Echo window artifact classification and visual indicators for an ultrasound system

A method for providing artifact detection and visualization during ultrasound image collection is performed by a processor in an ultrasound system. The method includes receiving ultrasound image data from an ultrasound probe, detecting areas with artifacts in the ultrasound image data, classifying the areas with artifacts into one of a plurality of available artifact classes, generating an indication of the areas with artifact for an ultrasound-based image, wherein the indications include a designation of the artifact class, and presenting to an operator the ultrasound-based image and the indication of the areas with artifacts.

BACKGROUND

Ultrasound scanners are typically used to identify a target organ or another structure in the body and/or determine features associated with the target organ/structure, such as the size of a structure or the volume of fluid in an organ. Obtaining an adequate quality of ultrasound images can be challenging even for experienced operators.

Using ultrasound imaging technology on human subjects, operators frequently notice unwanted image artifacts from different sources, such as the pubic bone, insufficient gel, bowel gas, etc. These artifacts can hide important information needed to make accurate organ measurements and corresponding clinical decisions. An experienced operator may be able to make adjustments to avoid unwanted artifacts, such as moving a probe to avoid blocking bones or applying more gel. However, for most operators, an automated indication of an error type can help the operator make correct adjustments more quickly and effectively to obtain better imaging of a target organ.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Implementations described herein utilize machine learning to classify artifact information from B-mode ultrasound echoes into simple visual indications within an ultrasound imaging system, to assist in providing better aiming and more accurate quantitative analysis. According to one example, a convolutional neural network can be used to generate a probability map corresponding to the shadows from different sources inside B-mode images. Simple visual indications can then be constructed from the maps for the operator to make adjustments in order to achieve better image quality and better measurement accuracy.

In one implementation, a method for providing artifact detection and visualization during ultrasound image collection may be performed by a processor in an ultrasound system. The method may include receiving ultrasound image data from an ultrasound probe, detecting areas with artifacts in the ultrasound image data, classifying the areas with artifacts into a class selected from a group of available artifact classes, generating an indication of the areas with an artifact for an ultrasound-based image, wherein the indications include a designation of the selected class, and presenting to an operator the ultrasound-based image and the indication of the areas with artifacts. As described further herein, an ultrasound-based image may generally include a B-mode image, a C-mode image, or another type of image derived from ultrasound data.

FIG. 1is a schematic of a scanning system100in which systems and methods described herein may be implemented. Referring toFIG. 1, scanning system100includes a probe110, a base unit120, and a cable130.

Probe110includes a handle portion, a trigger, and a nose (or dome) portion. Medical personnel may hold probe110via the handle and press trigger112to activate one or more ultrasound transceivers, located in the nose portion, to transmit ultrasound signals toward a target object of interest, which may include an organ (e.g., a bladder, an aorta, a kidney, etc.) or a non-organ structure (e.g., a catheter, a needle, or another medical device). For example, as shown inFIG. 1, probe110is located on pelvic area of patient150and over a target object of interest152, which in this example is the patient's bladder.

The dome of probe110is typically formed of a material that provides an appropriate acoustical impedance match to an anatomical portion and/or permits ultrasound energy to be properly focused as it is projected into the anatomical portion. For example, an acoustic gel or gel pads, illustrated at area154inFIG. 1, may be applied to patient's skin over the region of interest (ROI) to provide an acoustical impedance match when the dome is placed against the skin.

Probe110includes one or more ultrasound transceiver elements and one or more transducer elements within the dome that transmit ultrasound energy outwardly from the dome, and receive acoustic reflections or echoes generated by internal structures/tissue within the anatomical portion. For example, the one or more ultrasound transducer elements may include a one-dimensional, or a two-dimensional array of piezoelectric elements that may be moved within the dome by a motor to provide different scan directions with respect to the transmission of ultrasound signals by the transceiver elements. Alternatively, the transducer elements may be stationary with respect to probe110so that the selected anatomical region may be scanned by selectively energizing the elements in the array.

Probe110may communicate with base unit120via a wired connection, such as via cable130. In other implementations, probe110may communicate with base unit120via a wireless connection (e.g., Bluetooth, Wi-Fi, etc.). In each case, base unit120includes a display122to allow an operator to view processed results from an ultrasound scan, and/or to allow operational interaction with respect to the operator during operation of probe110. For example, display122may include an output display/screen, such as a liquid crystal display (LCD), light emitting diode (LED) based display, or other type of display that provides text and/or image data to an operator. For example, display122may provide artifact visualizations overlaid on B-mode images to help determine the quality/accuracy of an ultrasound scan. Display122may also display two-dimensional or three-dimensional images of the selected anatomical region.

To scan a selected anatomical portion of a patient, the dome of probe110may be positioned against a surface portion of patient150as illustrated inFIG. 1that is proximate to the anatomical portion to be scanned. The operator actuates the transceiver and transducer elements, causing the transceiver to transmit ultrasound signals into the body and receive corresponding return echo signals that may be at least partially processed by the transceiver to generate an ultrasound image of the selected anatomical portion. In a particular embodiment, the transceiver transmits ultrasound signals with the center frequency in a range that extends from approximately about two megahertz (MHz) to approximately 10 MHz or more.

In one embodiment, probe110may be coupled to a base unit120that is configured to generate ultrasound energy at a predetermined frequency and/or pulse repetition rate and to transfer the ultrasound energy to the transceiver. Base unit120also includes one or more processors or processing logic configured to process reflected ultrasound energy that is received by the transceiver to produce an image of the scanned anatomical region.

In still another particular embodiment, probe110may be a self-contained device that includes one or more microprocessors or processing logic configured within the probe110and software associated with the microprocessor to operably control the transceiver and transducer elements, and to process the reflected ultrasound energy to generate the ultrasound image. Accordingly, a display on probe110may be used to display the generated image and/or to view artifact visualizations and other information associated with the operation of the transceiver. In other implementations, the transceiver may be coupled to a general-purpose computer, such as a laptop or a desktop computer that includes software that at least partially controls the operation of the transceiver and transducer elements, and also includes software to process information transferred from the transceiver so that an image of the scanned anatomical region may be generated.

FIG. 2is a block diagram of functional logic components implemented in system100in accordance with an exemplary implementation. Referring toFIG. 2, system100includes a data acquisition unit210, an artifact identification unit220, a visual indication generator230, and main processing logic240. In an exemplary implementation, data acquisition unit210may be part of probe110and the other functional units (e.g., artifact identification unit220, visual indication generator230, and main processing logic240) may be implemented in base unit120. Alternatively, data acquisition unit210, artifact identification unit220, visual indication generator230, and main processing logic240may be implemented in probe110. In other implementations, the particular units and/or logic may be implemented by other devices, such as via computing devices or servers located externally with respect to both probe110and base unit120(e.g., accessible via a wireless connection to the Internet or to a local area network within a hospital, etc.). For example, probe110may transmit echo data and/or image data to a processing system via, for example, a wireless connection (e.g., Wi-Fi or some other wireless protocol/technology) that is located remotely from probe110and base unit120.

As described above, probe110may include a transceiver that produces ultrasound signals, receives echoes from the transmitted signals and generates image data based on the received echoes. Data acquisition unit210may include, for example, demodulation, decimation, log compression, and filtering sub-modules, to generate an image that can be presented for visualization by a human. A rotating transducer or transducer array with probe110may scan along multiple scan planes.

FIGS. 3A and 3Bprovide simplified illustrations of scan planes310(e.g., planes310-1,310-2, and310-3) and315(e.g., planes315-1,315-2,315-3, etc.) which may be employed by probe110to capture ultrasound images. Data acquisition unit210may use image data from one or more scan planes310or315to generate two-dimensional B-mode images, such as B-mode images410and420shown inFIGS. 4A and 4B, respectively. While three scan planes310and ten scan planes315are shown inFIGS. 3A and 3B, respectively, in practice, more scan planes are used to compile a comprehensive image of a target object. For example, dozens of scan planes may be used by some probes110. In an exemplary implementation, data acquisition unit210obtains data associated with multiple scan planes corresponding to the region of interest in patient150.

FIG. 3Cprovides a simplified illustration of C-mode image planes320. C-mode images may generally include a representation oriented perpendicular to typical B-mode scan planes310, for example. In one implementation, a C-mode image may include a cross-sectional image generated from ultrasound data of scan planes310or315at a particular depth, as indicated by plane320-1. Thus, data acquisition unit210may use image data from a certain depth in each of scan planes310or315to generate a C-mode image. In another implementation, a C-mode image may include a ‘shadowgram’ or ‘projection’ of 3D ultrasound data onto an ‘XY’ plane, where Z axis represents depth, as indicated by plane320-2. C-mode images may be presented as an ultrasound image or as a cartoon-like graphic. Simplified illustrations of C-mode images510and520are shown inFIGS. 5A and 5B, respectively.

Referring again toFIG. 2, artifact identification unit220may perform pre-processing of an image and detect in real time if ultrasound artifacts (e.g., false-positive echoes or false-negative echoes due to shadows, air/poor coupling, bowel gases, and other types of interference) are present in an echo window, which may be a B-mode image, a C-mode image, or another type of ultrasound image. For example, artifact identification unit220may receive an input image from data acquisition unit210(e.g., any of images410,420,510,520, etc.) and detect features in the echo window that are indicative of ultrasound artifacts. As described further herein, artifact identification unit220may analyze B-mode image data, C-mode image data, or other types of image data using a multi-class artifact categorization algorithm to recognize one or more types of artifacts, such as shadows, gases, air-interfaces, etc.

For example, referring toFIG. 4A, artifact identification unit220may detect an air scan area412and a pubic bone shadow414within B-mode image410that targets a bladder416. As another example, referring toFIG. 4B, artifact identification unit220may detect an area of bowel gas422within B-mode image420that targets an aorta424. Referring toFIG. 5A, C-mode images are generated from a compilation of B-mode images. Artifact identification unit220may similarly detect an air scan area512and a pubic bone shadow514within a C-mode image510that targets a bladder516(although air scan area512and pubic bone shadow514may not typically be shown in a conventional C-mode image). Referring toFIG. 5B, artifact identification unit220may detect areas of bowel gas522and an air scan area524within a C-mode image520that targets an aorta526. While artifacts such as air scan area412, pubic bone shadow414, bowel gas422, etc., may be identified by experienced operators, automatic real-time detection and visualization (or visual enhancement) of artifacts may simplify aiming of probe110and ensure better accuracy for both experienced and inexperienced operators. Detection and elimination of these artifacts in B-made images may also provide for more accurate C-mode images. Artifact identification unit220is described further, for example, in connection withFIG. 9.

Returning toFIG. 2, visual indication generator230may apply a visual indication, such as an overlay on images, for artifacts detected by artifact identification unit220. For example, based on a type of artifact detected by artifact identification unit220, visual indication generator230may provide a highlight or outline of an artifact area.FIGS. 6A-7Bprovide examples of sample outputs that may be generated by visual indication generator230.FIGS. 6A and 6Billustrate visualizations applied to B-mode images410and420, respectively.FIGS. 7A and 7Billustrate visualizations applied to C-mode images510and520respectively.

Referring toFIG. 6A, visual indication generator230may provide an output610for a bladder scan. Output610may include B-mode image410, an air artifact identifier612, a shadow artifact identifier614, and an organ outline616. B-mode image410may correspond to echo data from one of scan planes310or315ofFIG. 3A or 3B. Air artifact identifier612may be an outline, contrasting area, or another visible indicator highlighting air scan area412(e.g., as detected by artifact identification unit220). Shadow artifact identifier614may be another outline, contrasting area, or different visible indicator highlighting pubic bone shadow414. Organ outline616may correspond to an estimated shape of bladder416and may include another outline or visible indicator. In one implementation, air artifact identifier612and shadow artifact identifier614may be color-coded to indicate a respective type of artifact (e.g., air and shadow). In another implementation, artifact identifiers (including air artifact identifier612, shadow artifact identifier614, and other artifact identifiers described herein) may be supplemented with visible or audible text instructions to an operator. For example, a separate legend explaining the colors or artifact markings may accompany output610.

As shown inFIG. 6A, the echo window for B-mode image410is partially blocked by the pubic bone (e.g., due to improper aim) and air (e.g., due to poor coupling). Bladder416, in particular, is partially occluded by the pubic bone shadow414, which means the estimated shape of bladder416, as indicated by organ outline616, may be inaccurate. Indicators612,614, and616of output610provide a clear visual indication of how artifacts (e.g., air scan area412and pubic bone shadow414) can impact a desired target (e.g., bladder416) measurement without complex processing heuristics or delays. Thus, indicators612,614, and616may provide an operator with real-time information that probe110adjustments are needed to obtain an accurate bladder scan.

Referring toFIG. 6B, visual indication generator230may provide an output620for an aorta scan. Output620may include B-mode image420, a gas artifact identifier622, and an organ outline624. B-mode image420may correspond to echo data from one of scan planes310ofFIG. 3Aor scan planes315ofFIG. 3B. Gas artifact identifier622may be an outline, contrasting area, or another visible indicator highlighting bowel gas area422(e.g., as detected by artifact identification unit220). Organ outline624may correspond to an estimated shape of aorta424and may include another outline or indicator. Similar to artifact identifiers612and614described above, gas artifact identifier622may be color-coded and/or supplemented with descriptive text.

As shown inFIG. 6B, the echo window for B-mode image420is partially blocked by bowel gases. Aorta424, however, is fully visible without obstruction. Indicators622and624of output620provide a clear visual indication that artifacts (e.g., bowel gas area422) do not impact the desired target (e.g., aorta424) measurement without the need for complex processing heuristics or delays. Thus, indicators622and624may provide an operator with real-time information that probe110adjustments are not needed to obtain an accurate scan.

Referring toFIG. 7A, visual indication generator230may provide an output710for a bladder scan. Output710may include C-mode image510, an air artifact identifier712, a shadow artifact identifier714, and an organ outline716. C-mode image510may correspond to echo data from C-mode image plane320ofFIG. 3C. Air artifact identifier712may be an outline, contrasting color, or another visible indicator highlighting air scan area512(e.g., as detected by artifact identification unit220). Shadow artifact identifier714may be another outline, contrasting color, or different visible indicator highlighting pubic bone shadow514. Organ outline716may correspond to an estimated shape of bladder516and may include another outline, color, or indicator. In one implementation, air artifact identifier712and shadow artifact identifier714may be color-coded to indicate a respective type of artifact (e.g., air, shadow, etc.). In another implementation, artifact identifiers (including air artifact identifier712, shadow artifact identifier714, and other artifact identifiers described herein) may be supplemented with visible or audible text instructions to an operator.

As shown inFIG. 7A, the echo window for C-mode image510in general, and target bladder516in particular, is partially blocked by the pubic bone shadow (e.g., due to improper aim) and air (e.g., due to poor probe-skin coupling). Indicators712,714, and716of output710provide a clear visual indication of how the artifacts impede the desired target measurement (e.g., volume or size measurements of the bladder). Thus, indicators712,714, and716may provide an operator with real-time information that probe110adjustments are needed to obtain an accurate bladder scan.

Referring toFIG. 7B, visual indication generator230may provide an output720for an aorta scan. Output720may include C-mode image520, gas artifact identifiers722, air artifact identifier724, and an organ outline726. C-mode image510may correspond to echo data from C-mode image plane320ofFIG. 3C. Gas artifact identifier722may be an outline, contrasting area, or another visible indicator highlighting bowel gas area522(e.g., as detected by artifact identification unit220). Air artifact identifier724may be an outline, contrasting color, or another visible indicator highlighting air scan area524(e.g., as detected by artifact identification unit220). Organ outline726may correspond to an estimated shape of aorta526and may include another outline or indicator. Similar to the other artifact identifiers described above, gas artifact identifier722may be color-coded and/or supplemented with descriptive text (e.g., “A” for air, “G” for gas, “S” for shadow, the words “air,” “gas,” shadow,” etc.).

As shown inFIG. 7B, a portion of the echo window for C-mode image520is blocked by air scan and bowel gases. Aorta526, however, is visible without obstruction. Indicators722,724, and726of output720provide a clear visual indication that artifacts (e.g., bowel gas area522and air scan area524) do not impact the desired target (e.g., aorta526) measurement without the need for complex processing heuristics or associated delays. Thus, indicators722,724and726may provide an operator with real-time information that probe110adjustments are not needed to obtain an accurate scan.

WhileFIGS. 6A-7Bprovide examples of visual artifact indicators for B-mode and C-mode scans for a bladder and aorta. In other implementations, visual indication generator230may provide an output with visual indicators for artifacts in other scan modes and/or for other target objects.

Referring again toFIG. 2, main processing logic240may provide additional analysis of a target object (e.g., bladder416, aorta424, etc.), such as cavity-type recognition, volume estimations, diameter or size estimations, or other clinically useful information with B-mode, C-mode, or other types of images acquired by data acquisition unit210. For example, main processing logic240may identify a cavity as a bladder, estimate a volume for the bladder, or identify a diameter of the aorta.

The exemplary configuration illustrated inFIGS. 1 and 2is provided for simplicity. System100may include more or fewer logic units/devices than illustrated inFIGS. 1 and 2. For example, system100may include multiple data acquisition units210and multiple processing units that process the received data. In addition, system100may include additional elements, such as communication interfaces (e.g., radio frequency transceivers) that transmit and receive information via external networks to aid in analyzing ultrasound signals to identify a target object of interest. Furthermore, while illustrations and descriptions herein primarily refer to bladder and aorta applications, other embodiments can be applied to wall boundary detection of other organs, such as a prostate/kidney boundary, other blood vessels, thyroid, etc.

FIG. 8is a block diagram illustrating communications between functional logic components in scanning system100. As shown inFIG. 8, B-mode images810(e.g., from data acquisition unit210) may be received by artifact identification unit220, which may detect features of pixels and/or pixel grouping within each B-mode image810to permit artifact classification.

Artifact identification unit220may associate artifact portions (e.g. pixels) of the B-mode image810into one or more classes based on criteria from training data. In one implementation, artifact identification unit220may apply a pre-trained deep convolutional neural network (DCNN). A DCNN or other image classification algorithms may be well-suited for application of hardwired circuitry, such as a field-programmable gate array (FPGA), an application specific integrated circuits (ASIC), etc., to provide rapid image classification. Each type of artifact may result in distinct types of pixel groupings within an ultrasound image that can be detected using probability matching. According to one example, artifact identification unit220may include a probability map corresponding to the different types of shadows (e.g., strong shadows from pubic bone versus lighter shadows from bowel gas) and other artifacts (reverberations due to poor probe-to-skin coupling) inside B-mode images. In one implementation, artifact definitions may be generated off-line by a machine learning system and stored in artifact identification unit220. In another implementation, artifact identification unit220may include a dynamic learning capability, where actual processed images and user responses to visualizations (e.g., visualizations612,712, etc.) may be used improve artifact identification unit220.

In the example ofFIG. 8, artifact identification unit220may use four different classes: shadow from public bone820, shadow from bowel gas822, artifacts from bad coupling824, and artifacts from other sources826. Shadow from public bone820, shadow from bowel gas822, artifacts from bad coupling824may represent primary known artifact classes that are detectable from pixel groupings in B-mode images. Artifacts from other sources826may include any unidentified artifact or other types of known artifacts not in the primary known artifact classes, such as a catheter or needle shadow class, a rib shadow class, or other classes which impact the scanning image quality and associated quantitative analysis accuracy. In one implementation, artifacts from other sources826may be separated into multiple classes or sub-classes, such as separate groupings for catheter or needle shadow class, a rib shadow class, and an unknown class. In some cases, pixels may be classified under multiple classes. Artifact identification unit220may support any number of categories for output, as long as the amount of ultrasound images in the training dataset supports each category. However, as the total number of categories grows, the classification distance (e.g., in terms of image feature space) between two categories could be potentially decreased, which could lead to difficulties for the correct classification and the confusion to human operators. In one implementation, artifact identification unit220may use no more than six categories.

The particular artifact classes820,822,824, and826shown inFIG. 8may be appropriate for a particular ultrasound application, such as conducting a bladder scan. In other implementations, different types or amounts of artifact classes may be used in artifact identification unit220. For example, for an abdominal aortic aneurysm (AAA) scan, artifacts from bad coupling and shadow from bowel gas may be the primary classes with an undefined (or “other sources”) class for other artifacts. Conversely, for a new type of human heart scan, shadows from ribs and shadows from medical devices may be two primary artifact classifications (e.g., instead of being grouped together in an “other sources” class).

Artifact identification unit220may forward locations and classes of classified pixels to visual indication generator230. Visual indication generator230may produce overlays corresponding to the location and class of the artifact pixels. A B-mode visualizations unit830may apply visualizations (e.g., air artifact identifier612, gas artifact identifier622, etc.) directly to B-mode images. For C-mode images, a C-mode visualizations unit840may receive and compile locations for B-mode visualizations with other B-mode data to generate C-mode visualizations (e.g., air artifact identifier712, gas artifact identifier722, etc.). For other types of images, such as three-dimensional renderings, other visualizations unit850may receive and compile locations of B-mode visualizations with other image data to generate the other types of ultrasound images.

FIG. 9is a block diagram of exemplary communications for generating real-time artifact visualizations in scanning system100. An operator900may use ultrasound probe110to acquire real-time data from a targeted anatomical region. For example, operator900may control probe110to position905the nose of probe110toward an organ of interest (e.g., organ152ofFIG. 1) and emit ultrasonic signals.

Data acquisition unit210may receive echo data and process the echo data to generate, for example, a two-dimensional B-mode image910. Data acquisition unit210may forward B-mode image910to artifact identification unit220.

Artifact identification unit220may analyze image910using, for example, a multi-class artifact categorization algorithm to classify different areas (e.g., pixels) of image910with one or more of multiple different artifact categories, such as an air scan area412, a pubic bone shadow414, etc. According to an implementation, pixels of image910may be simultaneously included in two separate categories. For example, artifact identification unit220may identify some pixels in image910as having both strong shadows and reverberations. Artifact identification unit220may forward the category associations915for pixels of image910to visual indication generator230.

Visual indication generator230may receive category associations915and generate artifact visualizations based on category associations915. According to an implementation, visual indication generator230may match the category associations915to a particular color, indication, and/or textual reference using a table.

Visual indication generator230may select appropriate visualizations/text corresponding to the category for pixels in image910and submit an enhanced image920(e.g., output610,620, etc.) to display122for presentation to operator900. The artifact visualizations (e.g., gas artifact identifier622, etc.) may be displayed on the screen (e.g., display122) and, optionally, audibly output by a speaker to provide the operator real-time feedback and instructions in helping the operator acquire best quality data and subsequently accurate calculated results, such as volume or size measurements.

Because artifact identification unit220analyzes individual B-mode images (e.g., two-dimensional images), enhanced image920from visual indication generator230may be presented (via display122) in real-time (e.g., less than 0.5 seconds delay). Artifact visualizations for C-mode images, which are generated from the B-mode visualizations, may be similarly presented without significant delay.

Operator900may detect925enhanced image920from display122. Assuming enhanced image920includes artifact visualizations that require a user to adjust probe110, operator900may re-position905probe110(or take other actions to correct artifacts). Data acquisition unit210may receive new echo data and process the new echo data to generate another B-mode image910, C-mode image, etc. Artifact identification unit220may analyze the image910for artifact pixels to again provide category associations915to visual indication generator230.

Assuming enhanced image920includes artifact visualizations that do not obstruct a target object, operator900may choose to not adjust probe110. Visual indication generator230may forward the unobstructed/accepted image930to main processing logic240.

Main processing logic240may receive image930and subsequent images930, if needed, to provide a desired measurement or calculation, such as an organ boundary, bladder volume estimate, cavity recognition, aorta size, etc., based on image930. Main processing logic240may provide a calculated result935to display122for presentation to the operator900.

FIG. 10is a block diagram of exemplary communications for generating real-time artifact visualizations in scanning system100according to another implementation. Communications inFIG. 10represent feedback provided to an operator along with a requested output.

Similar to communications described in connection withFIG. 9, inFIG. 10, operator900may control probe110to position905the nose of probe110toward an organ of interest and emit ultrasonic signals. Data acquisition unit210may receive echo data and process the echo data to generate image910. Data acquisition unit210may send image910to artifact identification unit220. Artifact identification unit220may analyze image910and classify pixel groupings or areas within image910into one or more artifact categories. Artifact identification unit220may provide category associations915for image910to visual indication generator230.

Additionally (and simultaneously) with sending image910to artifact identification unit220, data acquisition unit210may send image910to main processing logic240. Main processing logic240may receive image910and subsequent images910, if needed, to detect a target object (e.g., a bladder, kidney, aorta, medical device, etc.) and provide a desired measurement or calculation, such as an organ boundary/shape or a bladder volume estimate, based on image910. Thus, an artifact assessment (from artifact identification unit220) and a calculated measurement (from main processing logic240) can be obtained at the same time. Main processing logic240may provide the calculated result1010to visual indication generator230.

Visual indication generator230may receive category associations915and calculated results1010. Visual indication generator230may use category associations915and calculated results1010to generate a visualization of artifact areas for operator900. In one implementation, visual indication generator230may provide the calculated result1010with additional guidance to indicate a potential error in a calculated result due to the presence of artifacts.

Visual indication generator230may select appropriate visualizations corresponding to the class of artifacts detected within image910and submit the images with artifact visualizations as output1020to display122for presentation to operator900. For example, output1020may correspond to output610,620,710, or720, described above and may be presented via display122. Operator900may detect1025output1020on display122. Operator900can choose to adjust probe110and re-scan or simply accept the result based on the location of the target object relative to the visualized artifacts (if any).

FIG. 11is a flow diagram illustrating an exemplary process1100for generating real-time artifact visualizations for ultrasound scans. Process1100may be performed, for example, by base unit120of system100. In another implementation, process1100may be performed by base unit120in conjunction with probe110. In one implementation, process1100may begin after probe110obtains an ultrasound image as described above.

Process1100may include receiving and processing scan images (block1110). For example, data acquisition unit210) may receive one or more B-mode ultrasound image from probe110and apply noise reduction and/or other pre-processing techniques to remove speckle and background noise from the image. In some embodiments, the aspect ratio of the raw B-mode image can be adjusted through a resizing process to compensate for differences between axial and lateral resolution. In other implementations, such as bladder scanning applications, a scan conversion can also be applied to make a bladder shape more accurately reflect the actual shape of a typical bladder.

Process1100may also include detecting ultrasound artifacts in the scan images (block1120) and visualizing the detected artifact areas (block1130). For example, artifact identification unit220may receive pre-processed images, such as image910, from data acquisition unit210. Artifact identification unit220may analyze pixel groupings in image910using, for example, a multi-class image categorization algorithm to classify areas of image910into one or more of multiple different artifact categories (e.g., pubic bone shadow, bowel gas shadow, reverberations, etc.). Visual indication generator230may generate visible indicators for the different types of artifacts in real time and overlay the artifact visualizations over the scan image for presentation to the operator900.

The operator may decide to accept the scan (block1140). For example, based on the location of visualized artifacts relative to a target object, operator900may determine that a scan is acceptable despite the presence of ancillary artifacts. Alternatively, operator900may determine that artifacts (e.g., as represented by visualizations612,712, etc.) require probe adjustment and re-scanning.

If the operator does not accept the scan (block1140—NO), process1100may include the operator adjusting the probe (block1150) and returning to block1110to receive and process scan images. For example, operator900may reject a scan with output (e.g. output610,710) that shows artifacts occluding a target object. Operator900may adjust the probe110position, apply more gel to the skin, or take other corrective actions to improve the scan result and initiate another scan.

If the operator accepts the scan (block1140—YES), process1100may include performing main processing (block1160) and presenting a result (block1170). For example, operator900may accept a scan with no artifacts or output (e.g. output620,720) that shows artifacts in the echo window do not impact view of a target object. Main processing logic240may perform a request calculation/measurement for the target object and return a result (e.g., bladder volume, aorta diameter, etc.) to operator900.

AlthoughFIG. 11shows a particular order of blocks for process1110, in other implementations, process1100may be performed in a different order. For example, in one implementation, the main data processing of block1160may be performed simultaneously with blocks1120and/or1130.

FIG. 12is a diagram illustrating exemplary physical components of base unit120. Base unit120may include a bus1210, a processor1220, a memory1230, an input component1240, an output component1250, and a communication interface1260. In other implementations, probe110may include similar components.

Bus1210may include a path that permits communication among the components of base unit120. Processor1220may include a processor, microprocessors, ASICs, controllers, programmable logic devices, chipsets, FPGAs, graphics processing unit (GPU), application specific instruction-set processors (ASIPs), system-on-chips (SoCs), central processing units (CPUs) (e.g., one or multiple cores), microcontrollers, and/or some other type of component that interprets and/or executes instructions and/or data. Processor1220may be implemented as hardware (e.g., an FPGA, etc.), a combination of hardware and software (e.g., a SoC, an ASIC, etc.), may include one or multiple memories (e.g., cache, etc.), etc.

Memory1230may include any type of dynamic storage device that may store information and instructions (e.g., software1235), for execution by processor1220, and/or any type of non-volatile storage device that may store information for use by processor1220.

Software1235includes an application or a program that provides a function and/or a process. Software1235is also intended to include firmware, middleware, microcode, hardware description language (HDL), and/or other form of instruction.

Input component1240may include a mechanism that permits an operator to input information to base unit120, such as a keyboard, a keypad, a button, a switch, a touch screen, etc. Output component1250may include a mechanism that outputs information to the operator, such as a display (e.g., display122), a speaker, one or more light emitting diodes (LEDs), etc.

Communication interface1260may include a transceiver that enables base unit120to communicate with other devices and/or systems via wireless communications, wired communications, or a combination of wireless and wired communications. For example, communication interface1260may include mechanisms for communicating with another device or system, such as probe110, via a network, or to other devices/systems, such as a system control computer that monitors operation of multiple base units (e.g., in a hospital or another type of medical monitoring facility). In one implementation, communication interface1260may be a logical component that includes input and output ports, input and output systems, and/or other input and output components that facilitate the transmission of data to/from other devices.

Base unit120may perform certain operations in response to processor1220executing software instructions (e.g., software1235) contained in a computer-readable medium, such as memory1230. A computer-readable medium may be defined as a non-transitory memory device. A non-transitory memory device may include memory space within a single physical memory device or spread across multiple physical memory devices. The software instructions may be read into memory1230from another computer-readable medium or from another device. The software instructions contained in memory1230may cause processor1220to perform processes described herein. Alternatively, hardwired circuitry, such as an ASIC, an FPGA, etc., may be used in place of or in combination with software instructions to implement processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software.

Base unit120may include fewer components, additional components, different components, and/or differently arranged components than those illustrated inFIG. 12. As an example, base unit120may include one or more switch fabrics instead of, or in addition to, bus1210. Additionally, or alternatively, one or more components of base unit120may perform one or more tasks described as being performed by one or more other components of base unit120.

Systems and methods described herein provide real-time artifact visualizations to operators during the ultrasound scanning. The real-time artifact visualization is helpful in assisting inexperienced operators, as well as experienced operators, to acquire high quality ultrasound data and achieve accurate calculated organ dimensions, such as bladder volume measurements, aorta diameter, etc. Machine learning, such as a deep convolutional neural network, enables rapid classification of artifact areas within scan images that can be used to provide real-time feedback.

Systems and methods described herein minimize the requirement for an operator to interpret artifacts within ultrasound images and transfer that task to logic within system100. Conventional ultrasound systems require that an operator interpret image content or wait for complete scan results to determine if artifacts have impacted the scan results. For inexperienced users, correctly understanding what happens in an ultrasound image is not a trivial task. The systems and methods described herein perform an initial level of artifact detection for the operators to minimize the burden of image interpretation and delays of a full scan calculation before detecting probe operator errors.

Although the invention has been described in detail above, it is expressly understood that it will be apparent to persons skilled in the relevant art that the invention may be modified without departing from the spirit of the invention. Various changes of form, design, or arrangement may be made to the invention without departing from the spirit and scope of the invention.