scanning planes. More specifically, as described herein, the probe 106 may be configured to acquire ultrasound images of a patient's lungs from the sagittal, coronal, and / or transverse planes using ultrasound at multiple angles and / or between anatomical features and the probe 106. According to an example configuration of the probe 106, matrix configuration 300 may include a 2D array of 6000 elements (e.g., signal elements 108).

[0085] As described herein, the orientation of probe 106 can be adjusted relative to the pleura using matrix configuration 300 (e.g., step 405 of the method). In other words, although at the initial orientation of probe 106, azimuth plane 210 and elevation plane 215 show probe 106 and / or ultrasound beam not perpendicular to the pleura, matrix configuration 300 allows probe 106 to be adjusted to capture image data where probe 106 and / or ultrasound beam are perpendicular to the pleura based on the patient anatomy being imaged during lung ultrasound. As described herein, the orientation of the pleura can help determine secondary anatomical features (e.g., B-line) and may be desired to have the pleura substantially parallel to the coronal plane.

[0086] refer to Figure 4 A flowchart illustrating a method 400 for performing three-dimensional ultrasound imaging using an ultrasound imaging system is shown. In at least one embodiment, the ultrasound imaging system referred to in method 400 is the one described above. Figure 1 The ultrasound imaging system 100 is described, and the method 400 can be implemented by the ultrasound imaging system 100. In some embodiments, the method 400 can be implemented as a memory of the ultrasound imaging system 100 (such as...). Figure 1 Executable instructions in memory 118.

[0087] like Figure 4 As shown, at step 405, method 400 includes identifying a first anatomical feature. As described herein, the transducer of ultrasound imaging system 100 is configured to transmit and receive ultrasound signals in a three-dimensional volume. The three-dimensional volume includes multiple slices in multiple views (e.g., lateral view, coronal view, sagittal view). At step 405, image data (e.g., first image data, second image data) may be sampled to identify the first anatomical feature in the image data. Processing of the ultrasound signals allows for image construction. Processing the ultrasound signals may include converting the echo received from probe 106 into an electrical signal, passing the electrical signal through receiving beamformer 112 for use in ultrasound data, and performing beamforming techniques to generate an image. Processing of the ultrasound signals occurs along a second plane to receive image data. The second plane may be a plane perpendicular to the coronal plane. For example, the second plane may be as follows: Figure 5A The depicted azimuth plane 210 and as shown Figure 5B The depicted elevation plane 215. In various embodiments, the image processing circuit 120 applies processing techniques to the constructed image to further improve the quality of the constructed image (e.g., contrast adjustment, brightness adjustment, noise reduction, etc.).

[0088] In some implementations, step 405 may be performed by processing circuitry 114. For example, as described herein, image data may be acquired during lung ultrasound, and therefore the first anatomical feature may include the pleura of the patient being imaged. However, it should be understood that although the ultrasound imaging system 100 may not be able to capture an image of the lung cavity due to the lung cavity being filled with air, an image of the lung cavity may be derived using the surrounding anatomical structures / features depicted in the image data (e.g., ribs, pleura 502, etc.).

[0089] Identifying the first anatomical feature at step 405 may include segmenting the first anatomical feature. Segmentation of the first anatomical feature may produce a binary mask of the first anatomical feature (e.g., Figure 6A and Figure 6B A binary mask depicting the pleura is used. While various methods can be employed to segment the first anatomical feature, one approach is to train a segmentation model (e.g., U-Net, MaskR-CNN, DeepLab, etc.). The trained model analyzes image data acquired along a second plane and outputs the probability that each data point (e.g., pixel) in the image data is either the first anatomical feature or the background (i.e., non-first anatomical feature). A threshold is set for the probability to determine which pixels are classified as first anatomical features, and then a binary mask of the first anatomical feature is created for identification.

[0090] Segmentation of the first anatomical feature allows for estimation of the feature's location and probe angle. As described herein, determining the location and probe angle of the first anatomical feature allows for the determination of the plane in which it resides. Determining this plane allows for the extraction of slices parallel to that plane from a three-dimensional volume, which can present information about the second anatomical feature. For example, with the pleura, because the B-line is located deep beneath and moves with the pleura, extracting slices parallel to the plane of the pleura allows for the detection of the B-line. Since the pleura is substantially parallel to the patient's body, as referenced herein... Figure 2 As described, therefore in various embodiments, the plane and the extracted slices are substantially parallel to the coronal plane. This highlights the benefit of using three-dimensional ultrasound imaging to detect B-lines, as coronal views, as well as sagittal and lateral views, are impossible to achieve with two-dimensional ultrasound imaging.

[0091] Various methods can be used to fit the identified first anatomical feature to this plane. For example, principal component analysis (PCA) or least squares fitting can be used. Fitting the identified first anatomical feature provides a representation of the identified first anatomical feature. The plane of the identified first anatomical feature forms a vector along a second plane of the image data at the corresponding location in the plane (e.g., as shown in the image data). Figure 6A and Figure 6B The described vector is 604.

[0092] Additionally, as described, determining the angle between the ultrasound beam and the normal of the first anatomical feature (i.e., the probe angle) allows for the determination of a plane. This can be done by first characterizing the identified first anatomical feature in the first image data and the second image data as a first vector and a second vector, respectively, to determine the normal of the anatomical feature. The first image data and the second image data are obtained along a second plane. Various methods can be used to fit the identified anatomical feature to a best-fit line. For example, orthogonal distance regression (ODR) can be used to calculate the best-fit line and determine the first vector and the second vector. ODR makes each data point (e.g., at a distance from the first anatomical feature)... Figure 6A and Figure 6B The sum of squared orthogonal distances between the white pixels in the illustrated mask is minimized to form a vector. Although the first and second vectors are shown in 2D space, they can be extended to 3D space (e.g., by adding zeros in either the X or Y dimension, which depends on the plane).

[0093] The normal to the first anatomical feature can be determined by identifying a vector perpendicular to both the first and second vectors. The cross product of the first and second vectors can be used to determine the normal to the anatomical feature. Once the normal to the anatomical feature is determined, the angle between the ultrasound beam and the normal to the anatomical feature can be determined. A trigonometric function (e.g., inverse cosine) can be used to determine the angle. Once the angle is determined, the plane of the first anatomical feature with that angle can be determined. In various embodiments, this angle can be used to determine an optimal view of the first anatomical feature, and a second ultrasound signal can be transmitted to align the first anatomical feature to the optimal view. For example, for the pleura, the optimal view might be such that the plane of the pleura is parallel to the coronal view and the angle is 0°.

[0094] like Figure 4 As shown, method 400 includes receiving first image data at step 410. As described with reference to step 405, the first image data is received by extracting slices from a three-dimensional volume along a plane parallel to a first anatomical feature. See below for further details. Figure 11A and Figure 11B As described, the first image data can be averaged to improve image quality. In biplane ultrasound imaging, commonly used for lung imaging, the views shown are typically a sagittal view and a transverse view. The first image data can be obtained along a view substantially parallel to the coronal view, which is perpendicular to both the sagittal and transverse views. In other words, the first image data can be along a cross-sectional plane of either the sagittal or transverse view.

[0095] like Figure 4As shown, method 400 continues at step 415 to identify at least one second anatomical feature based on the first image data. Processing circuitry 114 (e.g., image processing circuitry 120, AI circuitry 122, etc.) may be configured to identify the second anatomical feature using a segmentation model (e.g., U-Net, Mask R-CNN, DeepLab). The identified second anatomical feature may be displayed as a binary mask. (See references herein.) Figure 7C and Figure 7D As described above, the second anatomical feature could be line B702, where line B appears as a circle in the first image data.

[0096] like Figure 4 As shown, method 400 continues at step 420 to determine the algorithm output. The algorithm output includes at least one of the following: the number of identified second anatomical features, the area of ​​at least one second anatomical feature among the second anatomical features, or the area of ​​at least one second anatomical feature divided by the area of ​​the first anatomical feature. Step 420 may be determined by processing circuitry 114 based on the first image data. As described with reference to step 415, the number of second anatomical features may be determined from the segmented second anatomical features. An AI algorithm (e.g., along processing circuitry 114, AI circuitry 122, etc.) may match the segmented second anatomical features from the first image data positionally to ensure that the second anatomical features are not double-counted. In other words, a second anatomical feature at a position in one first image data should remain at approximately the same position in another first image data. Thus, the number of second anatomical features is determined. The area of ​​at least one second anatomical feature among the second anatomical features may be determined using the segmentation of the anatomical features. For example, pixels from the segmented second anatomical features may be used to determine the area of ​​at least one second anatomical feature among the second anatomical features. The area of ​​the first anatomical feature can be determined using a similar method to that used to determine the area of ​​the second anatomical feature. This determination then allows us to divide the area of ​​the second anatomical feature by the area of ​​the first anatomical feature.

[0097] like Figure 4 As shown, method 400 includes displaying an output image based on the first image data at step 425 and / or displaying an output image based on the algorithm output at step 430. In some embodiments, the first image data and / or the algorithm output are displayed on a display device 132. The output image may include multiple ultrasound images of the first image data, allowing the operator to visualize the second anatomical feature and other anatomical features. Additionally, displaying the algorithm output can provide the operator with more information about the second anatomical feature.

[0098] In various embodiments, method 400 further includes determining a third plane intersecting a second anatomical feature in at least one first image data. The third plane may be perpendicular to the plane of the first image data. In various embodiments, the third plane may be parallel to a transverse plane or a sagittal plane. Operation of the ultrasound imaging system 100 can be used to view anatomical features (e.g., a first anatomical feature, a second anatomical feature, etc.) in transverse and / or sagittal views. By determining the third plane, second image data along the third plane can be received, making the second image data displayable. This allows an operator to view the determined second anatomical feature in a transverse and / or sagittal view (i.e., a view that the operator is likely to be comfortable with). As referenced herein... Figure 11A As described, viewing the second anatomical feature in lateral or sagittal and coronal views allows the operator to distinguish the second anatomical feature from other anatomical features. For example, distinguishing the B line from the Z line.

[0099] refer to Figures 5A to 5B This illustrates a biplane image of a first ultrasound image 500a acquired in a first plane and a second ultrasound image 500b acquired in a second plane. The first plane is orthogonal to the second plane (e.g., an azimuth plane and an elevation plane). [The image is] usable... Figure 4 The techniques described herein (e.g., step 405) are used to acquire a first ultrasound image 500a and a second ultrasound image 500b. The first ultrasound image 500a and the second ultrasound image 500b show the pleura 502. First image data and second image data, respectively, from the first ultrasound image 500a and the second ultrasound image 500b, can be used to determine the position of the pleura 502 and the probe angle.

[0100] refer to Figures 6A to 6BImages of a first ultrasound image 500a and a second ultrasound image 500b following image processing (e.g., step 405) are shown. The pleura 502 in the first ultrasound image 500a and the second ultrasound image 500b is segmented, which is depicted by the segmented pleura 602. Segmentation of the pleura 502 can be performed using the segmentation model disclosed herein. Data points (e.g., pixels) of the segmented pleura 602 are fitted onto a line to create a vector 604. Vector 604 is used to determine the normal of the pleura 502 (e.g., by calculating the cross product of vectors 604 in the two images to determine the normal vector perpendicular to both vectors 604, i.e., the normal of the pleura 502). As disclosed herein (e.g., at step 405), the normal of the pleura 502 can be used to determine the angle between the pleural normal and the ultrasound beam, which allows determination of the plane in which the pleura 502 lies. In various embodiments, this plane is substantially parallel to the coronal plane. In various implementations, a transmission direction is determined to adjust the second ultrasound signal to a desired angle (e.g., 90°) to receive the third image dataset, wherein the pleura 502 is substantially parallel to the coronal plane and a new three-dimensional volume is obtained.

[0101] refer to Figures 7A to 7D The image shows three-dimensional ultrasound images of a first ultrasound image 700a, a second ultrasound image 700b, a third ultrasound image 700c, and a fourth ultrasound image 700d. The ultrasound images are extracted from a three-dimensional volume obtained by transmitting and receiving ultrasound signals. The second ultrasound image 700b, the third ultrasound image 700c, and the fourth ultrasound image 700d are extracted (cross-sectional) slices of a plane parallel to the pleura 502 (e.g., substantially in a coronal view). The locations of the slices are shown in the first ultrasound image 700a (e.g., slice 704 for the second ultrasound image 700b, slice 706 for the third ultrasound image 700c, and slice 708 for the fourth ultrasound image 700d). In the first ultrasound image 700a, B-lines 702 are visualized. Although the second ultrasound images 700b and the fourth ultrasound image 700d do not provide a clear detection of B-lines 702, the third ultrasound image shows B-lines 702 appearing as points, thus showing three B-lines 702. In various implementations, B-line 702 may not be visualized in the third ultrasound image 700c without being segmented, thus highlighting the importance of obtaining multiple slices.

[0102] refer to Figures 8A to 8DThe diagram shows three-dimensional ultrasound images of a first ultrasound image 800a, a second ultrasound image 800b, a third ultrasound image 800c, and a fourth ultrasound image 800d. The ultrasound images are extracted from a three-dimensional volume obtained by transmitting and receiving ultrasound signals. The second ultrasound image 800b, the third ultrasound image 800c, and the fourth ultrasound image 800d are extracted (cross-sectional) slices parallel to a plane parallel to the pleura 502. The positions of the slices are shown in the first ultrasound image 800a (e.g., slice 804 for the second ultrasound image 800b, slice 806 for the third ultrasound image 800c, and slice 808 for the fourth ultrasound image 800d). In the first ultrasound image 800a, B-line 702 is faintly visible and may not be correctly detected. However, in the cross-sectional slices of the second ultrasound image 800b, the third ultrasound image 800c, and the fourth ultrasound image 800d, B-line 702 is correctly detected and segmented by the algorithm. The B-line 702 in the first position 810 is detected as the same B-line 702 across the second ultrasound image 800b, the third ultrasound image 800c, and the fourth ultrasound image 800d. In various embodiments, and as described herein, coloring can be used to distinguish the B-line 702. For example, the first B-line 702 may be highlighted in a slice with a first color, which is different from the second color in which the second B-line 702 is highlighted.

[0103] refer to Figures 9A to 9D The diagram shows three-dimensional ultrasound images of a first ultrasound image 900a, a second ultrasound image 900b, a third ultrasound image 900c, and a fourth ultrasound image 900d. The ultrasound images are extracted from a three-dimensional volume obtained by transmitting and receiving ultrasound signals. The second ultrasound image 900b, the third ultrasound image 900c, and the fourth ultrasound image 900d are extracted (cross-sectional) slices parallel to a plane parallel to the pleura 502. The positions of the slices are shown in the first ultrasound image 900a (e.g., slice 904 for the second ultrasound image 900b, slice 906 for the third ultrasound image 900c, and slice 908 for the fourth ultrasound image 900d). In the first ultrasound image 900a, B-line 702 is faintly visible and may not be correctly detected. However, in the cross-sectional slices of the second ultrasound image 900b, the third ultrasound image 900c, and the fourth ultrasound image 900d, B-line 702 is correctly detected and segmented by the algorithm. Although the slice from Figure 9 did not detect as many B-lines 702 as the slice from Figure 8, extracting several slices could allow the maximum number of B-lines 702 to be found.

[0104] refer to Figures 10A to 10FThree-dimensional ultrasound images of a first ultrasound image 1000a, a second ultrasound image 1000b, a third ultrasound image 1000c, and a fourth ultrasound image 1000d are shown. As described herein, the cross-sectional view shown in the first ultrasound image 1000a may not be the preferred view for the operator of the ultrasound imaging system 100. Therefore, ultrasound images in preferred views (e.g., lateral views, sagittal views, the second ultrasound image 1000b, the third ultrasound image 1000c, the fourth ultrasound image 1000d, etc.) can be obtained (e.g., extracted from a 3D volume obtained by 3D ultrasound imaging). Figure 10A , Figure 10C and Figure 10E As shown, by determining the vector intersecting the second anatomical feature (in this example, line B 702), the plane corresponding to that vector can be determined by extending the vector into a view perpendicular to the cross-sectional view (e.g., a transverse view, a sagittal view, etc.). For example, as... Figure 10A As shown, vector 1002 intersects line B 702, and Figure 10B A second ultrasound image 1000b, obtained along the plane of vector 1002, is depicted. In another example, Figure 10C Examples Figure 10A The same ultrasound image. However, in this example, vector 1004 intersects with another B-line 702, and Figure 10D A third ultrasound image 1000c, obtained along the plane of vector 1004, is depicted. In another example, such as... Figure 10E As shown, vector 1006 intersects with another line B, 702, and Figure 10F A fourth ultrasound image 1000d is depicted, taken along a plane of vector 1006. In these examples, the plane of the first ultrasound image 1000a may be represented by vector 1008 (e.g., in the second ultrasound image 1000b, the third ultrasound image 1000c, and the fourth ultrasound image 1000d) and is perpendicular to the image showing a cross-sectional view of the ultrasound image. In various embodiments, as referenced herein... Figures 8A to 8D As described, color can be used to distinguish line B, 702. For example, Figure 10A In the second ultrasound image 1000b, B-line 702 can be represented as the first color.

[0105] Additionally, by finding the vector that intersects with the maximum number of B-lines, the plane on which the maximum number of B-lines appear can be determined and displayed. (Reference) Figures 11A to 11CThe diagram illustrates three-dimensional ultrasound images of a first ultrasound image 1100a, a second ultrasound image 1100b, and a third ultrasound image 1100c. The ultrasound images are extracted from a three-dimensional volume received from 3D ultrasound image data. When images are received in a lateral view and / or a sagittal view (e.g., first ultrasound image 1100a, first ultrasound image 1000a, first ultrasound image 800a, etc.), the image can be used to distinguish between B-lines and Z-lines. For example, the first ultrasound image 1100a shows Z-line 1106 and B-line 702. A cross-sectional image (e.g., along a coronal view) of the first ultrasound image 1100a can depict both Z-line 1106 and B-line 702 based on the location of the slice from which the cross-sectional image was extracted. However, by using the first ultrasound image 1100a, Z-line 1106 and B-line 702 can be distinguished based on the length of the lines in the first ultrasound image 1100a, since B-line 702 has a longer length than Z-line 1106.

[0106] A first ultrasound image 1100a can be used to extract multiple planes, including a cross-sectional plane 1102. A second ultrasound image 1100b shows an image obtained along the cross-sectional plane 1102. In the second ultrasound image 1100b, three B-lines 702 are detected. By determining a vector 1104 intersecting the three B-lines 702, a third ultrasound image 1100c can be extracted from the plane of vector 1104. Thus, the operator can view the third ultrasound image 1100c to determine the maximum number of B-lines in the patient. As described herein, finding the plane on which the maximum number of B-lines appears provides a consistent way for operators to assess a patient's lungs and addresses the current problem that lung scoring depends on the operator's level of expertise.

[0107] refer to Figure 12A and Figure 12B The figure illustrates a first ultrasound image 1200a and a second ultrasound image 1200b. The first ultrasound image 1200a is extracted from a single cross-sectional plane, while the second ultrasound image 1200b is an average of multiple cross-sectional planes. As shown, the signal-to-noise ratio can be improved by averaging multiple cross-sectional planes instead of extracting only one cross-sectional plane for display and processing. Furthermore, averaging the cross-sectional planes helps distinguish the B-line from noise in the cross-sectional view as the B-line moves with the pleura, and it appears more prominent in ultrasound images where the planes are averaged, as depicted in the second ultrasound image 1200b.

[0108] The embodiments described herein have been illustrated with reference to the accompanying drawings. The drawings illustrate certain details of specific embodiments providing the systems, methods, and procedures described herein. However, the use of the drawings to describe the embodiments should not be construed as imposing any limitations that may exist in the drawings on the content of this disclosure.

[0109] It should be understood that no element of any claim herein may be applied under 35 USC. The provisions of 112(f) shall be interpreted unless the element is explicitly described using the phrase “parts for…”.

[0110] As used herein, terms of degree such as “about,” “approximately,” “substantially,” and similar terms are intended to have a broad meaning consistent with common and accepted usage by one of ordinary skill in the art to which the subject matter of this disclosure pertains. Those skilled in the art who read this disclosure will understand that these terms are intended to allow for the description of certain features described and claimed, without limiting the scope of these features to any precise numerical range provided. Therefore, these terms should be interpreted as indicating that non-substantial or irrelevant modifications or alterations to the described and claimed subject matter are considered to be within the scope of the disclosure set forth in the appended claims.

[0111] It should be noted that terms such as “exemplary,” “example,” and similar terms used herein to describe various implementations are intended to indicate that such implementations are possible examples, representations, or illustrations of possible implementations, and such terms are not intended to imply that such implementations are necessarily special or excellent examples.

[0112] As used herein, the term “coupling” and its variations refer to the direct or indirect engagement of two components with each other. Such engagement can be static (e.g., permanent or fixed) or movable (e.g., removable or releasable). Such engagement can be achieved by directly coupling the two components together, by coupling the two components together using a separate intermediate component and any additional intermediate components coupled to each other, or by coupling the two components together using an intermediate component integrally formed with one of the two components. If “coupling” or its variations are modified by an additional term (e.g., direct coupling), the general definition of “coupling” provided above will be modified by the common linguistic meaning of the additional term (e.g., “direct coupling” refers to the engagement of two components without any separate intermediate component), resulting in a narrower definition than the general definition of “coupling” provided above. Such coupling can be mechanical, electrical, or fluid.

[0113] As used herein, the term "or" is inclusive (not exclusive), and therefore, when used to connect lists of elements, the term "or" indicates one, some, or all of the elements in the list. Unless otherwise explicitly stated, conjunctions such as the phrase "at least one of X, Y, and Z" should be understood to mean that the elements can be X, Y, and Z; X and Y; X and Z; Y and Z; or X, Y, and Z (i.e., any element on its own or any combination of X, Y, and Z). Therefore, unless otherwise stated, such conjunctions generally do not imply that certain embodiments require at least one of X, at least one of Y, and at least one of Z to be present separately.

[0114] References to element positions in this document (e.g., "top", "bottom", "above", "below") are used only to describe the orientation of the individual elements in the figure. It should be noted that, according to other exemplary embodiments, the orientation of the various elements may differ, and such variations are intended to be covered by this disclosure.

[0115] As used herein, terms such as “engine” or “circuit” can include hardware and machine-readable media on which instructions for configuring hardware to perform the functions described herein are stored. An engine or circuit can be embodied as one or more circuit components, including but not limited to processing circuitry, network interfaces, peripheral devices, input devices, output devices, sensors, etc. In some embodiments, an engine or circuit can take the form of one or more analog circuits, electronic circuits (e.g., integrated circuits (ICs), discrete circuits, system-on-a-chip (SoC) circuits, etc.), telecommunications circuits, hybrid circuits, and any other type of circuit. In this respect, an engine or circuit can include any type of component for implementing or facilitating the implementation of the operations described herein. For example, an engine or circuit as described herein can include one or more transistors, logic gates (e.g., NAND, AND, NOR, OR, XOR, NOT, XNOR, etc.), resistors, multiplexers, registers, capacitors, inductors, diodes, wiring, etc.

[0116] An engine or circuit may be embodied as one or more processing circuits, which include one or more processors communicatively coupled to one or more memories or memory devices. In this respect, the one or more processors may execute instructions stored in memory or instructions otherwise accessible to the one or more processors. The one or more processors may be constructed in a manner sufficient to perform at least the operations described herein. In some embodiments, the one or more processors may be shared by multiple engines or circuits (e.g., engine A and engine B, or circuit A and circuit B may include or otherwise share the same processor, which in some example embodiments may execute instructions stored or otherwise accessed via different regions of memory).

[0117] Alternatively or otherwise, one or more processors may be configured to perform or otherwise perform certain operations independently of one or more coprocessors. In other example embodiments, two or more processors may be bus-coupled to enable independent, parallel, pipelined, or multithreaded instruction execution. Each processor may be provided as one or more suitable processors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), or other suitable electronic data processing components configured to execute instructions provided by memory. One or more processors may take the form of a single-core processor, a multi-core processor (e.g., a dual-core processor, a triple-core processor, a quad-core processor, etc.), a microprocessor, etc. In some embodiments, one or more processors may be external to the device; for example, one or more processors may be remote processors (e.g., cloud-based processors). Alternatively or otherwise, one or more processors may be internal to the device and / or local to the device. In this regard, a given engine or circuitry or its components may be deployed locally (e.g., as part of a local server, a local computing system, etc.) or remotely (e.g., as part of a remote server such as a cloud-based server). For this purpose, an engine or circuitry as described herein may include components distributed across one or more locations.

[0118] Example systems used to provide an overall system or part of the embodiments described herein may include one or more computers, including processing units, system memory, and a system bus coupling various system components, including the system memory, to the processing units. Each memory device may include a non-transitory volatile storage medium, a non-volatile storage medium (e.g., one or more volatile and / or non-volatile memories), etc. In some embodiments, the non-volatile medium may be in the form of ROM, flash memory (e.g., flash memory such as NAND, 3D NAND, NOR, 3D NOR, etc.), EEPROM, MRAM, magnetic storage devices, hard disks, optical disks, etc. In other embodiments, the volatile storage medium may be in the form of RAM, TRAM, ZRAM, etc. Combinations of the above are also included within the scope of machine-readable media. In this regard, machine-executable instructions include, for example, instructions and data that cause a general-purpose computer, a special-purpose computer, or a special-purpose processor to perform a function or a set of functions. According to the example implementation described herein, each respective memory device is operable to retain or otherwise store information relating to operations performed by one or more associated circuits, including processor instructions and related data (e.g., database components, object code components, script components, etc.).

[0119] Although the accompanying drawings may illustrate and the specification may describe a particular order and composition of method steps, the order of these steps may differ from the order depicted and described. For example, two or more steps may be performed simultaneously or partially simultaneously. Furthermore, some method steps performed as discrete steps may be combined, steps performed as combined steps may be divided into discrete steps, the order of certain processes may be reversed or otherwise altered, and the nature or number of discrete processes may be changed or varied. According to alternative embodiments, the order or sequence of any element or device may be changed or replaced. Therefore, all such modifications are intended to be included within the scope of this disclosure as defined in the appended claims. Such variations may depend, for example, on the chosen software and hardware system and the designer's choice. All such variations are within the scope of this disclosure. Similarly, software implementations of the described methods may be accomplished using standard programming techniques with rule-based logic and other logic to perform various connection steps, processing steps, comparison steps, and decision steps.

[0120] The foregoing description of embodiments has been given for purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure to the precise forms disclosed, and modifications and variations can be made, or may be derived from, the teachings described above. These embodiments were chosen and described to explain the principles of the disclosure and its practical application, thereby enabling those skilled in the art to utilize the various embodiments and make various modifications suitable for the intended particular purpose. Other substitutions, modifications, alterations, and omissions may be made to the design, operating conditions, and arrangement of the embodiments without departing from the scope of the disclosure as set forth in the appended claims.

Claims

1. An ultrasound imaging system (100), the ultrasound imaging system comprising: A transducer configured to transmit and receive ultrasonic signals in a three-dimensional volume comprising a plurality of planes, the plurality of planes including cross-sectional planes; and Processing circuit (114) having a processor (116) coupled to a memory device (118) storing instructions that, when executed, cause the processing circuit (114) to perform operations including: A first anatomical feature is identified based on initial image data obtained from the three-dimensional volume by an ultrasound probe (106) along a first plane intersecting the surface of the ultrasound probe (106), the first anatomical feature being determined to be along the cross-sectional plane; First image data is received from the three-dimensional volume parallel to the cross-sectional plane; At least one second anatomical feature is identified based on the first image data; The algorithm output is determined based on the first image data, and the algorithm output includes at least one of the following: the number of identified second anatomical features, the area of ​​at least one of the second anatomical features, or the area of ​​at least one of the second anatomical features divided by the area of ​​the first anatomical feature; and An output image based on at least one of the algorithm output or the first image data is displayed on the display screen (132) of the ultrasound imaging system (100).

2. The ultrasound imaging system (100) according to claim 1, wherein the first plane includes an azimuth plane (210) and an elevation plane (215).

3. The ultrasound imaging system (100) according to claim 1, wherein the first anatomical feature is the pleura (502) and the second anatomical feature is a B-line (702), and the first image data is obtained deep beneath the pleura (502).

4. The ultrasound imaging system (100) according to claim 3, wherein the B-line (702) is shown to have a circular shape based on the first image data.

5. The ultrasound imaging system (100) of claim 1, wherein the cross-sectional plane is determined based on at least one of the following: the location of the first anatomical feature, or the angle between the transmitted signal and the normal of the first anatomical feature.

6. The ultrasound imaging system (100) according to claim 1, wherein determining the cross-sectional plane comprises: Determine the angle between the transmitted signal and the normal of the first anatomical feature; Determine a first transmission direction that adjusts the angle between the transmitted signal and the normal of the first anatomical feature to substantially 0°; and The second ultrasonic signal is automatically aligned in the first transmission direction to adjust the angle to the first angle.

7. The ultrasound imaging system (100) according to claim 1, wherein the operation further includes: A third plane is determined that intersects with the second anatomical feature in the first image data, the third plane being perpendicular to the cross-sectional plane; Receive second image data along the third plane; as well as The output image is displayed based on the second image data.

8. The ultrasound imaging system (100) of claim 7, wherein a second anatomical feature in the second anatomical feature based on the first image data is a first color, and the second anatomical feature based on the second image data is the first color.

9. The ultrasound imaging system (100) of claim 1, wherein the operation further comprises determining a third plane comprising a maximum number of second anatomical features based on the first image data, the third plane being parallel to the cross-sectional plane.

10. The ultrasound imaging system (100) of claim 9, wherein the operation further comprises determining a vector in the third plane that intersects with the maximum number of second anatomical features, the vector extending into a fourth plane perpendicular to the cross-sectional plane.

11. The ultrasound imaging system (100) of claim 10, wherein the operation further includes receiving second image data along the third plane, receiving third image data along the fourth plane, and displaying the output image based on at least one of the second image data or the third image data.

12. The ultrasound imaging system (100) of claim 1, wherein the first image data includes more than one first image data that is averaged to create averaged first image data, the averaged first image data being used to identify the second anatomical feature.

13. An ultrasound imaging system (100), the ultrasound imaging system comprising: A transducer configured to transmit and receive ultrasonic signals in a three-dimensional volume, the three-dimensional volume comprising a plurality of planes, the plurality of planes including a cross-sectional plane; An image processing circuit (120) is configured to identify a first anatomical feature based on initial image data obtained from the three-dimensional volume, and to receive the first image data from the three-dimensional volume parallel to the cross-sectional plane; and Control circuit (124), the control circuit being configured to: The algorithm output is determined based on the first image data, and the algorithm output includes at least one of the following: the number of identified second anatomical features, the area of ​​at least one of the second anatomical features, or the area of ​​at least one of the second anatomical features divided by the area of ​​the first anatomical feature. as well as An output image based on at least one of the algorithm output or the first image data is displayed on the display screen of the ultrasound imaging system (100).

14. The ultrasound imaging system (100) according to claim 13, wherein the second anatomical feature is a B-line (702).

15. The ultrasound imaging system (100) according to claim 13, wherein the cross-sectional plane is determined by: Identify at least one second anatomical feature among the second anatomical features based on the initial image data obtained along a first plane perpendicular to the cross-sectional plane; and A second plane is determined in which the at least one second anatomical feature is located, the cross-sectional plane being parallel to the second plane.

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