Automated fault detection and correction in ultrasound imaging system

Abstract

To provide ultrasound systems that can detect and adjust system faults.SOLUTION: A processor in an ultrasound imaging system identifies faults or errors in the system. In one embodiment, fault or error conditions are detected by monitoring system parameters during a self-test. In another embodiment, a processor provides ultrasound image data to a trained neural network to identify fault conditions in a transducer or the imaging system. In some embodiments, the processor makes adjustments to one or more operating parameters to compensate for the identified fault conditions so that the system continues to operate and produce images with the detected fault condition.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The disclosed technology relates to ultrasound imaging and in particular to ultrasound systems that can detect and correct system faults. [Background technology]

[0002] Due to its ease of use and non-ionizing radiation, ultrasound is an increasingly used imaging modality for human and animal subjects. In addition to providing images of internal body tissues, ultrasound is used to provide quantitative assessments of physiological function by researchers or healthcare providers. One common challenge, particularly with ultrasound systems and ultrasound transducers, is that they are relatively portable and therefore prone to abuse. Dropping a transducer or physically damaging an interconnecting cable can cause one or more channels to stop working or to operate erratically. Because operating with certain types of transducer failures can damage the system portion of the equipment, some ultrasound imaging systems stop imaging all together when a failure is detected. This can be frustrating in emergencies or other situations where a user would prefer a suboptimal image to no image at all, unless the failure is of a type that would further damage the equipment or potentially injure the subject. In other situations, the system continues imaging but does not notify the user that the image is suboptimal, and the user may interpret erroneous ultrasound data as actual data. Summary of the Invention

[0003] To address the above challenges and others, the disclosed technology is an ultrasound system that detects a system fault or error and performs one or more programmed actions, such as ceasing system operation, adjusting for the detected fault to allow the system to produce images without interruption, and alerting an operator that an image has been acquired with a detected fault.

[0004] In one embodiment, beamforming circuits for several transmit / receive channels are connected to the transducer elements via a programmable multiplexer. A processor controls a self-test in which the multiplexer is configured to disconnect one or more transducer probes from the beamforming circuit. Test pulses are generated while the transducers are disconnected. Faults are detected based on measured currents drawn from a power supply by the test pulses. If a current is detected, transmit pulsers can be selectively disabled to isolate the faulty channel. Depending on the severity of the fault, the processor can then compensate.

[0005] In another embodiment, the processor analyzes signals received on channels when the multiplexer connects or disconnects transducers. Acoustic echo information received on a channel that appears to be disconnected indicates a possible system multiplexer fault. Similarly, acoustic echo information received on a channel with a fully connected transducer is compared to adjacent channels. Abnormal signals indicate a faulty element. When a fault is detected, the processor takes programmed action to compensate for the particular fault.

[0006] In yet another embodiment, the processor controls the multiplexer to disconnect the transducer from the beamforming circuitry. At the same time, the multiplexer ground clamps are set for each transducer element, and the bias power supplies are enabled and monitored. If excessive current is detected, indicative of a bias voltage fault in the transducer, the processor may recommend not using the transducer and instead removing and repairing it.

[0007] In yet another embodiment, a processor of an ultrasound imaging system is programmed to provide ultrasound image data to a neural network trained to identify system faults or errors from the image data. In one embodiment, the neural network returns a probability of a detected fault or error to the processor. Upon detection of an identified fault or error, the processor is programmed to take one or more actions, such as ceasing operation of the system, adjusting the detected fault to allow the system to generate images without interruption, and alerting an operator that an image was acquired with a detected fault. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 illustrates an exemplary ultrasound system for generating ultrasound images according to an embodiment of the disclosed technology.

[0009] [Figure 2A] FIG. 2A shows an ultrasound image produced by a properly operating ultrasound imaging system.

[0010] [Figure 2B] FIG. 2B shows an ultrasound image produced by an ultrasound imaging system with noise problems and a failed receive channel.

[0011] [Figure 3] FIG. 3 is a block diagram of beamforming transmit / receive circuitry in an ultrasound imaging system that enables a processor to detect faults / errors according to an embodiment of the disclosed technology.

[0012] [Figure 4] FIG. 4 illustrates how a neural network is trained with several test images according to an embodiment of the disclosed technology.

[0013] [Figure 5] FIG. 5 is a logic diagram of steps performed by a programmed processor to use a trained neural network to detect faults or errors in an ultrasound imaging system and to compensate for the detected faults or errors, according to an embodiment of the disclosed technology. DETAILED DESCRIPTION OF THE INVENTION

[0014] As mentioned above, many ultrasound systems perform a diagnostic self-test when the device is first powered on. If a fault is detected, the system often prevents further operation or use of the system until it is inspected and repaired by a qualified technician. While this approach can prevent damage to the system and possibly the production of erroneous images, it often frustrates users in emergency situations where some image of the subject may be better than no image at all.

[0015] To overcome these deficiencies, the disclosed technology is directed to a system for automatically detecting a fault or error condition in an ultrasound imaging system. When a fault or error is detected, a processor is programmed to take one or more actions. In some embodiments, the processor is programmed to continue acquiring and displaying ultrasound images if the processor determines that imaging can continue without further damage to the system. In other embodiments, the processor is programmed to cease operation if the detected fault or error would cause further damage to the system. In other embodiments, the processor is programmed to continue imaging and compensate for the detected fault or error. In yet other embodiments, the processor is programmed to continue imaging and alert an operator that a fault or error has been detected.

[0016] FIG. 1 illustrates an exemplary ultrasound imaging system in which the disclosed technology may be implemented. The ultrasound imaging system 50 may include a handheld, portable, or cart-based imaging system. The ultrasound system 50 is coupled to one or more ultrasound transducers 54 that transmit ultrasound acoustic signals to a subject (not shown) and receive corresponding acoustic echo signals from the subject. In some embodiments, the ultrasound system 50 may receive signals from one or more additional external sensors 56, such as an SPO2 sensor, an EKG sensor, a respiration sensor, etc. The ultrasound imaging system 50 includes one or more displays on which ultrasound data is displayed. The displays may include touch-sensitive screens that allow a user to operate the system with touch commands. In some embodiments, additional controls (e.g., trackballs, buttons, keys, trackpads, voice-activated controls, motion sensors, etc.) may also be provided for user operation. The ultrasound imaging system 50 also includes communications circuitry (e.g., a modem, a NIC, Bluetooth, or 802.11 wireless, etc.) that allows the system to connect to one or more remote systems (not shown) via wired or wireless data communications links.

[0017] The ultrasound imaging system 50 includes signal and image processing circuitry having one or more processors (e.g., CPU, DSP, GPU, ASIC, FPGA, or combination thereof) configured to generate image data from returned analog echo signals. As described in detail below, the imaging system is configured to detect system faults or errors and determine a correct mode of operation. In one embodiment, a database 62 stores information regarding several different fault or error conditions and corresponding corrective actions to be taken by the processor. The database receives codes, numeric identifiers, or descriptions of specific fault or error conditions (also referred to as "fault conditions") and returns information regarding the actions the processor should take for the specific fault or error condition provided. In some embodiments, the imaging system performs self-tests by measuring operating conditions within the imaging system to detect fault or error conditions. In other embodiments, the processor executes programmed instructions stored in processor-readable memory or performs predetermined logical operations to implement a neural network 170 trained to analyze ultrasound image data to identify fault or error conditions in the imaging system. In the disclosed embodiments, the fault or error condition can be within the ultrasound imaging system itself, the transducer 54, or both. Examples of such error conditions or faults include, but are not limited to, shorted or open transducer elements, A / D converter quantization errors, receive amplifier or filter errors, beamformer FPGA errors, FPGA transmit circuitry, multiplexer, power amplifier errors, etc.

[0018] FIG. 2A shows a typical ultrasound image 80 obtained from a fully operational ultrasound imaging system, while FIG. 2B shows an image 82 with noise problems and a malfunctioning receive channel. Image 82 has several artifacts, such as a pattern of dark beamlines 84 corresponding to malfunctioning receive channels and a pattern of white stripes where noise is increasing. To a trained ultrasound technician, such artifacts indicate where a fault or error condition exists in the ultrasound imaging system. By detecting these types of errors, the processor of the ultrasound system of the disclosed technology can compensate for the artifacts. Such compensation involves filling in the data of the artifact lines using data from adjacent elements (before beamforming) or from adjacent beams (after beamforming) to approximate the compromised data, which reduces the discontinuity in the beamlines. If the processor determines that the artifact-causing fault could further damage the imaging system, the processor may disable further imaging until the fault can be repaired.

[0019] FIG. 3 is a block diagram of a transmit / receive path in an ultrasound imaging system according to one embodiment of the disclosed technology. The imaging system includes a processor and a beamforming control circuit 100. The processor executes program steps that control the operation of the ultrasound system, and the beamforming control circuit 100 controls the timing and magnitude of pulses provided to the transducer elements to steer the beam in a particular direction or perform a particular type of imaging (e.g., B-mode, M-mode, Doppler, etc.). A pulser circuit 102 provides high-voltage pulses to individual transducer elements via a transmit / receive switch 104 and a multiplexer 106. In one embodiment, the multiplexer can direct signals to one, two, or zero transducer elements via a switch 108. In one embodiment, the multiplexer (HDL6M06502B) includes a ground clamp 110 for each multiplexer output. A ground clamp 110 is open when the corresponding switch 108 is closed, and the ground clamp 110 is closed when the corresponding switch 108 is open. In the embodiment shown, the multiplexer 106 is coupled to a pair of transducers 120, 124, each having several individual transducer elements.

[0020] Power for the pulser 102 is provided by a pulser power supply 130. A transducer bias power supply 136 provides voltage rails for the transducers 120, 124. A receiver 140 (e.g., a low noise amplifier, an A / D converter, etc.) is connected to the transmit / receive switch 104. During transmission, the transmit / receive switch 104 connects the multiplexer 106 to the pulser 102. During reception, the transmit / receive switch connects the receiver 140 to the multiplexer 106. The output of the receiver 140 feeds the image processing hardware 144.

[0021] To test for faults or errors in the transmit / receive paths, the processor 100 is programmed to set the switch 108 of the multiplexer 102 to the open position so that the pulser 102 is disconnected from the transducers 120, 124 and the ground clamp 110 is set. In this configuration, all transducer elements are disconnected from the pulser. The processor provides test pulses to the transducer elements. The processor 100 receives a signal from a current sensor that senses the current provided by the pulser power supply 130. When all transducer elements are disconnected, the current should be minimal. If the switch 108 of the multiplexer is in the closed position due to some type of fault, a current draw may be detected that exceeds a minimum threshold. In this case, the processor is then programmed to apply test pulses to individual channels to determine which channels are associated with an increase in current draw. Once the extent of the fault is determined, the processor is programmed to take steps to mitigate the effects of the fault. In one example, the processor 100 is programmed to prevent signals from being transmitted on impaired channels and to average data to be received on the impaired channel with data signals received by adjacent elements.

[0022] In some embodiments, multiplexer 106 may lack ground clamp 110. In this embodiment, processor 100 is programmed to analyze the signal on each channel when the channel's multiplexer is set to "open." If a signal other than noise is received, the processor determines that there is a fault in the multiplexer switch or somewhere else in the receiving channel. The processor is then programmed to compensate for the fault, for example, by not transmitting on that channel and filling in the data for the faulty channel with data from an adjacent channel.

[0023] In another embodiment, the processor monitors the bias current provided by the bias power supply 136 when the transducers 120, 124 are connected to the system. With the switch 108 of the multiplexer 106 open and the ground clamp 110 closed, the transducers should be disconnected from the ultrasound system's beamforming circuitry. If an abnormal current spike occurs when the transducer's bias power supply is energized, the processor determines that the transducer is faulty. By monitoring the current provided by the bias power supply 136, short circuits can be detected without passing current through the transmit / receive switch 104 and potentially damaging it.

[0024] As described above, in some embodiments, the processor is programmed to use a trained neural network to detect faults / errors. Certain faults or error conditions may produce artifacts in the corresponding ultrasound image that are detectable by the trained neural network 170. After the neural network 170 is trained, the processor provides image data generated by the imaging processing electronics to the neural network, which returns a probability (percentage) that the image data was acquired by an imaging system with a known fault or error condition. For example, a transducer with a non-functional element may be associated with an image having dark regions along the beamlines in the image. As shown in FIG. 2B, a bad receive channel may produce dark lines or regions in the image. In some embodiments, the neural network 170 analyzes the image data and returns a probability or percentage, such as 0.96, and an associated error identifier, such as "#4 - transducer element 38 is not functioning," which is read by the processor to diagnose the fault / error condition.

[0025] In one embodiment, database 62 (FIG. 1) stores a record of known fault and error conditions and one or more actions to be taken to remedy the fault / error condition or whether imaging can continue. Once a fault or error condition is identified, the processor is programmed to use database 62 to determine a response to the detected fault / error condition.

[0026] Upon receiving results from the neural network, the processor is programmed to compensate or adjust one or more parameters to correct for the fault. For example, a non-functioning transducer element may be compensated for by increasing the contribution from signals from adjacent elements. Errors in a faulty amplifier or other signal processing component may be compensated for by not processing data from the faulty TX or RX channel or by adjusting the received ultrasound data to match data received by fully functional components before combining the data with data from other channels to generate an image. In other embodiments, the processor provides the user with a list of options for selecting how the system should respond to the detected fault / error. Such a list may include a warning that continued operation with the detected fault may harm the imaging system or transducer, void warranty protection, etc. In other embodiments, the user may be given a choice of how they want to adjust or replace the data for the faulty channel (e.g., averaging adjacent beamlines, copying the data of the faulty receive channel, etc.). In some embodiments, data for channels with detected faults may be color-coded or otherwise marked to ensure the operator knows that the data is being combined rather than received from tissue. In other embodiments, the adjusted or compensated data is combined to blend seamlessly with data from a fully functional channel.

[0027] As shown in FIG. 4, multiple test images 180 are provided to the neural network training engine 200 to train the neural network 170. The test images 180 are images or portions of images acquired by an imaging system or transducer with known impairments. In one embodiment, the image data is a uniform size, such as 256 pixels wide by 128 pixels high, with each pixel having an 8-bit black-and-white intensity value. Other image sizes, such as 256 pixels wide by 256 pixels high or 512 pixels wide by 64 pixels high, may also be used. The term "image" is intended to broadly refer to an image of ultrasound echo data. An image may include post-scan transformed data, pre-scan transformed data, or raw RF data. Also, not all data in an image may be used. The system may also be configured to better isolate errors in the image data through methods such as channel separation (transmitting and / or receiving on only one element), depth-dependent frequency filtering, control of beam firing order, and system and transducer multiplexer selection. A portion or section of the data of known size can be used to train a neural network.

[0028] As will be appreciated by those skilled in the art of machine learning, neural networks, and artificial intelligence, a large number of training images (e.g., 1,000-14,000 or more) are provided to the neural network training engine 200 to enable the engine to determine weights and bias values for multiple filters such that the convolutional neural network, using the weights and bias values, returns a probability that the image was acquired by an ultrasound imaging system or transducer having a particular fault or error condition. Those skilled in the art will also appreciate that data augmentation can be used to increase the total number of training images, whereby an initial basis set of images is augmented by linear and nonlinear modifications to generate additional training data. For example, augmentation can include both linear and nonlinear scaling, or changes in brightness or contrast.

[0029] In one embodiment, training data images are collected and classified by ultrasound repair technicians diagnosing faults or other error conditions from imaging systems or transducers returned for repair or from errors discovered during the manufacturing process. The images are classified and stored in a database along with metadata or a description of the fault or error condition. The image data is cropped or otherwise edited to conform to standard dimensions used to train the neural network.

[0030] Those skilled in the art will appreciate that many different machine learning models can be applied. For example, variants of freely available models such as VGG5, VGG16 (Oxford Visual Geometry Group), and MobileNet (Google) can be used. Custom models can also be developed. Tradeoffs for using different models can include prediction accuracy and size, which impacts inference speed on the embedded device.

[0031] A Python framework using Keras and Tensorflow may be used to train the model using the prepared and augmented data. An Adam optimizer with a variable learning rate may be applied to approximately 1 million training examples. Other optimizers, such as SGD (stochastic gradient descent), may be used. Tradeoffs for using different optimizers include convergence time and training speed. A combination of two or more different optimizers may also be used.

[0032] Neural network models themselves are generally interchangeable, and some have advantages over others. For example, computational complexity and output accuracy are considerations. Variations may include using a different model, changing the number of layers, or adding additional layers such as dense layers or additional convolutional layers.

[0033] Once the model is trained, it can be tested for accuracy using image data acquired with a system / transducer having an identified fault. The neural network 170 should identify the fault based on changes in the ultrasound image data relative to image data from a properly operating transducer / ultrasound system.

[0034] In use, trained neural network 170 is configured to receive input image data of the same size as the neural network was trained on (e.g., 256 x 128 x 1) and generate output data indicative of the probability of an image being acquired by an imaging system or transducer having a particular fault or error condition. In one embodiment, the output from the neural network (the return value from the trained neural network) is a list of the fault or error conditions that neural network 170 was trained to detect and the probability or likelihood that the input image was acquired by a transducer or imaging system having the particular fault / error condition. For example, in one embodiment, the system is trained to detect 200 known fault or error conditions. Thus, neural network 170 returns a 200-element array or list with the probability (e.g., percentage) that the image was acquired by a transducer or imaging system having one or more of the 200 known faults / error conditions.

[0035] A processor within the imaging system receives the array and scans the list for probabilities that exceed a threshold. For example, an entry [4, 0.78] indicates a 78% probability that the image was obtained from an imaging transducer with defined error condition #4 (e.g., transducer element 38 is open circuited), or, for example, [16, 0.86] indicates an 86% probability that error condition #16 (multiplexer shorted) occurred. More than one error condition / fault may be identified in the list. In some embodiments, all probabilities identified by the neural network sum to 1.0. A single entry in the returned data may indicate that the transducer / imaging system is operating normally.

[0036] In some embodiments, the imaging system's processor provides image data to the trained neural network 170 upon initial power-up for self-test. Modifications or adjustments to imaging parameters may occur before a scanning procedure begins. In other embodiments, the processor provides image data to the trained neural network 170 during a scanning procedure, with modifications or adjustments to imaging parameters occurring during the scan. For example, the neural network 170 is provided with an image (or a portion of an image) as the scan is occurring, and the neural network 170 identifies possible faults or error conditions as the system is in use. For example, if the neural network examines the image and determines that there is a problem with the amplifier of a receive channel, the processor may compensate the amplifier's gain to correct the error.

[0037] As shown above, once the neural network 170 is trained, the processor receives the network's output and adjusts one or more operating parameters to compensate for the detected fault or error condition. The processor may adjust the gain of signals received on faulty channels, not transmit from transducer elements found to be defective, or average signals received from elements adjacent to the faulty element. In some cases, the processor adjusts parameters internal to the imaging system (e.g., amplifier gain, averaging data from faulty channels, copying data from faulty channels, omitting transmission on certain transducer elements, etc.). In other cases, the processor may prompt the user to make adjustments to the testing procedure, such as instructing the user to apply more ultrasound gel or manually adjust gain. In other embodiments, the user is alerted to the detected fault / error condition and prompted to determine whether or not they wish to continue performing the test with the detected fault / error condition. Such prompts may include warnings about damage to the transducer or imaging system with continued use or may provide a prediction that continued use may result in another error or failure. In this case, the user can decide for themselves whether they want to take the risks associated with continuing to operate the system.

[0038] 5 is a flowchart of logic executed by a programmed processor in an ultrasound system in accordance with one embodiment of the disclosed technology. Beginning at 500, the processor acquires ultrasound image data from a connected transducer. Image data of the same size as used to train the neural network is provided to the neural network at 502 to determine whether the neural network can detect one or more faults or error conditions. At 504, the processor analyzes results from the neural network indicating whether the image was acquired using a transducer or imaging system with a particular fault or error condition. If an error or fault is detected, the processor adjusts one or more operating parameters (or prompts the user to make adjustments) at 506 to compensate for the detected fault or error condition and displays the image with the compensation at 508. If no fault or error is detected at 506, the image data is displayed without adjustment at 508.

[0039] As shown, the processor is programmed to detect faults and make adjustments to compensate for the detected faults / errors so that the imaging system can continue to produce images despite detected faults or errors, which benefits first responders, emergency room physicians, soldiers, and other individuals who require an imaging system that allows the system to continue operating even when outside of the manufacturer's specifications.

[0040] In one embodiment, images generated for a detected fault / error condition are stored along with a record of the compensation applied to the ultrasound data to correct the fault. In some cases, the observer can view images with and without the correction and select the image they prefer. Once a selection is made, additional images may be acquired, with or without providing each image to the neural network. In some embodiments, the compensation applied to a detected fault or error condition may not be optimal but may be preferred by the user. In some embodiments, the database 62 storing corrections or adjustments for detected faults or errors may store a record of user-preferred changes in place of predefined corrections or adjustments. In some embodiments, the database 62 stores a record of system operation and changes made to operating parameters after a fault or error is detected, allowing the system to record whether the changes made were effective in compensating for the detected fault or error condition. In some embodiments, databases from multiple imaging systems may be combined and updated to reflect changes most frequently selected by users or with updated changes or parameters discovered to better compensate for errors. In some embodiments, the database 62 is stored locally on the imaging system. If a communications link exists between the imaging system and a remote computer (server), the database 62 may be hosted remotely, and the imaging system processor is programmed to query the remote database for corrections or adjustments to be applied to detected faults / errors.

[0041] Embodiments of the subject matter and operations described in this specification can be implemented in digital electronic circuitry, or in software, firmware, or hardware, or in combinations of one or more of them, including the structures disclosed herein and their structural equivalents. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a computer storage medium for execution by or to control the operation of a data processing apparatus.

[0042] A computer storage medium may be or be included in a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of these. Furthermore, a computer storage medium is not a propagated signal, but a computer storage medium may be a source or destination of computer program instructions encoded in an artificially generated propagated signal. Also, a computer storage medium may be or be included in one or more separate physical components or media (e.g., EEPROM, flash memory, CD-ROM, magnetic disk, or other storage device). Operations described in this specification may be implemented as operations performed by a data processing device on instructions stored in one or more computer-readable storage devices or received from other sources.

[0043] The term "processor" encompasses all types of apparatus, devices, and machines for processing data, including, for example, a programmable processor, a microcontroller, a digital signal processor (DSP), a graphics processor (GPU), a computer, a system-on-a-chip, or two or more or combinations of these. An apparatus may include special purpose logic circuitry, such as an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit), or hardwired logic circuitry.

[0044] A computer program (also known as a program, software, software application, script, or code) can be written in any type of programming language, including compiled or interpreted, declarative or procedural, and can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program may be stored as part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple coordinated files (e.g., files storing one or more modules, subprograms, or portions of code). A computer program can be deployed to run on one processor or multiple processors within an ultrasound imaging system.

[0045] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors. Typically, a processor receives instructions and data from a read-only memory or a random-access memory or both. Suitable devices for storing computer program instructions and data include all types of non-volatile memory, media, and storage devices, including, by way of example, semiconductor memory devices such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0046] To provide for user interaction, embodiments of the subject matter described herein may be implemented on an ultrasound imaging system that has a display device, such as an LCD (liquid crystal display), LED (light emitting diode), or OLED (organic light emitting diode) monitor, for displaying information to the user, and a keyboard and pointing device, such as a mouse or trackball, that allows the user to provide input to the system. In some implementations, a touchscreen may be used to display information and receive input from the user. Other types of devices may be used to provide for user interaction; for example, feedback provided to the user may be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback, and input from the user may be received in any form, including acoustic, voice, or tactile input.

[0047] From the foregoing, it will be appreciated that, although specific embodiments of the invention have been described herein for purposes of illustration, various modifications may be made without departing from the scope of the invention. Accordingly, the invention is not limited except as by the appended claims.

Claims

1. a transducer configured to apply ultrasound signals to a subject and receive ultrasound echo signals from the subject; a processor configured to execute instructions to generate ultrasound image data from the received ultrasound echo signals and provide the ultrasound image data to a neural network configured to detect one or more fault conditions of the transducer or imaging system based on the provided image data, the processor further configured to execute instructions to adjust one or more operating parameters to compensate for the detected fault conditions; a display configured to display the ultrasound image data acquired with the one or more adjusted parameters; An ultrasound imaging system comprising:

2. the processor is configured to execute instructions to provide the ultrasound image data to the neural network during a self-test mode or during an examination. The ultrasound imaging system of claim 1 .

3. the processor is configured to execute instructions to alert a user to make one or more changes to operating parameters to compensate for the detected fault condition. The ultrasound imaging system of claim 1 .

4. the processor is configured to execute instructions to store ultrasound image data along with an indication of changes made to operating parameters to compensate for the detected fault condition. The ultrasound imaging system of claim 1 .

5. the processor is configured to execute instructions to display images with and without the change made to the operating parameter to compensate for the detected fault condition. The ultrasound imaging system of claim 4 .

6. further comprising a database accessible by the processor for storing changes in one or more operating parameters associated with the detected fault condition; The ultrasound imaging system of claim 1 .

7. the database stores a record of detected system status after a change made to one or more operating parameters after a detected fault condition; The ultrasound imaging system of claim 6 .

8. the database stores a record of changes made by a user to operating parameters to compensate for the detected fault condition; The ultrasound imaging system of claim 6 .

9. the processor is configured to execute instructions to provide a list of one or more actions to be taken in response to the detected fault condition or a prompt for an indication of whether imaging should continue in the detected fault condition. The ultrasound imaging system of claim 1 .

10. a transducer configured to apply ultrasound signals to a subject and receive ultrasound echo signals from the subject; a multiplexer configured to selectively connect the transducers to transmit / receive circuitry in an imaging system; a power supply that provides power to the transmit / receive circuitry to transmit ultrasound waves from the transducer to a subject; a processor configured to execute instructions to configure the multiplexer to disconnect the transducer from the transmit / receive circuitry, transmit a test pulse to the transducer, and monitor current drawn from the power supply with the transducer disconnected during the test pulse to detect one or more fault conditions in the transducer or imaging system, the processor further configured to execute instructions to adjust one or more operating parameters to compensate for identified fault conditions; a display configured to display the ultrasound image data acquired with the one or more adjusted parameters; An ultrasound imaging system comprising:

11. the processor is configured to execute instructions to alert a user to make one or more changes to operating parameters in response to the detected fault condition. The ultrasound imaging system of claim 10.

12. the processor is configured to execute instructions to store ultrasound image data along with an indication of changes made to operating parameters to compensate for the detected fault condition. The ultrasound imaging system of claim 10.

13. further comprising a database accessible by the processor for storing changes in one or more operating parameters associated with the identified fault condition; The ultrasound imaging system of claim 10.

14. the processor is configured to execute instructions to display images with and without the modification made to compensate for the detected fault condition. The ultrasound imaging system of claim 10.

15. the processor is configured to execute instructions to prompt a user whether imaging should continue with the detected fault condition. The ultrasound imaging system of claim 10.

16. a transducer having a plurality of transducer elements configured to apply ultrasonic signals to a subject and receive ultrasonic echo signals from the subject; a multiplexer having switches configured to selectively connect the transducers to transmit / receive circuitry in an imaging system and ground clamps that ground the transducer elements when the switches for the transducer elements are open; a power supply that provides power to the transmit / receive circuitry to transmit ultrasound waves from the transducer to a subject; a processor configured to execute instructions to configure the multiplexer to disconnect the transducer from the transmit / receive circuitry in the imaging system and monitor current drawn from the power supply when the power supply is energized to detect one or more fault conditions in the transducer, the processor further configured to execute instructions to adjust one or more operating parameters to compensate for identified fault conditions; a display configured to display the ultrasound image data acquired with the one or more adjusted parameters; An ultrasound imaging system comprising:

17. the processor is configured to execute instructions to alert a user to make one or more changes to operating parameters to compensate for the detected fault condition.

17. The ultrasound imaging system of claim 16.

18. the processor is configured to execute instructions to store ultrasound image data along with an indication of changes made to operating parameters to compensate for the detected fault condition.

17. The ultrasound imaging system of claim 16.

19. further comprising a database accessible by the processor for storing changes in one or more operating parameters associated with the identified fault condition; 17. The ultrasound imaging system of claim 16.

20. the processor is configured to execute instructions to display images with and without the modification made to compensate for the detected fault condition.

17. The ultrasound imaging system of claim 16.

21. the processor is configured to execute instructions to prompt a user whether imaging should continue with the detected fault condition.

17. The ultrasound imaging system of claim 16.

Images