CN116138807A - Ultrasonic imaging equipment and ultrasonic detection method of abdominal aorta - Google Patents

Ultrasonic imaging equipment and ultrasonic detection method of abdominal aorta Download PDF

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Publication number
CN116138807A
CN116138807A CN202211469392.7A CN202211469392A CN116138807A CN 116138807 A CN116138807 A CN 116138807A CN 202211469392 A CN202211469392 A CN 202211469392A CN 116138807 A CN116138807 A CN 116138807A
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inner diameter
image
abdominal aorta
axis
short
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Chinese (zh)
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裴海军
刘硕
林穆清
胡锦明
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Shenzhen Mindray Bio Medical Electronics Co Ltd
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Shenzhen Mindray Bio Medical Electronics Co Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0891—Detecting organic movements or changes, e.g. tumours, cysts, swellings for diagnosis of blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/13—Tomography
    • A61B8/14—Echo-tomography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/44—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device
    • A61B8/4444—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device related to the probe
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/46—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient
    • A61B8/467—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient characterised by special input means
    • A61B8/469—Ultrasonic, sonic or infrasonic diagnostic devices with special arrangements for interfacing with the operator or the patient characterised by special input means for selection of a region of interest
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data

Abstract

The invention provides ultrasonic imaging equipment and an ultrasonic detection method of abdominal aorta, which are characterized in that a long-axis section image and a short-axis section image of the abdominal aorta are obtained; identifying an abdominal aorta region from the short-axis section image and measuring to obtain a first inner diameter value of the abdominal aorta region; identifying an abdominal aorta region from the long-axis section image and measuring to obtain a second inner diameter value of the abdominal aorta region; and judging the risk of the abdominal aortic aneurysm according to the first inner diameter value and the second inner diameter value to obtain a judging result, thereby providing a diagnosis and treatment basis for doctors and improving the efficiency of abdominal aortic aneurysm examination.

Description

Ultrasonic imaging equipment and ultrasonic detection method of abdominal aorta
Technical Field
The invention relates to the field of medical equipment, in particular to ultrasonic imaging equipment and an ultrasonic detection method of abdominal aorta.
Background
Compared with other imaging examinations, the bedside immediate ultrasonic examination greatly saves examination time, reduces cost, avoids the use of ionizing radiation and vein contrast agents, has wide application in clinical examination, and becomes one of main auxiliary means for emergency treatment and serious doctor to diagnose diseases. Abdominal pain is a common condition in emergency situations, where examination of the abdominal aorta is a common item of emergency ultrasound. Abdominal aortic aneurysms are the most common aortic abnormalities, often accompanied by thrombosis, and intimal dissection (i.e. dissecting aneurysms) or rupture, with high mortality. Thus, any patient with abdominal discomfort should be considered for aortic imaging.
Measurement of the abdominal aorta generally involves scanning of both the transverse and longitudinal sections. In practice, the physician needs to place the ultrasound probe at the proximal end of the abdominal aorta with the probe marker facing the right side of the patient. In this position, the celiac dry artery and superior mesenteric artery are typically observed, and when the aorta is observed, the probe is slid from above and below along the abdominal wall for continuous imaging, typically until the left and right common iliac arteries are seen at the distal end of the aorta. The abdominal aortic inner diameter and changes are measured and observed in real time during the cross-section scanning. After the cross-section scan is completed, the probe is placed at the proximal end of the abdominal aorta and rotated 90 ° clockwise with the probe mark facing the patient's head side, and when the aorta is observed, an inner diameter measurement of the aorta can be performed. Longitudinal section scanning is required to be performed at the far end, and steps are similar to those of the scanning at the near end, and are not repeated. It can be seen that in the process of collecting the image of the abdominal aorta and measuring the internal diameter, the doctor needs to frequently judge the position of the abdominal aorta, manually measure the internal diameter, observe whether an internal valve exists, and then determine whether the abdominal valve is a dissection aneurysm or the like through color Doppler ultrasound, wherein a plurality of repeated operations exist. The diagnostic time of the abdominal aortic measurement, abdominal aortic aneurysm or dissection aneurysm is prolonged. Meanwhile, for emergency treatment and ICU doctors which are not familiar with ultrasound, the position of the abdominal aorta may not be accurately judged, and the measurement result may be inaccurate. Therefore, the efficiency of abdominal aortic examination in emergency or intensive ultrasound is still to be improved.
Disclosure of Invention
The invention mainly provides ultrasonic imaging equipment and an ultrasonic detection method of abdominal aorta, aiming at improving the efficiency of abdominal aorta examination.
An embodiment provides an ultrasound imaging apparatus comprising:
an ultrasonic probe;
a transmission/reception control circuit for controlling the ultrasonic probe to transmit ultrasonic waves to the region of interest and to receive echoes of the ultrasonic waves;
the man-machine interaction device is used for performing visual output and receiving input of a user;
the processor is used for acquiring an ultrasonic image of a standard section of the abdominal aorta, wherein the ultrasonic image of the standard section comprises a long-axis section image and a short-axis section image; identifying an abdominal aortic region from the short-axis section image and measuring to obtain a first inner diameter value of the abdominal aortic region; identifying an abdominal aorta region from the long-axis section image and measuring to obtain a second inner diameter value of the abdominal aorta region; and judging the risk of the abdominal aortic aneurysm according to the first inner diameter value and the second inner diameter value, and obtaining a judging result.
An embodiment provides an ultrasound imaging apparatus comprising:
an ultrasonic probe;
a transmission/reception control circuit for controlling the ultrasonic probe to transmit ultrasonic waves to the region of interest and to receive echoes of the ultrasonic waves;
The man-machine interaction device is used for performing visual output and receiving input of a user;
the processor is used for acquiring ultrasonic images of a standard section of the abdominal aorta, wherein the ultrasonic images of the standard section comprise short-axis section images of a plurality of frames at different moments; respectively identifying abdominal aorta areas from the short-axis section images of the multiple frames at different moments; measuring first inner diameters of abdominal aorta areas in the multi-frame short-axis-section images at different moments respectively to obtain a plurality of first inner diameter values; obtaining a change trend of the first inner diameter along with time according to the plurality of first inner diameter values corresponding to the multi-frame short-axis section images at different moments; and displaying the change trend through the man-machine interaction device.
An embodiment provides an ultrasound imaging apparatus comprising:
an ultrasonic probe;
a transmission/reception control circuit for controlling the ultrasonic probe to transmit ultrasonic waves to the region of interest and to receive echoes of the ultrasonic waves;
the man-machine interaction device is used for performing visual output and receiving input of a user;
the processor is used for acquiring an ultrasonic image of a standard section of the abdominal aorta, wherein the ultrasonic image of the standard section comprises a long-axis section image; identifying an abdominal aorta region from the long-axis section image, and measuring second inner diameters at a plurality of measuring points arranged along the long axis direction of the abdominal aorta on the abdominal aorta region to obtain a plurality of second inner diameter values; obtaining the change trend of the second inner diameter along with the position according to the positions of the plurality of measuring points and the corresponding second inner diameter value; and displaying the change trend through the man-machine interaction device.
In an ultrasound imaging apparatus provided in an embodiment, the processor is further configured to:
and detecting whether the interlayer aneurysm exists in the short-axis surface image by using a pre-trained model, and obtaining a detection result.
In an ultrasound imaging apparatus provided in an embodiment, the processor is further configured to:
and combining the judging result and the detecting result to obtain a risk assessment result of the abdominal aorta, and displaying the risk assessment result through the man-machine interaction device.
In the ultrasonic imaging device provided by an embodiment, the short-axis facet image included in the ultrasonic image of the standard facet is a short-axis facet image of a plurality of frames at different moments; the processor is further configured to:
respectively identifying abdominal aorta areas from the short-axis section images of the multiple frames at different moments; measuring first inner diameters of abdominal aorta areas in the multi-frame short-axis-section images at different moments respectively to obtain a plurality of first inner diameter values; obtaining a change trend of the first inner diameter along with time according to the plurality of first inner diameter values corresponding to the multi-frame short-axis section images at different moments; and displaying the change trend through the man-machine interaction device.
In the ultrasonic imaging apparatus provided in an embodiment, the trend of the change of the first inner diameter with time is a graph of the change of the first inner diameter with time, and the processor is further configured to:
Finding a maximum first inner diameter value from the plurality of first inner diameter values;
when the human-computer interaction device displays the change curve graph, marking the maximum first inner diameter value on the change curve graph, and displaying a short-axis section image corresponding to the maximum first inner diameter value.
In the ultrasonic imaging apparatus provided in an embodiment, the trend of the change of the first inner diameter with time is a graph of the change of the first inner diameter with time, and the processor is further configured to:
receiving an instruction of a user for selecting a time point on the change curve chart through the man-machine interaction device; responding to the instruction, and displaying a short-axis section image corresponding to the selected time point through the man-machine interaction device; or alternatively, the process may be performed,
displaying the multi-frame short-axis facet images at different moments through the man-machine interaction device, and receiving an instruction of selecting one frame of short-axis facet image from the multi-frame short-axis facet images at different moments through the man-machine interaction device; in response to the instructions, a first inside diameter value of a first inside diameter of the abdominal aorta in the selected short-axis-facet image is marked on the displayed variation graph.
In an ultrasound imaging apparatus provided in an embodiment, the processor is further configured to: when the selected time point is the time point corresponding to the maximum first inner diameter value, acquiring the maximum second inner diameter value and displaying in parallel; the maximum second inside diameter value is the maximum value of a plurality of second inside diameter values of the abdominal aorta measured from the long axis section image.
In an embodiment, after identifying the abdominal aortic region from the long axis section image, the processor is further configured to:
measuring second inner diameters at a plurality of measuring points arranged along the long axis direction of the abdominal aorta on the abdominal aorta region to obtain a plurality of second inner diameter values; obtaining the change trend of the second inner diameter along with the position according to the positions of the plurality of measuring points and the corresponding plurality of second inner diameter values; and displaying the change trend through the man-machine interaction device.
In the ultrasonic imaging apparatus provided in an embodiment, the long-axis section image included in the ultrasonic image of the standard section has multiple frames; the processor identifies an abdominal aortic region from the long axis section image and measures a second internal diameter value thereof, comprising:
selecting an optimal frame from the multi-frame long-axis section image by using a pre-trained model; identifying the abdominal aorta from the optimal frame and measuring to obtain a second inner diameter value of the abdominal aorta; or alternatively, the process may be performed,
identifying an abdominal aorta area from the multi-frame long-axis section image and measuring to obtain a second inner diameter value of the abdominal aorta area; and comparing the second inner diameter values of the multi-frame long-axis section images to obtain the maximum second inner diameter value.
In the ultrasonic imaging apparatus provided in an embodiment, the trend of the second inner diameter with respect to the position is a graph of the second inner diameter with respect to the position, and the processor is further configured to:
finding a maximum second inner diameter value from the plurality of second inner diameter values;
and when the change curve graph is displayed through the man-machine interaction device, the long-axis tangent plane image is also displayed, and the position of the measuring point corresponding to the maximum second inner diameter value is marked on the displayed long-axis tangent plane image.
In an ultrasound imaging apparatus provided in an embodiment, the processor is further configured to:
receiving an instruction of selecting a position point on the abdominal aorta area of the long-axis section image by a user through the man-machine interaction device; in response to the instruction, marking a second inner diameter value corresponding to a selected location point on the displayed variation graph; or alternatively, the process may be performed,
receiving an instruction of selecting a position point on the change curve graph by a user through the man-machine interaction device; in response to the instruction, the location point is marked on the abdominal aorta region of the long axis section image displayed.
In an ultrasound imaging apparatus provided in an embodiment, the processor is further configured to: when the selected position point is the position point corresponding to the maximum second inner diameter value, acquiring the maximum first inner diameter value and displaying in parallel; the maximum first internal diameter value is the maximum value of the first internal diameter values of the abdominal aorta measured from a plurality of frames of short-axis section images.
In an ultrasound imaging apparatus provided in an embodiment, the processor determines risk of an abdominal aortic aneurysm according to the first inner diameter value and the second inner diameter value, and obtains a determination result, including:
judging whether the first inner diameter value and/or the second inner diameter value is larger than a preset inner diameter threshold value, if so, obtaining a judging result that abdominal aortic aneurysm possibly exists; or alternatively, the process may be performed,
calculating to obtain an inner diameter average value of the first inner diameter value and the second inner diameter value, judging whether the inner diameter average value is larger than a preset inner diameter threshold value, and if so, obtaining a judging result that abdominal aortic aneurysm possibly exists; or alternatively, the process may be performed,
selecting an inner diameter maximum value dmax and an inner diameter minimum value dmi n from the first inner diameter value and the second inner diameter value; calculating according to a preset formula p= (dmax-dmi n)/dmi n to obtain a ratio p; judging whether the ratio p is larger than a preset value, and if so, obtaining a judging result that the abdominal aortic aneurysm possibly exists.
An embodiment provides an ultrasound imaging apparatus comprising:
an ultrasonic probe;
a transmission/reception control circuit for controlling the ultrasonic probe to transmit ultrasonic waves to the region of interest and to receive echoes of the ultrasonic waves;
the man-machine interaction device is used for performing visual output and receiving input of a user;
The processor is used for acquiring a long-axis section image or a short-axis section image of the abdominal aorta; identifying an abdominal aortic region from the long-axis section image or the short-axis section image and measuring to obtain an inner diameter value of the abdominal aortic region; and judging the risk of the abdominal aortic aneurysm according to the inner diameter value to obtain a judging result.
An embodiment provides an ultrasonic detection method of abdominal aorta, comprising:
scanning the short-axis surface of the abdominal aorta by the ultrasonic probe to obtain multi-frame short-axis surface images at different moments;
identifying an abdominal aortic value from the short-axis section image and measuring to obtain a first internal diameter value of the abdominal aortic value;
the ultrasonic probe scans the long-axis section of the abdominal aorta to obtain a long-axis section image;
identifying an abdominal aorta region from the long-axis section image and measuring to obtain a second inner diameter value of the abdominal aorta region;
and judging the risk of the abdominal aortic aneurysm according to the first inner diameter value and the second inner diameter value, and obtaining and displaying a judging result.
An embodiment provides an ultrasonic detection method of abdominal aorta, comprising:
scanning the short-axis surface of the abdominal aorta by the ultrasonic probe to obtain multi-frame short-axis surface images at different moments;
respectively identifying abdominal aorta areas from the short-axis section images of the multiple frames at different moments;
Measuring first inner diameters of abdominal aorta areas in the multi-frame short-axis-section images at different moments respectively to obtain a plurality of first inner diameter values;
and obtaining and displaying the time-dependent change trend of the first inner diameter according to the corresponding time of the short-axis section images of the multiple frames at different time and the first inner diameter value.
An embodiment provides an ultrasonic detection method of abdominal aorta, comprising:
the ultrasonic probe scans the long-axis section of the abdominal aorta to obtain a long-axis section image;
identifying an abdominal aortic region from the long axis slice image;
measuring second inner diameters at a plurality of measuring points arranged along the long axis direction of the abdominal aorta on the abdominal aorta to obtain a plurality of second inner diameter values;
and obtaining and displaying the change trend of the second inner diameter along with the position according to the positions of the plurality of measuring points and the corresponding second inner diameter value.
According to the ultrasonic imaging apparatus and the ultrasonic detection method of the abdominal aorta of the above embodiments, the long-axis section image and the short-axis section image of the abdominal aorta are obtained; identifying an abdominal aorta region from the short-axis section image and measuring to obtain a first inner diameter value of the abdominal aorta region; identifying an abdominal aorta region from the long-axis section image and measuring to obtain a second inner diameter value of the abdominal aorta region; and judging the risk of the abdominal aortic aneurysm according to the first inner diameter value and the second inner diameter value to obtain a judging result, thereby providing a diagnosis and treatment basis for doctors and improving the efficiency of abdominal aortic aneurysm examination.
Drawings
FIG. 1 is a block diagram of an embodiment of an ultrasound imaging apparatus provided by the present invention;
FIG. 2 is a schematic view of an embodiment of a short-axis slice image in an ultrasound imaging apparatus according to the present invention;
FIG. 3 is a schematic view of a display interface displaying a short-axis image and an inside diameter variation curve thereof in an ultrasonic imaging apparatus provided by the present invention;
FIG. 4 is a schematic view of a display interface displaying images of a long-axis tangential plane and an inner diameter distribution curve thereof in an ultrasonic imaging apparatus provided by the present invention;
FIG. 5 is a flowchart of an embodiment of an ultrasonic abdominal aorta detection method according to the invention;
FIG. 6 is a flowchart of another embodiment of an ultrasound examination method for abdominal aorta provided by the invention;
fig. 7 is a flowchart of another embodiment of the method for detecting abdominal aorta according to the invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings by means of specific embodiments. Wherein like elements in different embodiments are numbered alike in association. In the following embodiments, numerous specific details are set forth in order to provide a better understanding of the present application. However, one skilled in the art will readily recognize that some of the features may be omitted, or replaced by other elements, materials, or methods in different situations. In some instances, some operations associated with the present application have not been shown or described in the specification to avoid obscuring the core portions of the present application, and may not be necessary for a person skilled in the art to describe in detail the relevant operations based on the description herein and the general knowledge of one skilled in the art.
Furthermore, the described features, operations, or characteristics of the description may be combined in any suitable manner in various embodiments. Also, various steps or acts in the method descriptions may be interchanged or modified in a manner apparent to those of ordinary skill in the art. Thus, the various orders in the description and drawings are for clarity of description of only certain embodiments, and are not meant to be required orders unless otherwise indicated.
The numbering of the components itself, e.g. "first", "second", etc., is used herein merely to distinguish between the described objects and does not have any sequential or technical meaning. The terms "coupled" and "connected," as used herein, are intended to encompass both direct and indirect coupling (coupling), unless otherwise indicated.
As shown in fig. 1, the ultrasonic imaging apparatus provided by the present invention includes an ultrasonic probe 30, a transmission/reception control circuit 40 (i.e., a transmission circuit 410 and a reception circuit 420), a beam synthesis module 50, an IQ demodulation module 60, a processor 20, a man-machine interaction device 70, and a memory 80.
The ultrasonic probe 30 includes a transducer (not shown in the figure) composed of a plurality of array elements arranged in an array, the plurality of array elements being arranged in a row to form a linear array, or being arranged in a two-dimensional matrix to form an area array, the plurality of array elements may also form a convex array. The array element is used for transmitting ultrasonic waves according to the excitation electric signals or converting received ultrasonic waves into electric signals. Each array element can thus be used to achieve a mutual conversion of the electrical pulse signal and the ultrasound wave, so that the target tissue 10 transmits ultrasound waves, and also to receive echoes of ultrasound waves reflected back by the tissue. In performing ultrasonic detection, the transmitting circuit 410 and the receiving circuit 420 can control which array elements are used for transmitting ultrasonic waves and which array elements are used for receiving ultrasonic waves, or control the interval of the array elements to transmit ultrasonic waves or receive echoes of ultrasonic waves. The array elements participating in ultrasonic wave transmission can be excited by the electric signals at the same time, so that ultrasonic waves are transmitted at the same time; or the array elements participating in the ultrasonic wave transmission can be excited by a plurality of electric signals with a certain time interval, so that the ultrasonic wave with a certain time interval can be continuously transmitted.
The transmitting circuit 410 is configured to generate a transmitting sequence according to the control of the processor 20, where the transmitting sequence is configured to control some or all of the plurality of array elements to transmit ultrasonic waves to the target tissue, and the transmitting sequence parameters include an array element position for transmitting, an array element number, and an ultrasonic beam transmitting parameter (such as amplitude, frequency, number of transmitting times, transmitting interval, transmitting angle, waveform, focusing position, etc.). In some cases, the transmitting circuit 410 is further configured to delay the phases of the transmitted beams so that different transmitting elements transmit ultrasound waves at different times, so that each transmitting ultrasound beam can be focused at a predetermined region of interest. Different modes of operation, such as B-image mode, C-image mode, and D-image mode (doppler mode), the transmit sequence parameters may be different, and after the echo signals are received by the receive circuit 420 and processed by subsequent modules and corresponding algorithms, B-images reflecting tissue anatomy, C-images reflecting blood flow information, and D-images reflecting doppler spectrum images may be generated.
The receiving circuit 420 is used for receiving and processing the ultrasonic echo signals from the ultrasonic probe 30. The receive circuitry 420 may include one or more amplifiers, analog-to-digital converters (ADCs), and the like. The amplifier is used for amplifying the received echo signals after proper gain compensation, and the amplifier is used for sampling the analog echo signals at preset time intervals so as to convert the analog echo signals into digitized echo signals, wherein the digitized echo signals still retain amplitude information, frequency information and phase information. The data output by the receiving circuit 420 may be output to the beam forming module 50 for processing or output to the memory 80 for storage.
The beam forming module 50 is in signal connection with the receiving circuit 420, and is configured to perform corresponding beam forming processes such as delay and weighted summation on the echo signals, and because distances from the ultrasonic receiving points in the tissue to be measured to the receiving array elements are different, channel data of the same receiving point output by different receiving array elements have delay differences, delay processing is required to be performed, phases are aligned, and weighted summation is performed on different channel data of the same receiving point, so as to obtain beamformed ultrasonic image data, and the ultrasonic image data output by the beam forming module 50 is also referred to as radio frequency data (RF data). The beam forming module 50 outputs the radio frequency data to the IQ demodulation module 60. In some embodiments, the beam forming module 50 may also output the rf data to the memory 80 for buffering or saving, or directly output the rf data to the processor 20 for image processing.
The beam forming module 50 may perform the above functions in hardware, firmware, or software. The beam forming module 50 may be integrated in the processor 20 or may be separately provided, which is not limited by the present invention.
The IQ demodulation module 60 removes the signal carrier by IQ demodulation, extracts the tissue structure information contained in the signal, and performs filtering to remove noise, and the signal obtained at this time is referred to as a baseband signal (IQ data pair). The IQ demodulation module 60 outputs IQ data pairs to the processor 20 for image processing. In some embodiments, the IQ demodulation module 60 also outputs IQ data pairs to the memory 80 for buffering or saving so that the processor 20 reads the data from the memory 80 for subsequent image processing.
The IQ demodulation module 60 may also perform the above functions in hardware, firmware or software, and in some embodiments, the IQ demodulation module 60 may also be integrated with the beam forming module 50 in a single chip.
The processor 20 is configured to be a central controller Circuit (CPU), one or more microprocessors, graphics controller circuits (GPU) or any other electronic component capable of processing input data according to specific logic instructions, which may perform control of peripheral electronic components, or data reading and/or saving of memory 80, according to the input instructions or predetermined instructions, and may also perform processing of the input data by executing programs in the memory 80, such as one or more processing operations on the acquired ultrasound data according to one or more modes of operation, including but not limited to adjusting or defining the form of ultrasound emitted by the ultrasound probe 30, generating various image frames for display by a display of a subsequent human-machine interaction device 70, or adjusting or defining the content and form displayed on the display, or adjusting one or more image display settings (e.g., ultrasound images, interface components, locating regions of interest) displayed on the display.
The acquired ultrasound data may be processed by the processor 20 in real time during scanning as the echo signals are received, or may be temporarily stored on the memory 80 and processed in near real time in an on-line or off-line operation.
In this embodiment, the processor 20 controls the operation of the transmitting circuit 410 and the receiving circuit 420, for example, controls the transmitting circuit 410 and the receiving circuit 420 to operate alternately or simultaneously. The processor 20 may also determine an appropriate operation mode according to a user's selection or a program setting, form a transmission sequence corresponding to the current operation mode, and send the transmission sequence to the transmission circuit 410, so that the transmission circuit 410 controls the ultrasound probe 30 to transmit ultrasound waves using the appropriate transmission sequence.
The processor 20 is also operative to process the ultrasound data to generate a gray scale image of the signal intensity variations over the scan range reflecting the anatomy inside the tissue, referred to as the B image. The processor 20 may output the B-image to a display of the human interaction device 70 for display.
The man-machine interaction device 70 is used for man-machine interaction, such as outputting visual information and receiving user input. The input of the user can be received by a keyboard, an operation button, a mouse, a track ball, a touch pad and the like, and a touch screen integrated with a display can also be adopted; the output visual information can be a display.
The memory 80 is used to store the various types of data described above.
The ultrasonic imaging equipment provided by the invention can automatically acquire the standard section of the short axis and/or the long axis of the abdominal aorta, and automatically measure the inner diameter and automatically detect the abdominal aortic aneurysm and the interlayer aneurysm. The invention avoids a great deal of repeated labor, greatly optimizes the workflow of abdominal aortic examination, effectively improves the working efficiency, and can also improve the stability of measurement results of non-ultrasonic doctors such as emergency treatment, I CU and the like. The following is a description of some embodiments.
In one embodiment, the processor 20 is further configured to acquire an ultrasound image of a standard section of the abdominal aorta, wherein the ultrasound image of the standard section includes a long axis section image and a short axis section image. As shown in fig. 4, the long-axis section image of the abdominal aorta is an ultrasound image showing the longitudinal section of the abdominal aorta; as shown in fig. 2, the short-axis view image of the abdominal aorta is an ultrasound image showing the cross section of the abdominal aorta. The processor 20 may acquire ultrasound images of a standard section of the abdominal aorta from an external device or may be generated by the present ultrasound imaging device, the latter of which is illustrated as an example. And the doctor operates the ultrasonic probe to sweep the abdominal aorta according to a preset sweeping method to obtain an ultrasonic image containing the long-axis section of the abdominal aorta and an ultrasonic image containing the short-axis section of the abdominal aorta. For example, the physician places an ultrasound probe proximal to the abdominal aorta with the probe marker toward the right side of the patient. In this position, the celiac dry artery and superior mesenteric artery are typically observed, and when the aorta is observed, the probe is slid from above and below along the abdominal wall for continuous imaging, typically until the left and right common iliac arteries are seen at the distal end of the aorta. After the cross-section scan is completed, the probe is placed at the proximal end of the abdominal aorta and rotated 90 ° clockwise with the probe mark facing the patient's head side, and when the aorta is observed, an inner diameter measurement of the aorta can be performed. The longitudinal section scanning is performed at the distal end, and the steps are similar to those of the proximal end scanning, and are not repeated. Through the above scanning process on the major axis section and the minor axis section of the abdominal aorta, the processor 20 transmits ultrasonic waves through the ultrasonic probe during scanning, receives ultrasonic echoes, and further processes the ultrasonic echoes to obtain corresponding ultrasonic image data, where the ultrasonic image data may be ultrasonic dynamic video data, single-frame image data, and the like. The ultrasound image data may be two-dimensional or three-dimensional. The ultrasonic image data is obtained by scanning the abdominal aorta according to a preset scanning method based on an ultrasonic probe, and comprises a plurality of frames of ultrasonic images, wherein the long-axis section images and the short-axis section images are unknown to ultrasonic imaging equipment. The processor 20 may determine an ultrasound image of the standard section of the abdominal aorta from the ultrasound image data based on the operation of the user, or may automatically identify an ultrasound image of the standard section of the abdominal aorta from the ultrasound image data, the latter being exemplified in this embodiment. That is, in this embodiment, the user only needs to scan according to a preset scanning method, and the processor 20 can automatically identify the long-axis section image and the short-axis section image, where the identified long-axis section image may be one frame or multiple frames, and typically has multiple frames; the identified short-axis slice image may be one or more frames, typically multiple frames. The processor 20 automatically recognizes the long axis view and the short axis view in a variety of ways, two of which are illustrated below.
In one manner, the processor 20 may input ultrasound image data into a pre-trained deep learning model or machine learning model, from which the ultrasound image class of the ultrasound image in the ultrasound image data is output, such as whether the ultrasound image is a long axis facet image, a short axis facet image. The convolutional neural network may be constructed in advance. The neural network is composed of a series of convolution layers, pooling layers and full-connection layer stacks. And in the convolution layer, performing convolution operation in a mode that a convolution kernel slides on the image to extract the depth characteristics of the checked part or the current section. The feature extraction network comprises A l exNet, VGG, I ncept I on, resNet, denseNet and the like, and some light-weight networks comprise Mob I l eNet series, shuff l eNet series and the like. The pooling layer is used for reducing the dimension of the feature map of the checked part obtained by convolution to obtain more representative features, then the feature vector which can represent organs or sections is obtained through the full-connection layer or the global average pooling layer, and finally classification is carried out by using a classification function softmax and the like to obtain the category of the checked part, namely the section category of the ultrasonic image. The convolutional neural network working process is divided into two parts: training and testing. The training process requires training using a training set that includes all classes (labels) of the section of the examined region (e.g., abdominal aorta in this embodiment), each of which includes ultrasound images of different cases of the same section. The ultrasonic images with the labels are input into the network, and parameters in the deep learning model are adjusted in an iterative mode, so that the model can correctly output the probability of the category to which the training image belongs. After training the model, the processor 20 inputs the ultrasound image data into the model, which outputs which organ, which section of the part the ultrasound image in the ultrasound image data belongs to, in this embodiment, the model outputs whether each frame of ultrasound image belongs to a long-axis section image, a short-axis section image or other section images.
In another approach, the processor 20 may also determine which slice (major axis, minor axis, or other) the ultrasound image is based on a machine-learned classification method. For example, the processor 20 extracts image features of the ultrasonic image data by a conventional method such as PCA, LDA, HOG, harr, LBP, and classifies the image or video of the examination site using a classification algorithm mainly including KNN, SVM, decision tree, and the like. The principle of the KNN algorithm is to calculate the distance (including euclidean distance, hamming distance, etc.) between the optimal frame image of the ultrasonic image data and the training set image, and then select K images with the smallest distance, where the category with the largest occurrence number in the K images is the category (tangential plane) of the ultrasonic image data. The SVM classifier is mainly used for classifying problems, a hyper plane is trained by using a training set, images belonging to the training set class are arranged on one side of the hyper plane, and images not belonging to the training set class are arranged on the other layer of the hyper plane. When ultrasound image data is input to the classifier, the classifier determines whether the input image belongs to the training set class. Multiple classes (cut-planes) of classification can be achieved using multiple SVM classifiers. The decision tree is a process of simulating a person to make a decision by adopting a binary tree or multi-way tree, each part category of the training set can establish a tree model, each node of the tree corresponds to a feature, each time whether the newly input checked part image contains the feature is judged, the feature does not belong to the category, the feature is continuously judged to enter the next feature, and the feature belongs to the category.
The processor 20 automatically recognizes ultrasound images of the standard section of the abdominal aorta from the ultrasound image data using the methods described above.
Further, the processor 20 identifies the abdominal aortic region from the short-axis sectional image and measures the inner diameter thereof to obtain a first inner diameter value. There are a number of ways in which the processor 20 can identify the abdominal aortic region from the short-axis slice image, four of which are listed below.
The first automatic abdominal aorta recognition method based on the short-axis section image can be realized through deep learning target detection. Specifically, an ultrasound image database is constructed in advance, wherein each ultrasound image marks a region of interest of the abdominal aorta (abdominal aortic region), comprising: the presence or absence of the abdominal aorta, and, if present, the location of the region of interest. The deep learning target detection method can select RCNN (feature-level-rich structure extraction algorithm) model, fast RCNN model, fast-RCNN model, YOLO model, SSD model, ret i NANet model, efficientDet model, FCOS model and the like, and the model is trained by using the constructed ultrasonic image database to obtain a trained deep learning model. The processor 20 inputs the short-axis slice image into the model, which outputs the result of whether the abdominal aortic region is contained, and if so, the specific location of the abdominal aortic region.
The second is a method based on deep learning image segmentation. Specifically, a database of ultrasound images is pre-constructed, wherein each image marks a boundary region of interest of the abdominal aorta, comprising: the presence or absence of the abdominal aorta, if any, also includes a specific boundary range of the region of interest. The deep learning image segmentation method can select an FCN model, a Unet model, a SegNet model, a deep Lab model, a Mask RCNN model and the like, and uses the constructed ultrasonic image database to train the model to obtain a trained deep learning model. The processor 20 inputs the short-axis slice image into the model, which outputs the result of whether the abdominal aortic region is contained, and if so, the specific boundary range of the abdominal aortic region.
The third is a method based on machine learning object detection. Specifically, a group of candidate interested rectangular frame areas are obtained from the short-axis section image through a sliding window or se l ect ive search (selective search) algorithm; extracting the characteristics of each candidate frame region, wherein the characteristics can be extracted from PCA, LDA, HOG, harr, LBP, SI FT and other traditional characteristics, or from a neural network; and then matching the extracted features with features extracted from the marked region of interest in a preset image database, classifying by using a linear classifier, an SVM (support vector machine) or a simple neural network and the like, and determining whether the current candidate frame region comprises the abdominal aorta region or not.
The fourth is a machine learning image segmentation-based method. Specifically, pre-segmenting short-axis section images through image processing methods such as threshold segmentation, snake, level set, graphCut and the like, and obtaining a group of candidate target structure boundary ranges in the images; then, extracting the characteristics of the area surrounded by each candidate boundary range, wherein the characteristics can be extracted from the traditional characteristics such as PCA, LDA, HOG, harr, LBP, SI FT and the like, and can also be extracted from a neural network; and then matching the extracted features with features extracted from the marked boundary range in a preset database, classifying by using a linear classifier, an SVM (support vector machine) or a simple neural network and the like, and determining whether the current candidate boundary range contains the abdominal aorta region or not.
There are also various ways in which the processor 20 may measure the first inner diameter value of the abdominal aortic region in the short-axis slice image, and two are listed below.
One is based on the method of deep learning target detection or machine learning target detection described above, where the target detection result is obtained by taking the length or width of the region of interest thereof, and subtracting the threshold value as the inner diameter (first inner diameter value) thereof. The threshold is calculated from the average of all target detection results and the actual distance between the regions of interest.
The other is a method based on the deep learning image segmentation and the traditional machine learning image segmentation, the image segmentation result is obtained, the region of interest (abdominal aortic region) is taken, as shown in fig. 2, the maximum diameter d1 of the abdominal aortic region is measured as a first inner diameter value, or the longitudinal radial length d2 of the abdominal aortic region is measured as a first inner diameter value, or the perimeter c of the abdominal aortic region is measured, and then the inner diameter of the abdominal aortic region is calculated according to the perimeter c to obtain the first inner diameter value.
Processor 20 identifies the abdominal aortic region from the long axis slice image and measures its inner diameter to obtain a second inner diameter value. The processor 20 may train the deep learning model or the machine learning model in advance with the labeled long-axis tangential plane image as in the short-axis tangential plane image, input the obtained long-axis tangential plane image into the pre-trained deep learning model or machine learning model, and output the abdominal aorta region in the long-axis tangential plane image from the deep learning model or the machine learning model. The specific identification process is the same as that of the short-axis section image, and will not be described here.
There are also various ways in which the processor 20 may measure the abdominal aortic region in the long axis slice image to obtain its second internal diameter value, two of which are illustrated below.
One is based on the above deep learning and traditional machine learning image segmentation method: obtaining an image segmentation result, taking an interested region (an abdominal aorta region in a long-axis section image), and then sampling and measuring the inner diameter, wherein the specific sampling process is as follows: and selecting a plurality of sampling points along the lower edge (namely a blood vessel wall) of the region of interest, taking down the normal direction of the edge of each sampling point to obtain a radial line, wherein the length of the radial line in the region of interest is the inner diameter value corresponding to the sampling point, and taking the maximum value or the average value of the inner diameter values of the sampling points as the second inner diameter value of the long axis inner diameter of the abdominal aorta.
The other is a method based on the combination of the target detection and the traditional image processing: the target detection method of deep learning or traditional machine learning is used to locate the interested region of the long axis of the abdominal aorta, and then the traditional image processing method is used, such as: the method comprises the steps of obtaining an accurate interested range by using methods such as threshold segmentation, edge-based image segmentation, region-based image segmentation, graph theory-based image segmentation and the like, then carrying out sampling measurement on the inner diameter, wherein the sampling process is consistent with that in the method of using deep learning segmentation in the previous step, and the details are omitted. The maximum or average value of the inside diameter values of the respective sampling points may then be taken as the second inside diameter value of the major axis inside diameter of the abdominal aorta.
Of course, in some embodiments, the identification of the abdominal aorta may also be performed manually by a physician. That is, the processor 20 receives an area manually framed in the short-axis image by the user through the input device, and takes the area as an area of the abdominal aorta in the short-axis image, and then can identify, detect, measure the inside diameter, evaluate the risk, and the like of the abdominal aorta in the area. Similarly, the processor 20 receives the region manually framed in the long-axis section image by the user through the input device, and uses the region as the region of the abdominal aorta in the long-axis section image, and then can identify, detect, measure the inner diameter of the abdominal aorta in the region, evaluate the risk, and the like.
Typically, the ultrasound image of the standard section includes multiple frames of long axis section images, so the processor 20 may select the optimal frame from the multiple frames of long axis section images; the optimal frame can be selected based on the image quality of each frame long axis section image, for example, the image quality is compared based on one or more parameters of the definition, contrast and shape and position of the abdominal aorta area of each frame long axis section image, and then the long axis section image with the highest image quality is used as the optimal frame. Of course, the measured inner diameter value of the long axis section image of each frame can be compared with the second inner diameter value of the long axis section image of each frame to take the largest second inner diameter value in the long axis section image of each frame as the optimal frame in consideration of the fact that the deviation of the long axis mapping of the blood vessel occurs. The processor 20 then identifies the abdominal aortic region from the optimal frame and measures a second internal diameter value thereof, which results in a more accurate internal diameter on the long axis of the abdominal aorta. And the risk of the abdominal aortic aneurysm is judged by using the second inner diameter value and the first inner diameter value of the optimal frame, so that the effect is better.
The processor 20 selects the optimal frame from the multi-frame long-axis section image in various ways, such as screening by using a pre-trained model, calculating and judging by a preset formula, and the like, four ways are described below.
The first is a method based on deep learning image scoring. Specifically, an ultrasound image database is pre-constructed, and the ultrasound images in the database are marked with scoring values, wherein the higher the scoring value is, the better the quality of the ultrasound image is. The scoring score may be set by the physician, e.g., the higher the sharpness of the ultrasound image, the higher the contrast, the more complete the shape of the abdominal aortic region, the more appropriate the location, etc., indicating that the quality of the ultrasound image is higher, the physician may score the quality of the ultrasound image by integrating the sharpness, contrast, shape, location, etc. of the ultrasound image, thereby marking the score on the ultrasound image. The deep learning model can be constructed by selecting CNN models such as a Mobi eNet model, a VGG model, a ResNet model, an Al exNet model and the like. Training the model by using the constructed ultrasonic image database to obtain a trained deep learning model. The processor 20 inputs each frame of long axis section image into a deep learning model, the model outputs a scoring value of each frame of long axis section image, and whether one image belongs to the optimal section of the long axis of the abdominal aorta can be judged according to the height and the threshold value of the scoring value. For example, the processor 20 determines the long-axis slice image with the highest scoring score as the optimal frame.
The second method is based on the deep learning image distribution evaluation: and performing deep learning distribution evaluation on each frame in the multi-frame long-axis section image. Specifically, an ultrasound image database is pre-constructed, and the ultrasound image database comprises a plurality of long-axis section images, each long-axis section image has a score distribution formed by a plurality of scores, the score range can be 1-10, and each long-axis section image has a plurality of (e.g. 10) probabilities which are in one-to-one correspondence with the score ranges. Each score of the long-axis section image may correspond to an evaluation criterion, for example, multiple scores of the long-axis section image may be obtained by scoring the same long-axis section image by multiple doctors, or multiple scores may be obtained by scoring the same long-axis section image by multiple dimensions such as sharpness, contrast, shape and position of abdominal aortic region by the doctors. The deep learning model can be constructed by selecting CNN models such as a Mobi eNet model, a VGG model, a ResNet model, an Al exNet model and the like. Training the model by using the constructed ultrasonic image database, and obtaining a trained model after the training is finished. The processor 20 inputs each frame of long axis tangent plane image into a model, the model outputs a score distribution of each frame of long axis tangent plane image, and the optimal frame (optimal tangent plane) of the long axis tangent plane of the abdominal aorta can be judged according to some indexes such as mean value, variance, kurtosis, skewness and the like of each score distribution.
The third is based on conventional image quality evaluation methods, such as subjective image quality evaluation methods, machine learning image quality evaluation, and the like. Wherein: subjective evaluation is based on a person giving the image quality by a number of observers and then taking an average. The machine learning image quality evaluation can be further divided into: full reference, no reference, and partial reference image quality evaluations. The quality evaluation of the full reference image gives a standard reference image, then the distance/error (such as signal to noise ratio, mean square error, structural similarity and the like) between the image to be evaluated (long-axis section image) and the reference image is calculated, and the quality of the image to be evaluated can be obtained by analyzing the obtained error/distance. The semi-reference image quality evaluation is to obtain the quality of the image to be evaluated by only using the characteristic information of the image and comparing the key characteristic information between the reference image and the image to be evaluated. The non-reference image quality evaluation is to evaluate the image by only using the characteristics (variance, image entropy, spatial frequency, contrast and average gradient) of the image, and comprehensively analyze the image to obtain the quality of the image. The machine learning image quality evaluation method generally uses SVM to build a classification model to classify images, and then carries out regression on the image quality to be evaluated to obtain the quality value of the image to be measured. Finally, the processor 20 selects the long-axis section image with the highest quality, and the selection of the optimal frame is completed.
The fourth is a method based on an inside diameter maximum or an average inside diameter maximum. The processor 20 performs sampling measurement on the inner diameter of the abdominal aorta region in the multi-frame long-axis section image, namely the processor 20 identifies the abdominal aorta region from the multi-frame long-axis section image and measures the inner diameter of the abdominal aorta region to obtain a second inner diameter value; the sampling measurement process is consistent with the automatic measurement process of the long axis inner diameter (second inner diameter value) of the abdominal aorta, so that details are omitted, the second inner diameter values of the long axis section images of all frames are compared, the long axis section image with the largest second inner diameter value is taken as an optimal frame, the second inner diameter value of the optimal frame is taken as the second inner diameter value of the long axis section image to judge the risk of the abdominal aortic aneurysm, the second inner diameter value of the optimal frame is used for judging, and the result is more accurate. For the long-axis section image, the inner diameters of different positions of the abdominal aorta region can be measured to obtain a plurality of second inner diameter values, each second inner diameter value of the long-axis section image can be processed through a maximum function or an average function to obtain a maximum value or an average value of each second inner diameter value of the long-axis section image, and the maximum value or the average value is used as the second inner diameter value of the long-axis section image.
The processor 20 determines the risk of the abdominal aortic aneurysm according to the first inner diameter value and/or the second inner diameter value, and obtains a determination result. It is generally determined whether the first inner diameter value and/or the second inner diameter value exceeds a standard, which indicates that there is a risk of an abdominal aortic aneurysm, and if not, no risk. The specific methods can be various, and the following three methods are listed for explanation:
the first is in combination with an inside diameter threshold. The processor 20 determines whether the first inner diameter value and/or the second inner diameter value exceeds a preset inner diameter threshold, if one exceeds, a determination is made that an abdominal aortic aneurysm is likely to exist, and if not, it is indicated that there is no risk of an abdominal aortic aneurysm. In order to improve accuracy of risk determination of the abdominal aortic aneurysm, the first inner diameter value and the second inner diameter value are generally combined to determine, if one of the first inner diameter value and the second inner diameter value exceeds an inner diameter threshold value, the processor 20 obtains and outputs a determination result that the abdominal aortic aneurysm may exist, and if neither the first inner diameter value nor the second inner diameter value exceeds a preset inner diameter threshold value, no risk of the abdominal aortic aneurysm is indicated, and no indication is made. The specific process may be implemented in various ways, for example, there may be one or more frames of short-axis section images and long-axis section images acquired by the processor 20, that is, there may be one or more first inner diameter values and second inner diameter values, and if there are several first inner diameter values and several second inner diameter values, only if one inner diameter value exceeds the inner diameter threshold value, a determination result that there may be an abdominal aortic aneurysm may be obtained, or there may be an average or a maximum value of the first inner diameter values of the multi-frame short-axis section images, an average or a maximum value of the second inner diameter values of the multi-frame long-axis section images, and an average or a maximum value of the short-axis section images and long-axis section images may be obtained, if there is an average or a maximum value exceeding the inner diameter threshold value, a determination result that there may be an abdominal aortic aneurysm may be obtained. Further, the processor 20 outputs the judgment result, for example, displays a prompt message for prompting that an abdominal aortic aneurysm may exist through a display. If all the first inner diameter values and the second inner diameter values do not exceed the preset inner diameter threshold value, indicating that the risk of the abdominal aortic aneurysm is not caused, and not prompting; the method can also judge by using the average value or the maximum value of the short-axis section image and the long-axis section image, and if the average value or the maximum value of the short-axis section image and the long-axis section image does not exceed a preset inner diameter threshold, the risk of no abdominal aortic aneurysm is indicated, and no indication is provided. The inner diameter threshold may be set according to clinical experience, and may be 3.0cm, for example.
Second, the processor 20 calculates an inner diameter average value of the first inner diameter value and the second inner diameter value, determines whether the inner diameter average value is greater than a preset inner diameter threshold, if so, obtains a determination result that there is a possibility of an abdominal aortic aneurysm, otherwise, indicates that there is no risk of the abdominal aortic aneurysm. The physician may have one or more images of the short axis and long axis tangential planes obtained by the mapping operation, and one or more of the first inner diameter value and the second inner diameter value, and the processor 20 may calculate the average value of the inner diameters of all the first inner diameter values and the second inner diameter values, calculate the maximum value of all the first inner diameter values and the maximum value of the second inner diameter values, average the two maximum values to obtain the inner diameter average value, etc. The processor 20 outputs the judgment result, for example, displays a prompt message for prompting that an abdominal aortic aneurysm may exist through a display. If the average value of the inner diameters does not exceed the preset inner diameter threshold value, the risk of the abdominal aortic aneurysm is not indicated.
Third, the processor 20 selects an inside diameter maximum value dmax and an inside diameter minimum value dmi n from the first inside diameter value and the second inside diameter value; wherein the first inner diameter value may be one or more, and the second inner diameter value may be one or more. The processor 20 calculates a ratio p according to a preset formula p= (dmax-dmi n)/dmi n; judging whether the ratio p is larger than a preset value, and if so, obtaining a judging result that the abdominal aortic aneurysm possibly exists. Further, the processor 20 outputs the judgment result, for example, displays a prompt message for prompting that an abdominal aortic aneurysm may exist through a display. If p does not exceed the preset value, no risk of abdominal aortic aneurysm is indicated, and no indication is given. The preset value may be set according to clinical experience, for example, the preset value is 0.5.
Therefore, the invention can automatically identify the abdominal aortic aneurysm, thereby prompting doctors and improving the efficiency of abdominal aortic examination.
The processor 20 may also detect whether a dissection aneurysm exists in the short-axis view image, so as to obtain a detection result, and the detection result may be displayed through a display. For example, an ultrasound image database may be pre-constructed as in the method of identifying abdominal aortic regions described above, wherein ultrasound images with a dissection aneurysm label the region of interest of the dissection aneurysm and ultrasound images without a dissection aneurysm label the results without a dissection aneurysm. A deep learning model or machine learning model is trained with this ultrasound image database. After training the model, the processor 20 inputs the short-axis-facet image into a trained deep-learning model or machine-learning model, and the deep-learning model or machine-learning model outputs the result of whether the interlayer aneurysm exists in the short-axis-facet image, and if so, marks the interlayer aneurysm region. The specific method is the same as that of the above abdominal aorta identification, and the two methods are only different from the identification object and the corresponding mark (label), and are not described here. Therefore, the invention can automatically identify the interlayer aneurysm of the abdominal aorta, thereby prompting doctors and improving the efficiency of abdominal aorta examination.
The processor 20 may further integrate the determination result and the detection result to obtain a risk assessment result of the abdominal aorta, and display the risk assessment result through the man-machine interaction device. For example, if the risk assessment results in a risk level, and if the determination results in no risk of an abdominal aortic aneurysm, and the detection results in no interlayer aneurysm, the processor 20 determines that the risk level is low; if the determination result is that there is a risk of abdominal aortic aneurysm and the detection result is that there is no interlayer aneurysm, the processor 20 determines that the risk level is a medium level; if the determination result is that there is no risk of abdominal aortic aneurysm and the detection result is that there is a dissection aneurysm, the processor 20 determines that the risk level is a medium level; if the determination is that there is a risk of an abdominal aortic aneurysm and the detection is that there is a dissection aneurysm, the processor 20 determines that the risk level is high. Therefore, the risk assessment method can automatically carry out risk assessment on the abdominal aorta and prompt a doctor, and the efficiency of abdominal aorta examination is improved.
The ultrasound image of the standard slice comprises a short-axis slice image, which is typically multi-frame, i.e. the processor 20 acquires short-axis slice images at different times over a plurality of frames. In combination with the abdominal aorta scanning method, the multiple frames of short-axis facial images at different moments can be multiple frames of short-axis facial images at different moments obtained by scanning the ultrasonic probe along the long axis of the abdominal aorta. That is, the doctor operates the ultrasonic probe to scan along the long axis direction of the abdominal aorta, and the processor 20 obtains continuous multi-frame short axis section images at different times. Processor 20 identifies the abdominal aortic region from the multiple frames of short-axis slice images, respectively (the specific method is set forth above); the first inner diameters of the abdominal aortic region in the multiple frames of short axis slice images are measured separately to obtain a plurality of first inner diameter values (specific methods are set forth above). The processor 20 obtains the change trend of the first inner diameter along with time according to the moment corresponding to the multi-frame short-axis section image and the first inner diameter value; and displaying the change trend through a display of the man-machine interaction device. If the inside diameter of the blood vessel is enlarged due to the abnormality, a doctor can see the inside diameter according to the change trend, so that the image and the position of the section where the abnormality is located are found, and the working efficiency is improved.
In this embodiment, as shown in fig. 3, the trend of the change of the first inner diameter with time is a graph a of the change of the first inner diameter with time, and the graph a may be any form of graph, such as a line graph, a scatter plot, a line graph, or a histogram. Processor 20 also finds a maximum first inner diameter value from the plurality of first inner diameter values (e.g., by a maximum function); when the change curve graph is displayed through the display, the maximum first inner diameter value (shown by a dot in the figure) is marked on the change curve graph, and a short-axis section image (shown by an ultrasonic image at the uppermost layer in the figure) corresponding to the maximum first inner diameter value is displayed. Therefore, the change curve graph and the short-axis section image on the display interface are displayed in a linkage way, so that a doctor can check the inner diameter value and the corresponding ultrasonic image together. There are many ways to mark the maximum first inside diameter value, such as highlighting the point of the maximum first inside diameter value on the change curve, marking with a color, etc., and in any case, the maximum first inside diameter value may be highlighted to draw the attention of the user.
The processor 20 may also receive, via an input device of the human-computer interaction device, an instruction from a user to select a point in time on the variation graph a; the abscissa of any point on the change curve graph A represents a time point, so that a user can randomly perform clicking operation on the change curve graph A and send out an instruction for selecting the time point; in response to the instruction, the processor 20 displays, via the display, a short-axis-facet image corresponding to the selected point in time. The doctor can select an inner diameter value on the change curve graph A through a mouse or a track ball, and the ultrasonic image (short-axis section image) corresponding to the inner diameter value is displayed on the display interface, so that the method is very convenient. Likewise, the selected point in time may also be highlighted on the variation graph a.
When the selected time point is the time point corresponding to the maximum first inner diameter value, if the scanning of the long axis section of the abdominal aorta is completed at this time, the processor 20 can acquire the maximum second inner diameter value, which is the maximum value of the multiple second inner diameter values of the abdominal aorta region measured from the long axis section image, that is, the processor 20 can perform multiple second inner diameter value measurement on the long axis section image, and select the maximum value. Further, the processor 20 may display the maximum second inner diameter value in a linked manner through the display, for example, the maximum second inner diameter value may be displayed at a position adjacent to the selected time point in the variation graph a, or the maximum second inner diameter value may be displayed at another position of the display interface, so that the doctor can see the maximum first inner diameter value and the maximum second inner diameter value on the interface. Thus, the doctor can see the two largest inner diameter values to judge whether the abdominal aorta is abnormal or not.
The ultrasonic images can be displayed in a linked manner by selecting the time points, and the time points can be displayed in a linked manner by selecting the ultrasonic images. Specifically, the processor 20 displays a plurality of frames of short-axis facet images through the display, and receives an instruction of selecting one frame of short-axis facet image from the plurality of frames of short-axis facet images through the input device; in response to the instruction, a first inside diameter value corresponding to the selected short-axis-facet image is marked on the displayed variation graph. Thus, the user may select different frames of short axis view images by sliding the input device, such as a trackball, to view the inner diameter size at different points in time, while the corresponding region on the inner diameter change curve is highlighted (e.g., shown as highlighted). In addition, when the user slides to the maximum frame of the minor axis inner diameter, the maximum second inner diameter value of the punched major axis section (if the maximum second inner diameter value is obtained by mapping and processing before the maximum second inner diameter value) is automatically associated and displayed, so that the user can be assisted in analysis and comparison.
The processor 20 may further identify the abdominal aorta from the long axis section image (e.g., the optimal frame), and measure second inner diameters at a plurality of measurement points arranged along the long axis direction of the abdominal aorta on the abdominal aorta to obtain a plurality of second inner diameter values; obtaining the change trend of the second inner diameter along with the position according to the positions of the plurality of measuring points and the corresponding second inner diameter value; the trend of the change is displayed by a display. Thus, doctors can intuitively see which position of the abdominal aorta is thicker, whether abnormality exists or not, and the like.
As shown in fig. 4, the trend of the second inner diameter with respect to the position may be a graph B of the second inner diameter with respect to the position, and the graph B may be any form of graph, such as a line graph, a scatter graph, a line graph, or a histogram. The processor 20 is further configured to find a maximum second inner diameter value from the plurality of second inner diameter values; when the change curve graph B is displayed through the display, a long-axis section image is also displayed, and the position of a measuring point corresponding to the maximum second inner diameter value is marked on the displayed long-axis section image. The point of the maximum second inside diameter value on the variation graph B may also be highlighted for ease of viewing by the physician. In fig. 4, the measurement points on the variation graph B are marked with different colors, and the measurement points on the long-axis section image are also marked with different colors, so that the doctor can distinguish between them. The color of the same measuring point on the change curve graph B and the long axis section image is the same, so that a doctor can correspond the ultrasonic image and the inner diameter value on the change curve graph B.
In some embodiments, the long-axis section image is displayed as an optimal frame, and the change curve B may be displayed on the long-axis section image, typically in the non-abdominal aortic region of the long-axis section image, in order to facilitate viewing the image and the internal diameter of each position of the abdominal aorta simultaneously.
Likewise, the long axis slice image and the change curve B may be displayed in a linked manner. For example, the processor 20 also receives instructions from the user via the input device to select a location point on the abdominal aorta of the long axis section image; in response to the instruction, a second inner diameter value corresponding to the selected location point is marked on the displayed variation graph B. That is, when a doctor looks at the long-axis sectional image, he wants to know the inner diameter of the abdominal aorta at which position, and the inner diameter value can be seen from the change curve chart B at this position, which is very convenient. Further, the second inner diameter value corresponding to the selected position point on the variation graph B is also highlighted (e.g., highlighted). For example, the physician may slide through the trackball to see the size of the inner diameter at different long axis positions, while the corresponding highlighted area on the curve slides.
For another example, the processor 20 receives an instruction from the user to select a location point on the variation graph B through the input device; in response to the instruction, a selected location point is marked on the abdominal aorta of the displayed long axis section image. Therefore, the doctor can correspondingly check which position the second inner diameter value is, and can see on the long-axis section image only by selecting on the change curve chart B, so that the man-machine interaction efficiency is high.
Therefore, the doctor selects a position point of the abdominal aorta on the long-axis section image, the second inner diameter value of the position point on the change curve chart B is highlighted, the doctor selects a second inner diameter value on the change curve chart B, and the position point corresponding to the second inner diameter value on the long-axis section image is highlighted, so that the operation is very convenient.
When the selected position point is the position point corresponding to the maximum second inner diameter value, if the scanning of the short-axis section of the abdominal aorta is completed at this time, the processor 20 can acquire the maximum first inner diameter value, which is the maximum value of the first inner diameter values of the abdominal aorta area measured from the multi-frame short-axis section images, that is, the processor 20 can perform the first inner diameter value measurement on the multi-frame short-axis section images and select the maximum value. And the processor 20 is linked to display the maximum first inner diameter value through the display for the doctor to compare and reference. The linkage display may be performed by displaying the maximum first inside diameter value at a position adjacent to the selected position point in the change graph B, or by displaying the maximum first inside diameter value at a position adjacent to the selected position point in the long axis tangential plane image, or by displaying the maximum first inside diameter value at other positions of the display interface, so that the doctor can see the maximum first inside diameter value and the maximum second inside diameter value on the interface.
Based on the above ultrasonic imaging apparatus, the method for detecting abdominal aorta provided by the invention is shown in fig. 5, and comprises the following steps:
and step 1, scanning a short-axis surface of the abdominal aorta by the ultrasonic probe to obtain multi-frame short-axis surface images at different moments. For example, the ultrasonic probe moves along the major axis direction of the abdominal aorta under the control of the user to scan the minor axis surface of the abdominal aorta, and during the scanning process, the processor 20 processes the ultrasonic echo to obtain multi-frame minor axis surface images at different moments, and the specific process is described in detail in the above embodiments and is not described herein.
Step 2, the processor 20 identifies the abdominal aortic region from the short axis sectional image and measures a first internal diameter value thereof. The specific process is described in detail in the above embodiments, and will not be described here again
Step 3, the ultrasound probe scans the long-axis section of the abdominal aorta, and during the scanning process, the processor 20 processes the ultrasound echo to obtain a long-axis section image, and the specific process is described in detail in the above embodiments and is not described herein.
Step 4, the processor 20 identifies the abdominal aortic region from the long axis section image and measures a second internal diameter value thereof. The specific process is described in detail in the above embodiments, and will not be described here again.
And 5, the processor 20 judges the risk of the abdominal aortic aneurysm according to the first inner diameter value and the second inner diameter value, and the judging result is obtained and displayed. The specific process is described in detail in the above embodiments, and will not be described here again.
In some embodiments, as shown in fig. 6, the method for ultrasonic detection of abdominal aorta may also include the following steps:
step 1', the ultrasonic probe scans the short-axis surface of the abdominal aorta to obtain multi-frame short-axis surface images at different moments. For example, the ultrasound probe is moved in the long axis direction of the abdominal aorta under the control of the user to scan the short axis surface of the abdominal aorta, and during the scanning process, the processor 20 processes the ultrasound echo to obtain multi-frame short axis surface images at different times. The specific process is described in detail in the above embodiments, and will not be described here again.
Step 2', processor 20 identifies the abdominal aortic region from the multiple frames of short-axis-facet images at different times, respectively. The specific process is described in detail in the above embodiments, and will not be described here again.
Step 3', the processor 20 respectively measures the first inner diameters of the abdominal aorta regions in the short-axis section images of the multiple frames at different moments to obtain a plurality of first inner diameter values. The specific process is described in detail in the above embodiments, and will not be described here again.
And step 4', the processor 20 obtains and displays the time variation trend of the first inner diameter according to the corresponding time of the short-axis section images of the multiple frames at different times and the first inner diameter value. The specific process is described in detail in the above embodiments, and will not be described here again.
In some embodiments, as shown in fig. 7, the method for ultrasonic detection of abdominal aorta may also include the following steps:
step 1", the long-axis section of the abdominal aorta is scanned by the ultrasonic probe, and during the scanning process, the long-axis section image is obtained by processing the ultrasonic echo by the processor 20. The specific process is described in detail in the above embodiments, and will not be described here again.
The obtained long-axis section image can be provided with only one frame, and the subsequent step is to process the frame image. Of course, in some embodiments, the obtained long-axis section image may have multiple frames, and the processor 20 may select an optimal frame from the multiple frames of long-axis section images, and the subsequent steps process the optimal frame, and the specific process is described in detail in the above embodiments, which is not described herein.
Step 2", processor 20 identifies the abdominal aortic region from the long axis slice image. The specific process is described in detail in the above embodiments, and will not be described here again.
Step 3", the processor 20 measures a second inner diameter at a plurality of measurement points arranged along the long axis direction of the abdominal aorta on the abdominal aorta region, to obtain a plurality of second inner diameter values. The specific process is described in detail in the above embodiments, and will not be described here again.
And 4", the processor 20 obtains and displays the change trend of the second inner diameter along with the position according to the positions of the plurality of measuring points and the corresponding second inner diameter value. The specific process is described in detail in the above embodiments, and will not be described here again.
Reference is made to various exemplary embodiments herein. However, those skilled in the art will recognize that changes and modifications may be made to the exemplary embodiments without departing from the scope herein. For example, the various operational steps and components used to perform the operational steps may be implemented in different ways (e.g., one or more steps may be deleted, modified, or combined into other steps) depending on the particular application or taking into account any number of cost functions associated with the operation of the system.
Additionally, as will be appreciated by one of skill in the art, the principles herein may be reflected in a computer program product on a computer readable storage medium preloaded with computer readable program code. Any tangible, non-transitory computer readable storage medium may be used, including magnetic storage devices (hard disks, floppy disks, etc.), optical storage devices (CD-ROMs, DVDs, blu-Ray disks, etc.), flash memory, and/or the like. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including means which implement the function specified. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified.
While the principles herein have been shown in various embodiments, many modifications of structure, arrangement, proportions, elements, materials, and components, which are particularly adapted to specific environments and operative requirements, may be used without departing from the principles and scope of the present disclosure. The above modifications and other changes or modifications are intended to be included within the scope of this document.
The foregoing detailed description has been described with reference to various embodiments. However, those skilled in the art will recognize that various modifications and changes may be made without departing from the scope of the present disclosure. Accordingly, the present disclosure is to be considered as illustrative and not restrictive in character, and all such modifications are intended to be included within the scope thereof. Also, advantages, other advantages, and solutions to problems have been described above with regard to various embodiments. The benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, system, article, or apparatus. Furthermore, the term "couple" and any other variants thereof are used herein to refer to physical connections, electrical connections, magnetic connections, optical connections, communication connections, functional connections, and/or any other connection.
Those skilled in the art will recognize that many changes may be made to the details of the above-described embodiments without departing from the underlying principles of the invention. Accordingly, the scope of the invention should be determined from the following claims.

Claims (19)

1. An ultrasonic imaging apparatus, comprising:
an ultrasonic probe;
a transmission/reception control circuit for controlling the ultrasonic probe to transmit ultrasonic waves to the region of interest and to receive echoes of the ultrasonic waves;
the man-machine interaction device is used for performing visual output and receiving input of a user;
the processor is used for acquiring an ultrasonic image of a standard section of the abdominal aorta, wherein the ultrasonic image of the standard section comprises a long-axis section image and a short-axis section image; identifying an abdominal aortic region from the short-axis section image and measuring to obtain a first inner diameter value of the abdominal aortic region; identifying an abdominal aorta region from the long-axis section image and measuring to obtain a second inner diameter value of the abdominal aorta region; and judging the risk of the abdominal aortic aneurysm according to the first inner diameter value and the second inner diameter value, and obtaining a judging result.
2. An ultrasonic imaging apparatus, comprising:
an ultrasonic probe;
a transmission/reception control circuit for controlling the ultrasonic probe to transmit ultrasonic waves to the region of interest and to receive echoes of the ultrasonic waves;
The man-machine interaction device is used for performing visual output and receiving input of a user;
the processor is used for acquiring ultrasonic images of a standard section of the abdominal aorta, wherein the ultrasonic images of the standard section comprise short-axis section images of a plurality of frames at different moments; respectively identifying abdominal aorta areas from the short-axis section images of the multiple frames at different moments; measuring first inner diameters of abdominal aorta areas in the multi-frame short-axis-section images at different moments respectively to obtain a plurality of first inner diameter values; obtaining a change trend of the first inner diameter along with time according to the plurality of first inner diameter values corresponding to the multi-frame short-axis section images at different moments; and displaying the change trend through the man-machine interaction device.
3. An ultrasonic imaging apparatus, comprising:
an ultrasonic probe;
a transmission/reception control circuit for controlling the ultrasonic probe to transmit ultrasonic waves to the region of interest and to receive echoes of the ultrasonic waves;
the man-machine interaction device is used for performing visual output and receiving input of a user;
the processor is used for acquiring an ultrasonic image of a standard section of the abdominal aorta, wherein the ultrasonic image of the standard section comprises a long-axis section image; identifying an abdominal aorta region from the long-axis section image, and measuring second inner diameters at a plurality of measuring points arranged along the long axis direction of the abdominal aorta on the abdominal aorta region to obtain a plurality of second inner diameter values; obtaining the change trend of the second inner diameter along with the position according to the positions of the plurality of measuring points and the corresponding second inner diameter value; and displaying the change trend through the man-machine interaction device.
4. The ultrasound imaging apparatus of claim 1, wherein the processor is further configured to:
and detecting whether the interlayer aneurysm exists in the short-axis surface image by using a pre-trained model, and obtaining a detection result.
5. The ultrasound imaging apparatus of claim 4, wherein the processor is further configured to:
and combining the judging result and the detecting result to obtain a risk assessment result of the abdominal aorta, and displaying the risk assessment result through the man-machine interaction device.
6. The ultrasound imaging apparatus of claim 1, wherein the ultrasound image of the standard slice comprises a short-axis slice image that is a multi-frame short-axis slice image at different times; the processor is further configured to:
respectively identifying abdominal aorta areas from the short-axis section images of the multiple frames at different moments; measuring first inner diameters of abdominal aorta areas in the multi-frame short-axis-section images at different moments respectively to obtain a plurality of first inner diameter values; obtaining a change trend of the first inner diameter along with time according to the plurality of first inner diameter values corresponding to the multi-frame short-axis section images at different moments; and displaying the change trend through the man-machine interaction device.
7. The ultrasound imaging apparatus of claim 2 or 6, wherein the trend of the first inner diameter over time is a plot of the first inner diameter over time, the processor further configured to:
finding a maximum first inner diameter value from the plurality of first inner diameter values;
when the human-computer interaction device displays the change curve graph, marking the maximum first inner diameter value on the change curve graph, and displaying a short-axis section image corresponding to the maximum first inner diameter value.
8. The ultrasound imaging apparatus of claim 2, 6 or 7, wherein the trend of the first inner diameter over time is a plot of the first inner diameter over time, the processor further configured to:
receiving an instruction of a user for selecting a time point on the change curve chart through the man-machine interaction device; responding to the instruction, and displaying a short-axis section image corresponding to the selected time point through the man-machine interaction device; or alternatively, the process may be performed,
displaying the multi-frame short-axis facet images at different moments through the man-machine interaction device, and receiving an instruction of selecting one frame of short-axis facet image from the multi-frame short-axis facet images at different moments through the man-machine interaction device; in response to the instructions, a first inside diameter value of a first inside diameter of the abdominal aorta in the selected short-axis-facet image is marked on the displayed variation graph.
9. The ultrasound imaging apparatus of claim 8, wherein the processor is further configured to: when the selected time point is the time point corresponding to the maximum first inner diameter value, acquiring the maximum second inner diameter value and displaying in parallel; the maximum second inside diameter value is the maximum value of a plurality of second inside diameter values of the abdominal aorta measured from the long axis section image.
10. The ultrasound imaging apparatus of claim 1, wherein after identifying an abdominal aortic region from the long axis slice image, the processor is further configured to:
measuring second inner diameters at a plurality of measuring points arranged along the long axis direction of the abdominal aorta on the abdominal aorta region to obtain a plurality of second inner diameter values; obtaining the change trend of the second inner diameter along with the position according to the positions of the plurality of measuring points and the corresponding plurality of second inner diameter values; and displaying the change trend through the man-machine interaction device.
11. The ultrasound imaging apparatus of claim 1, wherein the ultrasound image of the standard section comprises a long axis section image of a plurality of frames; the processor identifies an abdominal aortic region from the long axis section image and measures a second internal diameter value thereof, comprising:
Selecting an optimal frame from the multi-frame long-axis section image by using a pre-trained model; identifying the abdominal aorta from the optimal frame and measuring to obtain a second inner diameter value of the abdominal aorta; or alternatively, the process may be performed,
identifying an abdominal aorta area from the multi-frame long-axis section image and measuring to obtain a second inner diameter value of the abdominal aorta area; and comparing the second inner diameter values of the multi-frame long-axis section images to obtain the maximum second inner diameter value.
12. The ultrasound imaging apparatus of claim 3 or 10, wherein the trend of the second inner diameter with position is a graph of the second inner diameter with position, the processor further configured to:
finding a maximum second inner diameter value from the plurality of second inner diameter values;
and when the change curve graph is displayed through the man-machine interaction device, the long-axis tangent plane image is also displayed, and the position of the measuring point corresponding to the maximum second inner diameter value is marked on the displayed long-axis tangent plane image.
13. The ultrasound imaging apparatus of claim 12, wherein the processor is further configured to:
receiving an instruction of selecting a position point on the abdominal aorta area of the long-axis section image by a user through the man-machine interaction device; in response to the instruction, marking a second inner diameter value corresponding to a selected location point on the displayed variation graph; or alternatively, the process may be performed,
Receiving an instruction of selecting a position point on the change curve graph by a user through the man-machine interaction device; in response to the instruction, the location point is marked on the abdominal aorta region of the long axis section image displayed.
14. The ultrasound imaging apparatus of claim 13, wherein the processor is further configured to: when the selected position point is the position point corresponding to the maximum second inner diameter value, acquiring the maximum first inner diameter value and displaying in parallel; the maximum first internal diameter value is the maximum value of the first internal diameter values of the abdominal aorta measured from a plurality of frames of short-axis section images.
15. The ultrasound imaging apparatus of claim 1, wherein the processor determines a risk of an abdominal aortic aneurysm based on the first inner diameter value and the second inner diameter value, the determination comprising:
judging whether the first inner diameter value and/or the second inner diameter value is larger than a preset inner diameter threshold value, if so, obtaining a judging result that abdominal aortic aneurysm possibly exists; or alternatively, the process may be performed,
calculating to obtain an inner diameter average value of the first inner diameter value and the second inner diameter value, judging whether the inner diameter average value is larger than a preset inner diameter threshold value, and if so, obtaining a judging result that abdominal aortic aneurysm possibly exists; or alternatively, the process may be performed,
Selecting an inner diameter maximum value dmax and an inner diameter minimum value dmin from the first inner diameter value and the second inner diameter value; calculating according to a preset formula p= (dmax-dmin)/dmin to obtain a ratio p; judging whether the ratio p is larger than a preset value, and if so, obtaining a judging result that the abdominal aortic aneurysm possibly exists.
16. An ultrasonic imaging apparatus, comprising:
an ultrasonic probe;
a transmission/reception control circuit for controlling the ultrasonic probe to transmit ultrasonic waves to the region of interest and to receive echoes of the ultrasonic waves;
the man-machine interaction device is used for performing visual output and receiving input of a user;
the processor is used for acquiring a long-axis section image or a short-axis section image of the abdominal aorta; identifying an abdominal aortic region from the long-axis section image or the short-axis section image and measuring to obtain an inner diameter value of the abdominal aortic region; and judging the risk of the abdominal aortic aneurysm according to the inner diameter value to obtain a judging result.
17. An ultrasonic testing method of abdominal aorta, comprising:
scanning the short-axis surface of the abdominal aorta by the ultrasonic probe to obtain multi-frame short-axis surface images at different moments;
identifying an abdominal aortic value from the short-axis section image and measuring to obtain a first internal diameter value of the abdominal aortic value;
The ultrasonic probe scans the long-axis section of the abdominal aorta to obtain a long-axis section image;
identifying an abdominal aorta region from the long-axis section image and measuring to obtain a second inner diameter value of the abdominal aorta region;
and judging the risk of the abdominal aortic aneurysm according to the first inner diameter value and the second inner diameter value, and obtaining and displaying a judging result.
18. An ultrasonic testing method of abdominal aorta, comprising:
scanning the short-axis surface of the abdominal aorta by the ultrasonic probe to obtain multi-frame short-axis surface images at different moments;
respectively identifying abdominal aorta areas from the short-axis section images of the multiple frames at different moments;
measuring first inner diameters of abdominal aorta areas in the multi-frame short-axis-section images at different moments respectively to obtain a plurality of first inner diameter values;
and obtaining and displaying the time-dependent change trend of the first inner diameter according to the corresponding time of the short-axis section images of the multiple frames at different time and the first inner diameter value.
19. An ultrasonic testing method of abdominal aorta, comprising:
the ultrasonic probe scans the long-axis section of the abdominal aorta to obtain a long-axis section image;
identifying an abdominal aortic region from the long axis slice image;
Measuring second inner diameters at a plurality of measuring points arranged along the long axis direction of the abdominal aorta on the abdominal aorta to obtain a plurality of second inner diameter values;
and obtaining and displaying the change trend of the second inner diameter along with the position according to the positions of the plurality of measuring points and the corresponding second inner diameter value.
CN202211469392.7A 2021-11-22 2022-11-22 Ultrasonic imaging equipment and ultrasonic detection method of abdominal aorta Pending CN116138807A (en)

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CN202111386552 2021-11-22

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