Patent Application
20230263501
Ultrasound imaging is a useful medical imaging modality. For example, internal structures of a patient's body may be imaged before, during or after a therapeutic intervention. Also, qualitative and quantitative observations in an ultrasound image can be a basis for diagnosis. For example, ventricular volume determined via ultrasound is a basis for diagnosing, for example, ventricular systolic dysfunction and diastolic heart failure.
A healthcare professional typically holds a portable ultrasound probe, sometimes called a “transducer,” in proximity to the patient and moves the transducer as appropriate to visualize one or more target structures in a region of interest in the patient. A transducer may be placed on the surface of the body or, in some procedures, a transducer is inserted inside the patient's body. The healthcare professional coordinates the movement of the transducer so as to obtain a desired representation on a screen, such as a two-dimensional cross-section of a three-dimensional volume.
Many ultrasound systems perform imaging in multiple imaging modes; two of these are B-mode and M-mode. B-mode is the system's default imaging mode, in which the system displays echoes in two dimensions by assigning a brightness level based on the echo signal amplitude. M-mode, also known as Motion Mode, provides a trace of the image displayed over time. A single beam of ultrasound is transmitted, and reflected signals are displayed as dots of varying intensities, which create lines across the screen.
Particular views of an organ or other tissue or body feature (such as fluids, bones, joints or the like) can be clinically significant. Such views may be prescribed by clinical standards as views that should be captured by the ultrasound operator, depending on the target organ, diagnostic purpose or the like.
In some ultrasound images, it is useful to identify anatomical structures visualized in the image. For example in an ultrasound image view showing a particular organ, it can be useful to identify constituent structures within the organ. As one example, in some views of the heart, constituent structures are visible, such as the left and right atria; left and right ventricles; and aortic, mitral, pulmonary, and tricuspid valves.
Existing software solutions have sought to identify such structures automatically. These existing solutions seek to “detect” a structure by specifying a horizontal bounding box in which the structure is visible, or “segment” the structure by identifying the individual pixels in the image that show the structure.
“Heart rate” is a medical metric indicating the rate at which the heart fills with and pumps out blood, often expressed in the unit “beats per minute,” or “bpm.” The heart rate metric is commonly measured using a stethoscope or electrocardiogram.
The inventor has recognized that ultrasound is commonly used in emergency medicine settings where healthcare providers examine a patient's health condition on the spot, perform quick diagnosis, and execute optimal procedure. In this setting, heart rate can provide useful information on a patient's cardiac functionality. There is seldom time or space to measure heart rate by conventional means dedicated to this purpose, however.
It has occurred to the inventor that automatically determining heart rate based upon a sequence of ultrasound images captured for a different primary purpose can provide the benefit of an accurate heart rate measurement without the need to have on hand special-purpose instruments such as stethoscope or electrocardiogram, or to devote the space or provider time needed to operate them.
In response, the inventor has conceived and reduced to practice a software and/or hardware facility that automatically determines heart rate from a sequence of ultrasound images (“the facility”). In various embodiments, the facility determines heart rate using data produced in the B-mode and/or M-mode imaging modes.
In some embodiments, the facility performs peak finding, period calculation, and post-processing on a time-series signal. In some embodiments, the facility generates this time-series signal by a process of filtering, pooling, and buffering an ultrasound-related input. In various embodiments, this input is any combination of a series of raw ultrasound images; a series of vectors of view logits indicating the likelihood that each ultrasound image was captured from each of a number of possible views; and/or a series of object detection result that identifies, in each ultrasound image, objects of different types detected at particular locations. In some embodiments, these ultrasound images are captured in B-mode. In some embodiments, the facility produces the view logit vectors and/or the object detection results using a machine learning model, such as a convolutional neural network or other artificial neural network.
In some embodiments, the facility applies an auto-encoder model made using multi-layer perceptrons (MLP) to reconstruct the vertical lines in an M-mode ultrasound image captured in the M-mode imaging mode. In particular, in the auto-encoder model constructed by the facility, a first MLP reduces, or “encodes,” the signal of the M-mode image to a small latent representation of the signal. A second auto-encoder expands, or “decodes,” the latent representation to a reconstructed version of the signal. After training the model, the facility operates the trained first multi-layer perceptron of the auto-encoder model to reduce the signal of an M-mode image to a latent representation, then applies the process of filtering, pooling, buffering, peak-finding, period calculation, and post-processing described above. In some embodiments, the facility augments the sequence of ultrasound images for display to include visual information about the patient's determined heart rate, such as a digital display of heart rate, a bar graph or analog gauge showing heart rate, a heart rate vs. time graph, etc.
By performing in some or all of these ways, the facility determines a patient's heart rate from a sequence of the patient's ultrasound images, without the need to possess or operate any separate instruments dedicated to that task. Also, the facility incorporates information about the patient's determined heart rate into display of the patient's sequence of ultrasound images so that both can easily be viewed together by the ultrasound operator or other healthcare providers.
Additionally, the facility improves the functioning of computer or other hardware, such as by reducing the dynamic display area, processing, storage, and/or data transmission resources needed to perform a certain task, thereby enabling the task to be permitted by less capable, capacious, and/or expensive hardware devices, and/or be performed with lesser latency, and/or preserving more of the conserved resources for use in performing other tasks. For example, the facility in many cases renders unnecessary a stethoscope and/or an electrocardiogram machine, so that high-quality emergency medical care can be provided without the expense or effort of acquiring and maintaining these instruments.
The probe 12 is configured to transmit an ultrasound signal toward a target structure and to receive echo signals returning from the target structure in response to transmission of the ultrasound signal. The probe 12 includes an ultrasound sensor 20 that, in various embodiments, may include an array of transducer elements (e.g., a transducer array) capable of transmitting an ultrasound signal and receiving subsequent echo signals.
The device 10 further includes processing circuitry and driving circuitry. In part, the processing circuitry controls the transmission of the ultrasound signal from the ultrasound sensor 20. The driving circuitry is operatively coupled to the ultrasound sensor 20 for driving the transmission of the ultrasound signal, e.g., in response to a control signal received from the processing circuitry. The driving circuitry and processor circuitry may be included in one or both of the probe 12 and the handheld computing device 14. The device 10 also includes a power supply that provides power to the driving circuitry for transmission of the ultrasound signal, for example, in a pulsed wave or a continuous wave mode of operation.
The ultrasound sensor 20 of the probe 12 may include one or more transmit transducer elements that transmit the ultrasound signal and one or more receive transducer elements that receive echo signals returning from a target structure in response to transmission of the ultrasound signal. In some embodiments, some or all of the transducer elements of the ultrasound sensor 20 may act as transmit transducer elements during a first period of time and as receive transducer elements during a second period of time that is different than the first period of time (i.e., the same transducer elements may be usable to transmit the ultrasound signal and to receive echo signals at different times).
The computing device 14 shown in
In some embodiments, the display screen 22 may be a touch screen capable of receiving input from an operator that touches the screen. In such embodiments, the user interface 24 may include a portion or the entire display screen 22, which is capable of receiving operator input via touch. In some embodiments, the user interface 24 may include one or more buttons, knobs, switches, and the like, capable of receiving input from an operator of the ultrasound device 10. In some embodiments, the user interface 24 may include a microphone 30 capable of receiving audible input, such as voice commands.
The computing device 14 may further include one or more audio speakers 28 that may be used to output acquired or conditioned auscultation signals, or audible representations of echo signals, blood flow during Doppler ultrasound imaging, or other features derived from operation of the device 10.
The probe 12 includes a housing, which forms an external portion of the probe 12. The housing includes a sensor portion located near a distal end of the housing, and a handle portion located between a proximal end and the distal end of the housing. The handle portion is proximally located with respect to the sensor portion.
The handle portion is a portion of the housing that is gripped by an operator to hold, control, and manipulate the probe 12 during use. The handle portion may include gripping features, such as one or more detents, and in some embodiments, the handle portion may have a same general shape as portions of the housing that are distal to, or proximal to, the handle portion.
The housing surrounds internal electronic components and/or circuitry of the probe 12, including, for example, electronics such as driving circuitry, processing circuitry, oscillators, beamforming circuitry, filtering circuitry, and the like. The housing may be formed to surround or at least partially surround externally located portions of the probe 12, such as a sensing surface. The housing may be a sealed housing, such that moisture, liquid or other fluids are prevented from entering the housing. The housing may be formed of any suitable materials, and in some embodiments, the housing is formed of a plastic material. The housing may be formed of a single piece (e.g., a single material that is molded surrounding the internal components) or may be formed of two or more pieces (e.g., upper and lower halves) which are bonded or otherwise attached to one another.
In some embodiments, the probe 12 includes a motion sensor. The motion sensor is operable to sense a motion of the probe 12. The motion sensor is included in or on the probe 12 and may include, for example, one or more accelerometers, magnetometers, or gyroscopes for sensing motion of the probe 12. For example, the motion sensor may be or include any of a piezoelectric, piezoresistive, or capacitive accelerometer capable of sensing motion of the probe 12. In some embodiments, the motion sensor is a tri-axial motion sensor capable of sensing motion about any of three axes. In some embodiments, more than one motion sensor 16 is included in or on the probe 12. In some embodiments, the motion sensor includes at least one accelerometer and at least one gyroscope.
The motion sensor may be housed at least partially within the housing of the probe 12. In some embodiments, the motion sensor is positioned at or near the sensing surface of the probe 12. In some embodiments, the sensing surface is a surface which is operably brought into contact with a patient during an examination, such as for ultrasound imaging or auscultation sensing. The ultrasound sensor 20 and one or more auscultation sensors are positioned on, at, or near the sensing surface.
In some embodiments, the transducer array of the ultrasound sensor 20 is a one-dimensional (1D) array or a two-dimensional (2D) array of transducer elements. The transducer array may include piezoelectric ceramics, such as lead zirconate titanate (PZT), or may be based on microelectromechanical systems (MEMS). For example, in various embodiments, the ultrasound sensor 20 may include piezoelectric micromachined ultrasonic transducers (PMUT), which are microelectromechanical systems (MEMS)-based piezoelectric ultrasonic transducers, or the ultrasound sensor 20 may include capacitive micromachined ultrasound transducers (CMUT) in which the energy transduction is provided due to a change in capacitance.
The ultrasound sensor 20 may further include an ultrasound focusing lens, which may be positioned over the transducer array, and which may form a part of the sensing surface. The focusing lens may be any lens operable to focus a transmitted ultrasound beam from the transducer array toward a patient and/or to focus a reflected ultrasound beam from the patient to the transducer array. The ultrasound focusing lens may have a curved surface shape in some embodiments. The ultrasound focusing lens may have different shapes, depending on a desired application, e.g., a desired operating frequency, or the like. The ultrasound focusing lens may be formed of any suitable material, and in some embodiments, the ultrasound focusing lens is formed of a room-temperature-vulcanizing (RTV) rubber material.
In some embodiments, first and second membranes are positioned adjacent to opposite sides of the ultrasound sensor 20 and form a part of the sensing surface. The membranes may be formed of any suitable material, and in some embodiments, the membranes are formed of a room-temperature-vulcanizing (RTV) rubber material. In some embodiments, the membranes are formed of a same material as the ultrasound focusing lens.
The model takes a 128×128×1 ultrasound image 420 as its input, and produces two outputs: a vector 461 of N view logits, each value indicating the probability that the ultrasound image was captured from a different standard ultrasound view; an object detection output 471, which in some embodiments is a 3-dimensional array where two of the dimensions identify different positions in the ultrasound image, and the third dimension identifies the type, size, and location of objects identified near those positions. The model first subjects the input ultrasound image to a convolutional block made up of 2D convolutional layer 421, 2D batch normalization layer 422, and leaky relu activation function layer 423. The model then proceeds to a convolutional block made up of 2D convolutional layer 424, 2D batch normalization layer 425, and leaky relu activation function layer 426. The model then proceeds to a downsample layer 430. The model then proceeds to a convolutional block made up of 2D convolutional layer 431, 2D batch normalization layer 432, and leaky relu activation function layer 433. The model then proceeds to a convolutional block made up of 2D convolutional layer 434, 2D batch normalization layer 435, and leaky relu activation function layer 436. The model then proceeds to a downsample layer 440. The model then proceeds to a convolutional block made up of 2D convolutional layer 441, 2D batch normalization layer 442, and leaky relu activation function layer 443. The model then proceeds to a convolutional block made up of 2D convolutional layer 444, 2D batch normalization layer 445, and leaky relu activation function layer 446. The model then proceeds to a downsample layer 450. The model then proceeds to a convolutional block made up of 2D convolutional layer 451, 2D batch normalization layer 452, and leaky relu activation function layer 453. The model then proceeds to a convolutional block made up of 2D convolutional layer 454, 2D batch normalization layer 455, and leaky relu activation function layer 453. The model then branches to produce the view prediction 461 via a pooling layer 460, and the object detention output 471. In various embodiments, the facility uses a variety of neural network architectures and other machine learning model architectures to produce similar results. In some embodiments, the network produces one or the other of the shown outputs, but not both.
Those skilled in the art will appreciate that the acts shown in
In some embodiments (not shown), rather than performing peak-finding and calculating periods between peaks, the facility subjects the contents of the time-series array to a machine learning model trained to predict heart rate from time-series array contents. In some embodiments, the facility uses a direct regression machine learning model for this purpose. In some embodiments, the facility trains the machine learning model for predicting heart rate, such as by performing the described process on a number of experimental human subjects, and contemporaneously determining their heart rate by another means, such as by using a stethoscope and/or electrocardiogram. The facility trains machine the learning model with these observations to predict heart rate as the dependent variable from time-series array contents as the independent variable.
The various embodiments described above can be combined to provide further embodiments. All of the U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patent applications and non-patent publications referred to in this specification and/or listed in the Application Data Sheet are incorporated herein by reference, in their entirety. Aspects of the embodiments can be modified, if necessary to employ concepts of the various patents, applications and publications to provide yet further embodiments.
These and other changes can be made to the embodiments in light of the above-detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure.
