Ultrasonic determination of dynamic air bronchograms and associated devices, systems and methods

Abstract

Ultrasound imaging devices, systems, and methods are provided. In one embodiment, the ultrasound imaging system includes an interface coupled to an ultrasound imaging component and configured to receive a plurality of image data frames representing a body of a subject, the body of the subject including at least a portion of a lung; a processing component in communication with the interface and configured to determine a metric for each of the plurality of image data frames based on a threshold comparison; and determine a dynamic air bronchogram (AB) condition of the subject's body based on changes between the metrics of the plurality of image data frames. In one embodiment, the processing component is configured to determine a differential data frame based on differences between successive image data frames in the plurality of image data frames; and determine a dynamic AB condition of the subject's body based on the differential data frame.

Description

[0001] Related Applications

[0002] This application claims priority to U.S. Provisional Application No. 62 / 238,361, filed on February 9, 2018, and International Application No. PCT / CN2017 / 097624, filed on August 16, 2017, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates generally to ultrasound imaging, and in particular, to providing automated systems and methods for identifying the presence of dynamic air bronchograms (ABs) indicative of pneumonia based on lung ultrasound imaging. Background Art

[0004] Pneumonia (PN) is a common disease worldwide, with approximately 2 to 3 million cases diagnosed each year in the United States. Symptoms of PN may include high fever, cough, shortness of breath, chest pain, and / or other respiratory-related symptoms. Physical examination (e.g., listening to lung sounds over the chest) may not be effective or reliable in detecting PN in the early stages. One of the commonly used techniques for PN diagnosis is chest radiography (CXR). However, bedside CXR may provide limited image quality. In addition, CXR is a time-consuming procedure for emergency situations, and the interpretation of bedside CXR may be challenging, requiring extensive radiology experience to avoid misinterpretation of a variety of pleural and lung diseases. In addition, the final results may vary greatly between radiologists. An improved method for diagnosing PN compared to bedside CXR is to use chest computed tomography (CT) imaging. However, CT imaging is expensive and may have a higher radiation exposure than CXR. Therefore, CT imaging may not be suitable for routine diagnosis in the emergency department, intensive care unit (CCU), or intensive care unit (ICU), especially for young children and pregnant women.

[0005] Ultrasound imaging, especially bedside point-of-care ultrasound (POC-US), has gained popularity in ICUs and emergency settings for various types of diagnostics. Recent studies have shown that lung ultrasound imaging can be useful and effective in diagnosing PN with relatively high accuracy. For example, for normal aerated lungs, ultrasound images may not show useful or sufficient information, while lung consolidation may appear as bright spots or bright structures under ultrasound imaging. In addition, the appearance of bright (B) lines in ultrasound images can indicate PN at an early stage. Therefore, POC lung ultrasound imaging may be useful and attractive for PN diagnosis. However, PN diagnosis based on ultrasound imaging may require trained or experienced physicians or clinicians to analyze and interpret the acquired lung images. Currently, there are no effective tools to guide inexperienced users in screening and diagnosis of PN. Summary of the invention

[0006] Although existing ultrasonic lung imaging has been proven to be useful for PN diagnosis, there is still a clinical need for improved systems and techniques to provide low-cost and easy-to-interpret PN diagnostic results. Embodiments of the present disclosure provide a mechanism for diagnosing PN by identifying and indicating the presence of a dynamic air bronchogram (AB) in an automated manner based on lung ultrasound images. In lung ultrasound imaging, a dynamic AB corresponds to a bright spot or pixel that changes or moves over time due to the respiratory cycle. In one embodiment, a dynamic AB is identified based on changes over time in a number of bright spots or pixels across several image frames. In another embodiment, a dynamic AB is identified based on changes over time in the temporal intensity of bright spots or pixels across several image frames. In yet another embodiment, the appearance of the bronchial tree corresponding to the dynamic AB is enhanced in the lung ultrasound image by accumulating differences across image data frames over time.

[0007] In one embodiment, an ultrasound imaging system is provided. The ultrasound imaging system includes an interface coupled to an ultrasound imaging component and configured to receive a plurality of image data frames representing a body of a subject, the body of the subject including at least a portion of a lung. A processing component in communication with the interface and configured to determine a metric for each of the plurality of image data frames based on a threshold comparison; and determine a dynamic air bronchogram (AB) condition of the body of the subject based on a change between the metrics of the plurality of image data frames.

[0008] In some embodiments, each image data frame includes a plurality of pixel values ​​representing pixel intensities of an image of a body of an object. In some embodiments, the plurality of image data frames represent an image of a body of an object over a time period including at least one respiratory cycle. In some embodiments, the processing component is configured to determine the metric for each of the plurality of image data frames by determining the number of a plurality of pixel values ​​that meet a threshold in each image data frame. In some embodiments, the processing component is configured to determine the dynamic AB condition by determining a ratio between a maximum metric and a minimum metric; and determine the dynamic AB condition based on the ratio. In some embodiments, the processing component is further configured to identify a region of interest (ROI) corresponding to at least a portion of a lung from the plurality of image data frames. And the metric is determined based on the ROI. In some embodiments, the ultrasound imaging system further includes a display component configured to display a result of the dynamic AB condition. In some embodiments, the ultrasound imaging system further includes: an ultrasound imaging probe including an ultrasound imaging component; a processing component; and a display component configured to display a result of the dynamic AB condition.

[0009] In one embodiment, an ultrasound imaging system is provided. The ultrasound imaging system includes an interface coupled to an ultrasound imaging component and configured to receive a plurality of image data frames representing a body of a subject, the body of the subject including at least a portion of a lung; a processing component in communication with the interface and configured to determine a differential data frame based on differences between successive image data frames in the plurality of image data frames; determine a cumulative data frame based on a sum of the differential data frames; and determine a dynamic air bronchogram (AB) condition of the body of the subject based on the cumulative data frame.

[0010] In some embodiments, the plurality of image data frames represent an image of a body of a subject over a time period that includes at least one respiratory cycle. In some embodiments, each image data frame includes a plurality of pixel values ​​representing pixel intensities of the image of the body of the subject. In some embodiments, each differential data frame includes a plurality of difference values, wherein the processing component is further configured to determine each differential data frame by determining each difference value in the plurality of difference values, wherein each difference value in the plurality of difference values ​​is determined by determining an absolute difference between a pixel value of a first data frame in the plurality of image data frames and a pixel value of a second data frame in the plurality of image data frames. The first data frame is adjacent to the second data frame, wherein the pixel values ​​of the first data frame and the pixel values ​​of the second data frame represent the same sub-portion of at least a portion of a lung. In some embodiments, each differential data frame includes a first plurality of difference values, wherein the accumulated data frame includes a plurality of summed values, and wherein the processing component is further configured to determine the dynamic AB condition by determining each summed value in the plurality of summed values, each summed value in the plurality of summed values ​​being determined by accumulating a second plurality of pixel values ​​across the differential data frame, wherein the second plurality of pixel values ​​across the differential data frame represent the same portion of the subject's body; and determining the dynamic AB condition based on the plurality of summed values. In some embodiments, the ultrasound imaging system of claim 9 further includes: a display component configured to display the accumulated data frame.

[0011] In one embodiment, an ultrasound imaging system is provided. The ultrasound imaging system includes an interface coupled to an ultrasound imaging component and configured to receive a plurality of image data frames representing a body of a subject, the body of the subject including at least a portion of a lung; a processing component in communication with the interface and configured to identify a subset of data from the plurality of image data frames based on a threshold comparison; and determine a dynamic air bronchogram (AB) condition of the body of the subject based on a temporal change in the subset of data.

[0012] In some embodiments, each of the plurality of image data frames includes a plurality of pixel values ​​representing pixel intensities of an image of the subject's body. In some embodiments, the plurality of image data frames represent an image of the subject's body over a time period including at least one respiratory cycle. In some embodiments, the processing component is configured to identify the subset of data by selecting one or more pixel values ​​corresponding to the same sub-portion of the at least part of the lung and satisfying a threshold from each of the plurality of image data frames; determining a first value for each of the plurality of image data frames based on one or more pixel values ​​of the corresponding image data frame; and determining the dynamic AB condition based on a ratio between a maximum value of the first value and a minimum value of the first value. In some embodiments, the processing component is configured to apply a filter on the first value before determining the dynamic AB condition. In some embodiments, the ultrasound imaging system further comprises a display component configured to display a result of the dynamic AB condition.

[0013] In one embodiment, a method of ultrasound imaging diagnosis is provided. The method includes: receiving a plurality of image data frames associated with a subject's body including at least a portion of a lung from an ultrasound imaging probe; determining a first value for each of the plurality of image data frames based on a threshold comparison; and determining a dynamic air bronchogram (AB) condition of the subject's body based on changes between the first values ​​of the plurality of image data frames.

[0014] In some embodiments, each image data frame includes a plurality of pixel values ​​representing pixel intensities of an image of a body of a subject. In some embodiments, the plurality of image data frames represent an image of a body of a subject over a time period including at least one respiratory cycle. In some embodiments, the determination of the first value includes determining the number of pixel values ​​in each image data frame that meet a threshold, wherein determining the dynamic AB condition includes determining a ratio between a maximum value of the first value and a minimum value of the first value; and determining the dynamic AB condition based on the ratio. In some embodiments, the method further includes identifying a region of interest (ROI) corresponding to at least a portion of the lung from the plurality of image data frames. And determining the first value based on the ROI. In some embodiments, the method further includes displaying a result of the dynamic AB condition at a display component.

[0015] Additional aspects, features and advantages of the present disclosure will become apparent from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Illustrative embodiments of the present disclosure will be described with reference to the accompanying drawings, in which:

[0017] Figure 1 is a schematic diagram of an ultrasound imaging system according to aspects of the present disclosure.

[0018] Figure 2 The dynamics of AB from the exhalation period to the inspiration period are illustrated according to aspects of the present disclosure.

[0019] Figure 3 The dynamics of AB in areas of lung consolidation over time are illustrated in accordance with aspects of the present disclosure.

[0020] Figure 4 is a schematic diagram illustrating a dynamic AB detection scheme according to aspects of the present disclosure.

[0021] Figure 5 is an image frame illustrating a dynamic AB according to aspects of the present disclosure.

[0022] Figure 6 is a graph illustrating the change in several bright pixels over time across several image frames in the presence of dynamic AB according to aspects of the present disclosure.

[0023] Figure 7 is an image frame illustrating a static AB according to aspects of the present disclosure.

[0024] Figure 8 is a graph illustrating the change over time of several bright pixels across several image frames without dynamic AB, according to aspects of the present disclosure.

[0025] Fig. 9 is a schematic diagram illustrating a dynamic AB detection scheme according to aspects of the present disclosure.

[0026] Fig.10 is a schematic diagram illustrating a bronchial tree enhancement scheme according to aspects of the present disclosure.

[0027] Fig.11A is an image frame illustrating a bronchial tree at a moment in time according to aspects of the present disclosure.

[0028] Fig. 11B is an image frame illustrating a bronchial tree at another time instant in accordance with aspects of the present disclosure.

[0029] Fig. 11C is a differential image frame illustrating the difference between a pair of consecutive image frames including a bronchial tree according to aspects of the present disclosure.

[0030] Fig.11D is a cumulative image frame illustrating an enhanced bronchial tree according to aspects of the present disclosure.

[0031] Fig.12is a graph illustrating the change over time of several bright spots across several image frames in the presence of dynamic AB according to aspects of the present disclosure.

[0032] Fig.13 is a flow chart of a dynamic AB detection method according to aspects of the present disclosure.

[0033] Fig.14 is a flow chart of a dynamic AB detection method according to aspects of the present disclosure.

[0034] Fig.15 is a flow chart of a bronchial tree enhancement method according to aspects of the present disclosure. DETAILED DESCRIPTION

[0035] For the purpose of promoting understanding of the principles of the present disclosure, reference will now be made to the embodiments illustrated in the accompanying drawings, and specific language will be used to describe them. However, it should be understood that it is not intended to limit the scope of the present disclosure. As will be normally understood by those skilled in the art involved in the present disclosure, any changes and further modifications to the described equipment, systems and methods and any other application of the present disclosure are fully contemplated and included in the present disclosure. In particular, it is fully contemplated that the features, components and / or steps described about an embodiment can be combined with the features, components and / or steps described about other embodiments of the present disclosure. However, a large number of iterations of these combinations will not be described separately for the purpose of brevity.

[0036] Figure 1 1 is a schematic diagram of an ultrasound imaging system 100 according to aspects of the present disclosure. The system 100 is used to scan a region or volume of a patient's body. The system 100 includes an ultrasound imaging probe 110 that communicates with a host computer 130 via a communication interface or link 120. The probe 110 includes a transducer array 112, a beamformer 114, a processing component 116, a display 117, and a communication interface 118. The host computer 130 includes a display 132, a communication interface 136, and a communication interface 136.

[0037] The transducer array 112 transmits an ultrasonic signal toward the anatomical object 105 and receives an echo signal reflected from the object 105 back to the transducer array 112. The transducer array 112 may include acoustic elements arranged in a one-dimensional (1D) array or a two-dimensional (2D) array. The beamformer 114 is coupled to the transducer array 112. The beamformer 114 controls the transducer array 112, for example, for the transmission of ultrasonic signals and the reception of ultrasonic echo signals. The beamformer 114 provides an image signal to the processing component 116 based on the response or the received ultrasonic echo signal. The beamformer 114 may include multiple stages of beamforming. Beamforming can reduce the number of signal lines for coupling to the processing component 116. In some embodiments, the combination of the transducer array 112 and the beamformer 114 can be referred to as an ultrasonic imaging component.

[0038] The processing component 116 is coupled to the beamformer 114. The processing component 116 generates image data based on the image signal. The processing component 116 can be implemented as a combination of software components and hardware components. In one embodiment, the processing component 116 can be implemented on a field programmable gate array (FPGA) and can include a programmable state machine to control the processing and conversion of the image signal to image data. For example, the processing component 116 can perform filtering and / or orthogonal demodulation to adjust the image signal. The processing component 116 can perform analysis detection on the filtered signal. The display 117 is coupled to the processing component 116. The display 132 can be a screen or any suitable display integrated with the housing of the probe 110. The display 117 can be configured to display the results of the analysis detection.

[0039] The communication interface 118 is coupled to the processing component 116. The communication interface 118 sends the image signal to the host 130 via the communication link 120. At the host 130, the communication interface 136 can receive the image signal. The host 130 can be any suitable computing and display device, such as a workstation, a personal computer (PC), a laptop computer, a tablet computer, or a mobile phone. The communication link 120 can be any suitable communication link. For example, the communication link 120 can be a wired link, such as a universal serial bus (USB) link or an Ethernet link. Alternatively, the communication link 120 can be a wireless link, such as an ultra-wideband (UWB) link, an Institute of Electrical and Electronics Engineers (IEEE) 802.11 WiFi link, or a Bluetooth link.

[0040] The processing component 134 is coupled to the communication interface 136. The processing component 134 can be implemented as a combination of software components and hardware components. The processing component 134 may include a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a controller, an FPGA device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein. The processing component 134 may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessor cores in combination with a DSP, or any other such configuration. The processing component 134 may be configured to perform image processing and image analysis for various diagnostic modalities. The display 132 is coupled to the processing component 134. The display 132 may be a monitor or any suitable display. The display 132 is configured to display images and / or diagnostic results processed by the processing component 134.

[0041] System 100 can be configured for dynamic AB detection. For example, object 105 can correspond to a part of a patient's body, and the part of the body includes at least a part of the patient's lung. In one embodiment, probe 110 can transmit image signals (e.g., echo signals) received from transducer array 112 to host 130. Processing component 134 can detect dynamic AB from ultrasound images and indicate positive dynamic AB detection and the position of the detected dynamic AB or negative dynamic AB detection on display 132. Processing component 134 can identify dynamic AB based on the change of multiple bright spots or pixels over time across multiple image frames. Alternatively, processing component 134 can identify dynamic AB based on the change of the time intensity of bright spots or pixels across several image frames over time. Processing component 134 can enhance the appearance or visibility of bronchial trees in ultrasound images. This article describes in more detail the mechanism for detecting dynamic AB and enhancing bronchial trees. In another embodiment, processing component 116 on probe 110 can be configured to perform dynamic AB detection without the need for a host. In such an embodiment, the dynamic AB detection result can be displayed on the overall display 117.

[0042] One of the ultrasound imaging signals used to identify PN is the detection of positive dynamic AB within lung consolidation. AB is the tubular outline of the airways visible under lung ultrasound imaging due to the filling of the surrounding alveoli by fluid or inflammatory exudate. Some studies have shown that in clinical practice, the combination of lung consolidation with dynamic AB accounts for about 70 percent (%) of the total PN cases.

[0043] Figure 2 The dynamics of AB from the exhalation period to the inspiration period are illustrated according to aspects of the present disclosure. Figure 2, image 210 is acquired during an exhalation period of a patient, and image 220 is acquired during an inhalation period of the patient. As shown in image 210, AB 212 appears as a tubular bright structure. Similarly, in image 220, AB 214 appears as a tubular bright structure. As can be observed, the appearance of AB (e.g., bright structure) changes over time, for example, from AB 212 during the exhalation period to AB 214 during the inhalation period. In addition, images 210 and 220 show the movement of dot-like AB 216 (e.g., bright spot) to dot-like AB 218.

[0044] Figure 3 The dynamics of AB in a region of lung consolidation over time are illustrated in accordance with aspects of the present disclosure. Figure 3 , the x-axis represents time in a certain constant unit and the y-axis represents depth in a certain constant unit. Image 300 illustrates a real-time ultrasound scan line as a function of time. For example, the transducer array 112 is used to generate a beam that is directed to an area within a lung consolidation along the scan line and is repeated at a certain time interval. Image 300 shows the echo signal corresponding to the transmitted beam received from the transducer array 112. In image 300, the bright cyclic pattern lines show the movement or displacement of ABs such as AB 212, 214, 216 and 218 in a periodic manner. The cyclic or periodic pattern corresponds to the expiratory period 310 and the inspiratory period 320 marked in image 300.

[0045] As in Figure 2 and 3 As can be seen in FIG, dynamic AB can be detected based on the intensity changes of pixels across image frames over one or more respiratory cycles. However, real-time visual identification of dynamic AB may be difficult or unreliable due to the rapid movement or motion of dynamic AB, while the lungs in the ultrasound image background may still move during breathing, where a respiratory cycle may span from about 3 seconds to about 6 seconds and duration. In addition, clinicians performing lung ultrasound imaging (e.g., at POC-US) may not necessarily be trained to interpret lung ultrasound images for dynamic AB detection.

[0046] Some studies have performed quantitative analysis of dynamic AB identification based on a measure of mean pixel intensity. Although such quantitative analysis has shown encouraging results (e.g., detection sensitivity of approximately 93%), quantitative analysis requires expertise to identify and separate regions corresponding to the patient's lungs. In addition, quantitative analysis methods may perform well only in large areas of pulmonary consolidation and may miss the detection of smaller pulmonary consolidations (e.g., extending less than approximately 1 centimeter (cm)) or PN at an early stage.

[0047] Figure 4is a schematic diagram illustrating an automatic dynamic AB detection scheme 400 according to aspects of the present disclosure. The scheme 400 can be used by the system 100 for dynamic AB detection. Specifically, the scheme 400 can be implemented by the processing component 134 on the host 130 or the processing component 116 on the probe 110. In some embodiments, the implementation of the scheme 400 can be divided between the host 130 and the probe 110.

[0048] The protocol 400 begins by receiving a plurality of image frames 410. The image frames 410 may be generated by using the probe 110, wherein ultrasound signal energy is emitted from the transducer array 112 toward a patient's body (e.g., the subject 105), and echo signals are received by the transducer array 112 forming the image frames 410. The probe 110 may be positioned on the patient's body to obtain an anterior chest view (e.g., from above the chest region) or a lateral chest view (e.g., from the side of the chest region) including at least some portions of the patient's lungs. Each image frame 410 may include a plurality of pixel values ​​(e.g., amplitudes) representing pixel intensities of an image of the patient's body. The image frames 410 may be taken over a time period (e.g., an exhalation period and an inspiration period) including at least one respiratory cycle. The image frames 410 are shown as frames (i) to frames (i+N) representing images of the patient's body from time (i) to time (i+N), where i and N are positive integers.

[0049] In addition to the patient's lungs, the image frame 410 may also include other parts of the patient's body. The scheme 400 may apply a region of interest (ROI) identification component 420 to the image frame 410 to identify the portion of the image frame corresponding to the patient's lungs for subsequent processing. One method of identifying the lung region is based on pleural line identification. The pleural line may appear as a bright line (e.g., Figure 5 ), and the area below the pleural line may correspond to the lungs. After identifying the portion corresponding to the patient's lungs, the ROI identification component 420 can identify the ROI in the lung area with potential lung consolidation for subsequent processing. For example, the ROI identification component 420 can perform overall background motion detection using block matching on a frame-by-frame basis, for example, based on the correlation between the image frames 410.

[0050] After the ROI is identified, the ROI identification component 420 can use the initial frame (e.g., frame (i)) as an anchor frame to align or register subsequent image frames 410 (e.g., frame (i+1) to frame (i+N)) to the initial image frame 410 based on the background motion information for subsequent operations described below. Alignment or registration allows operations to be performed on the same portion of the lung for each image frame 410.

[0051] After the ROI is identified, a threshold component 430 can be applied to each image frame 410. The threshold component 430 determines the number of pixels in the ROI of the corresponding image frame 410 that are above a predetermined threshold. The number of pixels above the threshold can be represented by a count value 432. The pixels above the threshold can correspond to bright spots, as shown in AB 212, 214, 216 and 218. Depending on the dynamic range of the pixel values ​​and the amplitude and / or pixel distribution in the ROI, the predetermined threshold can be configured to any suitable value. In one embodiment, the dynamic range of the pixel intensity value can be configured to be between about 0 and about 255. In such an embodiment, the threshold can be configured to be a value between about 40 and about 80.

[0052] After determining the number of bright pixels in each image frame 410, a maximum value component 440 and a minimum value component 450 may be applied to the count values ​​432. The maximum value component 440 determines a maximum value 442 of the count values ​​across the image frames 410. The minimum value component 450 determines a minimum value 452 of the count values ​​432 across the image frames 410. In one embodiment, the maximum value 442 and the minimum value 452 may be normalized so that the maximum value 442 has a value of 1.

[0053] After determining the maximum value 442 and the minimum value 452, the AB index component 460 can be used to determine the dynamic AB diagnosis result. As described above, the dynamic AB is shown as a bright structure (e.g., AB 212 and 214) or a bright spot (e.g., AB 216 and 218) that changes over time. The AB index component 460 identifies the dynamics of the AB by calculating the ratio between the maximum value 442 and the minimum value 452. The ratio can be called an AB index. For example, the AB index component 460 can compare the ratio with a predetermined threshold. When there is a large change between the maximum value 442 and the minimum value 452, the dynamic AB condition can be positive. On the contrary, when there is a small change between the maximum value 442 and the minimum value 452, the dynamic AB condition can be negative, and a static AB condition can exist. The presence of a dynamic AB condition may indicate a high probability of PN, while the presence of a static AB condition may indicate a high probability of atelectasis (e.g., a collapsed lung without infection) or other lung diseases. The dynamic AB diagnosis result can be displayed on the display 132 and / or the display 117. In addition, the result can indicate the location of the dynamic AB condition in the lungs.

[0054] Figure 55 is an ultrasound image 500 showing a dynamic AB according to aspects of the present disclosure. The system 100 can be used to acquire the image 500. The image 500 can represent an image of a patient's body (e.g., subject 105) including a lung region. The image 500 shows the presence of an AB 512 and a lung consolidation 506 in the lung region of the patient. As marked in the image 500, the pleural interface 502 or pleural boundary is shown as a bright line on the image 500. The area below the pleural interface 502 corresponds to the patient's lungs. The area above the pleural interface 510 corresponds to the patient's chest wall 504.

[0055] Figure 6 600 is a graph showing the change of several bright pixels over time across multiple image frames in the presence of dynamic AB according to aspects of the present disclosure. The x-axis represents the frame number. The y-axis represents the normalized number of pixels in the ROI in the image frame that are above a predetermined intensity threshold. The plot 600 is generated using the scheme 400. The graph 610 shows the change of the number of pixels above the threshold (e.g., count value 432 in normalized form) over time on several image frames (e.g., image frame 410) in the region of the dynamic AB 512 as shown in the image 500. It can be seen that the curve 610 varies between about 0.84 and about 1. The large difference between the minimum value (e.g., 0.84) and the maximum value (e.g., 1) observed in the graph 610 and the cyclic pattern can indicate the presence of a dynamic AB condition. The periodicity or periodic pattern can correspond to the patient's breathing cycle.

[0056] Figure 7 7 is an ultrasound image 700 showing a static AB according to aspects of the present disclosure. Image 700 may be acquired using system 100. Image 700 may represent an image of a patient's body (eg, subject 105) including a lung region. Image 700 shows the presence of a static AB 712 in the lung region of the patient.

[0057] Figure 8 800 is a graph showing the change of several bright pixels over time across multiple image frames in the absence of dynamic AB according to aspects of the present disclosure. The x-axis represents the frame number. The y-axis represents the normalized number of pixels in the ROI in the image frame that are above a predetermined intensity threshold. The plot 800 is generated using the scheme 400. The graph 810 shows the change of the number of pixels above the threshold (e.g., count value 432 in normalized form) over time on several image frames (e.g., image frame 410) in the area of ​​static AB 712 such as in image 700. It can be seen that the curve 810 is relatively static, varying between about 0.987 and about 1. The small difference between the minimum value (e.g., 0.987) and the maximum value (e.g., 1) observed in the graph 810 and the relatively static pattern can indicate the presence of a static AB condition (e.g., an atelectasis condition).

[0058] Fig. 9 is a schematic diagram illustrating a dynamic AB detection scheme 900 according to aspects of the present disclosure. The scheme 900 can be used by the system 100 for dynamic AB detection. Specifically, the scheme 900 can be implemented by the processing component 134 on the host 130 or the processing component 116 on the probe 110. In some embodiments, the implementation of the scheme 900 can be divided between the host 130 and the probe 110.

[0059] The scheme 900 begins by receiving a plurality of image frames 910 similar to the image frame 410. For example, each image frame 910 includes pixel intensity values ​​representing an image of a patient's body, the patient's body including at least a portion of the patient's lung. The scheme 900 may apply a ROI identification component 920 to the image frames 910. The ROI identification component 920 may be substantially similar to the ROI identification component 420. The ROI identification component 920 may identify a subset of data or pixels from the image frames 910 for dynamic AB condition determination. Identification may include selecting one or more pixel values ​​corresponding to the same portion of the patient's lung surrounding a lung consolidation from each image frame 910. The ROI identification component 920 may output a subset 930 of image data including pixels within the ROI. The subset 930 is shown as frame (i, k) to frame (i+N, k), respectively representing a subset k within frame (i) to a subset k within frame (i+1). When each subset 930 includes a pixel value, the pixel value is represented by an intensity value 932. When each subset 930 includes more than one pixel value, a spatial filter may be applied to each subset 930 to produce an average intensity value 932 .

[0060] After identifying subset 930, a maximum component 940 and a minimum component 950 may be applied to intensity values ​​932. Maximum component 940 determines a maximum value 942 of intensity values ​​932. Minimum component 950 determines a minimum value 952 of intensity values ​​932. In one embodiment, maximum value 942 and minimum value 952 may be normalized such that maximum value 942 has a value of 1. In some embodiments, a temporal filter (e.g., a smoothing filter) may be applied to subset 930 before determining maximum value 942 and minimum value 952, for example to obtain an average value of several frames.

[0061] After determining the maximum value 942 and the minimum value 952, a time intensity change determination component 960 may be applied to determine a dynamic AB diagnosis result. The time intensity change determination component 960 determines the time intensity change over time on the subset 930. The time intensity change determination component 960 may determine a ratio between the maximum value 942 and the minimum value 952. The time intensity change determination component 960 may compare the ratio to a predetermined threshold and determine whether a dynamic AB condition exists based on the threshold comparison. Similar to scheme 400, a large change between the maximum value 942 and the minimum value 952 indicates a positive dynamic AB condition, and a small change between the maximum value 942 and the minimum value 952 indicates a negative dynamic AB condition.

[0062] Fig.10 1 is a schematic diagram showing a bronchial tree enhancement scheme 1000 according to aspects of the present disclosure. The scheme 1000 can be employed by the system 100 to enhance the bronchial tree in lung consolidation in lung ultrasound images for dynamic AB diagnosis. Specifically, the scheme 1000 can be implemented by the processing component 134 on the host 130.

[0063] The protocol 1000 begins by receiving a plurality of image frames 1010 similar to image frames 410 and 910. For example, each image frame 1010 includes pixel intensity values ​​representing an image of a patient's body, the patient's body including at least a portion of the patient's lungs. The protocol 1000 applies a difference component 1020 to each pair of adjacent or successive image frames 1010 (e.g., frame (i) and frame (i+1)). The difference component 1020 calculates the difference between adjacent image frames 1010 to produce a differential image frame 1022. For example, the difference component 1020 subtracts the pixel values ​​in frame (i+1) from the pixel values ​​in frame (i) on a pixel-by-pixel basis. The pixel values ​​of frame (i) and the pixel values ​​of frame (i+1) correspond to the same sub-portion of the patient's lungs.

[0064] The absolute value component 1030 may be applied to the pixel values ​​in the differential image frame 1022 to generate a differential image frame 1032 having differential pixel absolute values. Subsequently, the summation component 1040 may be applied to the cumulative differential image frame 1032 to generate a cumulative image frame 1042. For example, the summation component 1040 sums the pixel values ​​of the differential image frame 1032 pixel by pixel. When the image frame 1010 includes a bronchial tree, the summation component 1040 may be applied to the cumulative image frame 1042 (in the following example). Fig.11D Detection of the bronchial tree indicates positive dynamic AB.

[0065] Figures 11A-11D Shows the above Fig.10The described scheme 1000 corresponds to each image frame at each stage. For example, the scheme 1000 is applied to eighty sequential ultrasound image frames represented by frame (1), frame (2), ..., frame (80). Fig.11A 1 is an image frame 1110 illustrating a bronchial tree 1112 at a moment in time in accordance with aspects of the present disclosure. Image frame 1110 may be similar to image frames 410, 910, and 1010. Image frame 1110 may represent the first image frame (e.g., frame (1)) of eighty sequential image frames. As shown, the bright Y-shaped branching structure in the middle portion of image frame 1110 corresponds to the bronchial tree 1112 within the patient's lung consolidation. The bright curved horizontal structure at the bottom of image frame 1110 corresponds to the patient's spine.

[0066] Fig. 11B is an image frame 1120 illustrating bronchial tree 1112 at another time in accordance with aspects of the present disclosure. For example, image frame 1120 may represent the last image frame (eg, frame (80)) of eight consecutive image frames.

[0067] Fig. 11C 1 is a diagram illustrating a difference between a pair of successive image frames including a bronchial tree 1112 according to aspects of the present disclosure. For example, in scheme 1000, difference image frame 1130 can represent difference image frame 1032 at the output of absolute component 1030. Difference image frame 1130 can be calculated by subtracting frame (80) from frame (79) frame by frame.

[0068] Fig.11D 1 is a cumulative image frame 1140 illustrating an enhanced bronchial tree 1112 according to aspects of the present disclosure. For example, in scheme 1000, image frame 1140 may represent cumulative image frame 1042 at the output of sum component 1040. Comparing cumulative image frame 1140 with the originally acquired image frames 1110 and 1120, the appearance or visibility of bronchial tree 1112 is enhanced in cumulative image frame 1140.

[0069] Fig.121 is a graph 1200 showing the change of a plurality of bright pixels over time across a plurality of image frames in the presence of a dynamic AB according to aspects of the present disclosure. The x-axis represents the frame number. The y-axis represents the normalized number of pixels above a predetermined intensity threshold in an ROI in an image frame. Graph 1200 is generated by applying scheme 400 to eight consecutive image frames used to illustrate scheme 1000 in FIG. 11 . Graph 1210 shows the change of the normalized number of pixels above a predetermined threshold over time on a plurality of image frames (e.g., image frame 1010) in the region of a bronchial tree 1112 shown in image frames 1110 and 1120. It can be seen that curve 1210 varies between about 0.53 and about 1. The large difference between the minimum value (e.g., 0.53) and the maximum value (e.g., 1) observed in graph 1210 and the periodic waveform can indicate the presence of a dynamic AB condition.

[0070] Fig.13 1 is a flow chart of a dynamic AB detection method 1300 according to aspects of the present disclosure. The steps of method 1300 may be performed by a computing device (e.g., a processor, processing circuitry, and / or other suitable components) of an ultrasound imaging probe such as probe 110 or a host such as host 130. Method 1300 may be implemented in the manner described in connection with Figure 4 A similar mechanism is described in the scheme 400. As shown, the method 1300 includes a plurality of steps listed, but embodiments of the method 1300 may include additional steps before, after, and between the steps listed. In some embodiments, one or more of the steps listed may be omitted or performed in a different order.

[0071] At step 1310, method 1300 includes receiving a plurality of frames of image data (eg, image frames 410, 910, 1010, 1110, and 1120) representing a body of a subject (eg, target 105) including at least a portion of a lung. The body of the subject may be a human or animal body.

[0072] At step 1320 , method 1300 includes determining a metric (eg, count value 432 ) for each of the plurality of image data frames, for example using threshold component 430 .

[0073] In step 1330, method 1300 includes determining the dynamic AB condition of the object body based on the change between the metrics of multiple image data frames. For example, maximum component 440 can be applied to the metric to calculate the maximum value (e.g., maximum value 442) of the metric, and minimum component 450 can be applied to the metric to calculate the minimum value (e.g., minimum value 452). Subsequently, AB index component 460 can be applied to calculate the ratio between the maximum value and the minimum value, and the ratio is compared with a predetermined threshold. When the ratio meets the predetermined threshold, a positive dynamic AB condition may occur. When the ratio does not meet the predetermined threshold, there may be no dynamic AB condition.

[0074] Fig.14 1 is a flow chart of a dynamic AB detection method 1400 according to aspects of the present disclosure. The steps of method 1400 may be performed by a computing device (e.g., a processor, processing circuitry, and / or other suitable components) of an ultrasound imaging probe such as probe 110 or a host such as host 130. Method 1400 may be implemented in the manner described in connection with Fig. 9 A similar mechanism is described in the scheme 900. As shown, the method 1400 includes a plurality of enumerated steps, but embodiments of the method 1400 may include additional steps before, after, and between the enumerated steps. In some embodiments, one or more of the enumerated steps may be omitted or performed in a different order.

[0075] At step 1410, method 1400 includes receiving a plurality of frames of image data (eg, image frames 410, 910, 1010, 1110, and 1120) representing a body of a subject (eg, target 105) including at least a portion of a lung. The body of the subject may be a human or animal body.

[0076] At step 1420 , method 1400 includes identifying a subset of data (eg, subset 930 ) from the plurality of frames of image data based on a threshold comparison, for example, using ROI identification component 920 .

[0077] At step 1430, method 1400 includes determining the dynamic AB condition of the body of the object based on the time variation across the data subset. For example, the subset of data may include a portion of each image data frame (e.g., one or more pixel values), and a spatial filter may be applied to the corresponding portion of each image frame to determine a first value (e.g., intensity) maximum value 932 of each image data frame. Maximum component 940 may be applied to the first value to calculate the maximum value of the first value (e.g., maximum value 942), and minimum component 950 may be applied to the first value to calculate the minimum value (e.g., minimum value 952). Subsequently, time intensity variation determination component 960 may be applied to calculate the ratio between the maximum value and the minimum value, and the ratio is compared with a predetermined threshold. When the ratio meets the predetermined threshold, a positive dynamic AB condition may occur. When the ratio does not meet the predetermined threshold, a dynamic AB condition may not exist.

[0078] Fig.15 1 is a flow chart of a bronchial tree enhancement method 1500 according to aspects of the present disclosure. The steps of method 1500 may be performed by a computing device (eg, a processor, processing circuitry, and / or other suitable components) of a host such as host 130. Method 1500 may be implemented in accordance with the present disclosure. Fig.10 A similar mechanism is described in the scheme 1000. As shown, the method 1500 includes a plurality of steps listed, but embodiments of the method 1500 may include additional steps before, after, and between the listed steps. In some embodiments, one or more of the listed steps may be omitted or performed in a different order.

[0079] At step 1510, method 1500 includes receiving a plurality of frames of image data (eg, image frames 410, 910, 1010, 1110, and 1120) representing a body of a subject (eg, target 105) including at least a portion of a lung. The body of the subject may be a human or animal body.

[0080] At step 1520 , method 1500 includes determining a differential image data frame (eg, differential image frame 1032 ) based on differences between successive image data frames of the plurality of image data frames.

[0081] At step 1530 , the method 1500 includes determining a cumulative data frame (eg, cumulative image frame 1042 ) based on the sum of the differential data frames.

[0082] At step 1540, method 1500 includes determining a dynamic AB condition of the subject's body based on the accumulated data frame. For example, the accumulated data frame shows an enhanced appearance or visibility of a bronchial tree at a location within a region of lung consolidation. Thus, the determination can be based on observation of the bronchial tree.

[0083] Various aspects of the present disclosure can provide several benefits. For example, automatic detection of dynamic AB does not require a trained clinician to interpret ultrasound lung images, so point-of-care medical ultrasound (POC-US) imaging can be used for PN screening and diagnosis. Automatic dynamic AB detection can produce diagnostic results in a very short time. Therefore, the PN examination time can be shortened compared to a full chest PN examination. In addition, the disclosed embodiments provide standardized testing protocols. The use of standardized testing protocols can produce more consistent diagnostic results compared to subjective assessments and analyses by different physicians and clinicians. Standardized testing protocols can be easily screened and are suitable for use in time-sensitive situations (e.g., in emergency situations). In addition, the enhanced display of the bronchial tree can help physicians quickly and easily identify PN locations. The disclosed embodiments are suitable for pediatric patients and / or pregnant women who are concerned about radiation exposure.

[0084] Those skilled in the art will recognize that the above-described devices, systems, and methods can be modified in many ways. Therefore, those skilled in the art will appreciate that the embodiments encompassed by the present disclosure are not limited to the specific exemplary embodiments described below. In this respect, although illustrative embodiments have been shown and described, wide-ranging modifications, alterations, and substitutions are contemplated in the above disclosure. It is to be understood that such changes may be made to the foregoing without departing from the scope of the present disclosure. Therefore, it is appropriate that the appended claims be understood broadly and in a manner consistent with the present disclosure.

Claims

1. An ultrasonic imaging system, comprising: an interface coupled to the ultrasound imaging component and configured to receive a plurality of image data frames representing a body of a subject including at least a portion of a lung, each image data frame of the plurality of image data frames comprising a plurality of pixel values ​​representing pixel intensities of an image of the body of the subject; a processing component in communication with the interface and configured to: determining a count metric for each of the plurality of image data frames by counting a number of a plurality of pixel values ​​in each image data frame that satisfy a threshold; and A dynamic air bronchogram AB condition of the subject's body is determined based on changes between the count metrics of the plurality of image data frames.

2. The ultrasound imaging system according to claim 1, wherein: The plurality of frames of image data represent images of the subject's body across a time period that includes at least one breathing cycle.

3. The ultrasound imaging system according to claim 1, wherein: The processing component is configured to determine the dynamic air bronchogram AB status by: determining a ratio between a maximum value of the count metric and a minimum value of the count metric; and The dynamic air bronchogram AB condition is determined based on the ratio.

4. The ultrasound imaging system according to claim 1, wherein: The processing unit is further configured to: identifying a region of interest (ROI) corresponding to the at least portion of the lung from the plurality of frames of image data; and The count metric is determined based on the region of interest ROI. 5 . The ultrasonic imaging system according to claim 1 , further comprising a display component configured to display a result of the dynamic air bronchogram AB condition.

6. The ultrasound imaging system according to claim 1, further comprising an ultrasound imaging probe, wherein the ultrasound imaging probe comprises: The ultrasonic imaging component; said processing means; and A display component is configured to display the result of the dynamic air bronchogram AB condition.

7. An ultrasonic imaging system, comprising: an interface coupled to the ultrasound imaging component and configured to receive a plurality of image data frames representing a body of a subject including at least a portion of a lung, each image data frame of the plurality of image data frames comprising a first plurality of pixel values ​​representing pixel intensities of an image of the body of the subject; a processing component in communication with the interface and configured to: determining a differential data frame based on differences between successive image data frames in the plurality of image data frames by subtracting pixel values ​​of a first image data frame in the plurality of image data frames from pixel values ​​of a second image data frame in the plurality of image data frames, the first image data frame being adjacent to the second image data frame; determining a cumulative data frame based on the sum of the differential data frames; and A dynamic air bronchial AB condition of the subject's body is determined based on the accumulated data frames.

8. The ultrasound imaging system according to claim 7, wherein: The plurality of frames of image data represent images of the subject's body across a time period that includes at least one breathing cycle.

9. The ultrasound imaging system according to claim 7, wherein: Each differential data frame includes a plurality of difference values, wherein the processing component is further configured to determine a first differential data frame in the differential data frames by the following operations: determining a difference value for the first differential data frame by determining an absolute value of a result of the subtraction, Wherein the pixel values ​​of the first image data frame and the pixel values ​​of the second image data frame represent a same sub-portion of the at least portion of the lung.

10. The ultrasound imaging system according to claim 7, wherein: Each differential data frame comprises a second plurality of pixel values, wherein the cumulative data frame comprises a plurality of summed values, and wherein the processing component is further configured to determine the dynamic air bronchogram AB condition by: determining each of the plurality of summed values ​​by accumulating a third plurality of pixel values ​​across the differential data frame, wherein the third plurality of pixel values ​​across the differential data frame represent a same portion of the subject's body; and The dynamic air bronchogram AB status is determined based on the plurality of summed values.

11. The ultrasound imaging system according to claim 7, further comprising: A display component is configured to display the accumulated data frame.

12. An ultrasound imaging system, comprising: an interface coupled to the ultrasound imaging component and configured to receive a plurality of image data frames representing a body of a subject including at least a portion of a lung across a period of time, each image data frame of the plurality of image data frames comprising a plurality of pixel values ​​representing pixel intensities of an image of the body of the subject; a processing component in communication with the interface and configured to: identifying a subset of data from the plurality of frames of image data by selecting one or more pixel values ​​corresponding to a same sub-portion of the at least portion of the lung from each of the plurality of frames of image data; determining an intensity metric for each frame of image data from the subset of data; and A dynamic air bronchogram AB condition of the subject's body is determined based on temporal changes across the subset of data by comparing a maximum value of the intensity metric across the time period with a minimum value of the intensity metric across the time period.

13. The ultrasound imaging system according to claim 12, wherein: The time period includes at least one breathing cycle.

14. The ultrasound imaging system according to claim 12, wherein: The processing unit is configured to: The dynamic air bronchogram AB condition is determined based on a ratio between the maximum value of the intensity measure and the minimum value of the intensity measure.

15. The ultrasound imaging system according to claim 14, wherein: The processing component is configured to apply a filter on the intensity measure before determining the dynamic air bronchogram AB condition. 16 . The ultrasound imaging system according to claim 12 , further comprising a display component configured to display a result of the dynamic air bronchogram AB condition.

Images