Patent Description:
The most common clinical measure of AAA severity, which plays a major role in making a decision on surgical intervention, is the diameter of the aneurysm.

The article "<NPL> discloses an approach for improving the surveillance of the size of abdominal aortic aneurysms (AAA), where use of 3D ultrasound imaging combined with semi-automatic quantification provides automatic selection of the optimal plane for diameter measurement.

The article "<NPL> discloses different 3D vessel lumen segmentation techniques.

The following detailed description refers to the accompanying drawings. Also, the following detailed description does not limit the invention.

Current ultrasound examination of abdominal aortic aneurysms (AAA) uses anterior-posterior measurement derived from a single two-dimensional (2D) still image. A major source of error associated with this method is that investigators will orientate the image plane differently. Furthermore, agreement between ultrasound and computed tomography (CT) is known to be inadequate.

Implementations described herein relate to using ultrasound imaging for identifying an abdominal aorta, which may include an aneurysm. In accordance with one exemplary implementation, ultrasound imaging of the abdominal aorta may be performed without the need for manual segmentation of the aorta and without using other imaging modalities, such as CT scans or magnetic resonance imaging scans (MRIs). Three-dimensional (3D) ultrasound offers the opportunity to establish a 3D AAA model from which both the maximum diameter perpendicular to the centerline of the abdominal aorta and a partial volume can be calculated. According to systems and methods described herein, 3D ultrasound can be used to measure aorta boundaries, such as estimate the AAA diameter perpendicular to the centerline as well as the AAA volume. The systems and methods may perform 3D abdominal aorta segmentation based on a 3D vascular shape model and intensity model.

For example, in some implementations, a flexible 3D aorta model is applied to 3D echo data to provide image segmentation for structures of interest, such as the abdominal aorta (or other blood vessels) or other structures of interest (e.g., an aneurysm) based on information obtained via an ultrasound scanner. The flexible 3D aorta model is defined based on the human abdominal aorta, with possible variations integrated into the shape model. Fitting the flexible 3D aorta model to a new echo data set can be defined as minimizing a special energy function. In some implementations, the flexible 3D aorta model may be a defined segment. In other implementations, the flexible 3D aorta model may be open-ended (e.g., without length restrictions). The intensity model can also be defined by analyzing the ultrasound image brightness inside and outside the aorta structures.

Segmentation is the first step for quantitative analysis in AAA evaluation using 3D ultrasound imaging. With abdominal aorta segmentation complete, post processing steps, such as centerline extraction and maximum diameter calculations, can be easily determined.

<FIG> is a diagram illustrating an exemplary scanning system <NUM> consistent with an exemplary embodiment. Referring to <FIG>, scanning system <NUM> includes a probe <NUM>, a base unit <NUM>, and a cable <NUM>.

Probe <NUM> includes a handle portion <NUM> (also referred to as handle <NUM>), a trigger <NUM> and a nose portion <NUM> (also referred to as dome or dome portion <NUM>). Medical personnel may hold probe <NUM> via handle <NUM> and press trigger <NUM> to activate one or more ultrasound transceivers and transducers located in nose portion <NUM> to transmit ultrasound signals toward a patient's area of interest (e.g., a blood vessel, organ, joint, etc.). For example, as shown in <FIG>, probe <NUM> may be positioned over the abdominal region of a patient and over a target vessel, such as the abdominal aorta to obtain an image of the abdominal aorta.

Handle <NUM> allows a user to move probe <NUM> relative to the patient's area of interest. As discussed above, trigger <NUM> initiates an ultrasound scan of a selected anatomical portion while dome <NUM> is in contact with a surface portion of a patient's body when the patient's area of interest is scanned. Dome <NUM> is typically formed of a material that provides an appropriate acoustical impedance match to the anatomical portion and/or permits ultrasound energy to be properly focused as the acoustical energy is projected into the anatomical portion. In some implementations, an acoustic gel or gel pads may be applied to a patient's skin over the region of interest (ROT) to provide an acoustical impedance match when dome <NUM> is placed against the patient's skin.

Dome <NUM> may enclose one or more ultrasound transceiver elements and one or more transducer elements (not shown in <FIG>). The transceiver elements transmit ultrasound energy outwardly from the dome <NUM>, and receive acoustic reflections or echoes generated by internal structures/tissue within the anatomical portion. 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 dome <NUM> by a motor to provide different scan directions with respect the transmissions of ultrasound signals by the transceiver elements. Alternatively, the transducer elements may be stationary with respect to probe <NUM> so that the selected anatomical region may be scanned by selectively energizing the elements in the array.

In an exemplary implementation, the scanning protocol of system <NUM> is configurable. For example, scanning system <NUM> may be configured to increase the scanning plane density, increase the scanning line numbers or change the rotational scanning to a fan scanning method to capture three-dimensional (3D) image data, depending on the particular target organ of interest, size of the target organ of interest, etc., as described in more detail below.

In some implementations, probe <NUM> may include a directional indicator panel <NUM> that includes a number of arrows that may be illuminated for initial targeting and guiding a user to scan a vessel, organ or other structure within the ROI. For example, in some implementations, if the vessel, organ or structure is centered from placement of probe <NUM> placed against the dermal surface at a first location of a patient, the directional arrows may not be illuminated. However, if the vessel, organ or structure is off-center, an arrow or set of arrows may be illuminated to direct the user to reposition probe <NUM> at a second or subsequent dermal location of the patient. In other implementations, the directional indicators may be presented on display <NUM> of base unit <NUM>.

The one or more transceivers located in probe <NUM> may include an inertial reference unit that includes an accelerometer and/or gyroscope positioned preferably within or adjacent to dome <NUM>. The accelerometer may be operable to sense an acceleration of the transceiver, preferably relative to a coordinate system, while the gyroscope may be operable to sense an angular velocity of the transceiver relative to the same or another coordinate system. Accordingly, the gyroscope may be of a conventional configuration that employs dynamic elements, or may be an optoelectronic device, such as an optical ring gyroscope. In one embodiment, the accelerometer and the gyroscope may include a commonly packaged and/or solid-state device. In other embodiments, the accelerometer and/or the gyroscope may include commonly packaged micro-electromechanical system (MEMS) devices. In each case, the accelerometer and gyroscope cooperatively permit the determination of positional and/or angular changes relative to a known position that is proximate to an anatomical region of interest in the patient. Using these sensors (e.g., accelerometer, gyroscope, etc.) may help scanning system <NUM> reconstruct a 3D aorta vessel by combining scans at different locations, such as when the entire length of the aorta cannot be fully recovered in a single scan.

Probe <NUM> may communicate with base unit <NUM> via a wired connection, such as via cable <NUM>. In other implementations, probe <NUM> may communicate with base unit <NUM> via a wireless connection (e.g., Bluetooth, WiFi, etc.). In each case, base unit <NUM> includes display <NUM> to allow a user to view processed results from an ultrasound scan, and/or to allow operational interaction with respect to the user during operation of probe <NUM>. For example, display <NUM> may 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 a user. For example, display <NUM> may provide instructions for positioning probe <NUM> relative to the selected anatomical portion of the patient. Display <NUM> may also display two-dimensional or three-dimensional images of the selected anatomical region.

To scan a selected anatomical portion of a patient, dome <NUM> may be positioned against a surface portion of patient that is proximate to the anatomical portion to be scanned. The user actuates the transceiver by depressing trigger <NUM>. In response, the transducer elements optionally position the transceiver, which transmits ultrasound signals into the body, and receives 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, system <NUM> transmits ultrasound signals in a range that extends from approximately about two megahertz (MHz) to approximately <NUM> or more MHz (e.g., <NUM>).

In one embodiment, probe <NUM> may be coupled to a base unit <NUM> that 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 unit <NUM> also 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, probe <NUM> may be a self-contained device that includes a microprocessor positioned within the probe <NUM> and software associated with the microprocessor to operably control the transceiver, and to process the reflected ultrasound energy to generate the ultrasound image. Accordingly, a display on probe <NUM> may be used to display the generated image and/or to view other information associated with the operation of the transceiver. For example, the information may include alphanumeric data that indicates a preferred position of the transceiver prior to performing a series of scans. In other implementations, the transceiver may be coupled to a local or remotely-located 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 also includes software to process information transferred from the transceiver so that an image of the scanned anatomical region may be generated.

<FIG> is a block diagram of functional logic components implemented in system <NUM> in accordance with an exemplary implementation. Referring to <FIG>, system <NUM> includes a data acquisition unit <NUM>, a vessel/organ identification unit <NUM>, a segmentation unit <NUM>, and post-processing unit <NUM>. In an exemplary implementation, data acquisition unit <NUM> may be part of probe <NUM> and the other functional units (e.g., vessel/organ identification unit <NUM>, segmentation unit <NUM>, and post-processing unit <NUM>) may be implemented in base unit <NUM>. 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 probe <NUM> and base unit <NUM> (e.g., accessible via a wireless connection to the Internet or to a local area network within a hospital, etc.). For example, probe <NUM> may transmit echo data and/or image data to a processing system via, for example, a wireless connection (e.g., WiFi or some other wireless protocol/technology) that is located remotely from probe <NUM> and base unit <NUM>.

As described above, probe <NUM> may include one or more transceivers that produces ultrasound signals, receives echoes from the transmitted signals and generates B-mode image data based on the received echoes. In an exemplary implementation, data acquisition unit <NUM> obtains data associated with multiple scan planes corresponding to the region of interest in a patient. For example, probe <NUM> may receive echo data that is processed by data acquisition unit <NUM> to generate two-dimensional (2D) B-mode image data to determine a size of the abdominal aorta and/or the size of an aneurysm in abdominal aorta. In other implementations, probe <NUM> may receive echo data that is processed to generate three-dimensional (3D) image data that can be used to determine the size of the abdominal aorta.

Vessel/organ identification unit <NUM> may perform pre-processing of an image and detect if a vessel or organ is present within a region of interest based on, for example, differentiation of pixel intensity (e.g., as scanned and collected by data acquisition unit <NUM>). As examples of pre-processing, vessel/organ identification unit <NUM> may apply noise reduction, adjust the aspect ratio of the raw B-mode image, and/or apply a scan conversion. As an example of vessel identification, in a 2D image, a blood carrying vessel may be identified as a dark region within an area of lighter-shaded pixels, where the lighter-shaded pixels typically represent body tissues. In another implementation, vessel/organ identification unit <NUM> may include artifact detection logic to detect particular structures adjacent the aorta, similar to that used in bladder scanning.

Segmentation unit <NUM> may receive data from data acquisition unit <NUM> and/or vessel/organ identification unit <NUM> and apply image processing using a 3D vascular shape model to segment the abdominal aorta. The 3D vascular shape model may include simulated 3D AAA shapes derived from human samples. An intensity model may include ultrasound image brightness information derived from human samples. In one implementation, segmentation unit <NUM> may apply a flexible 3D vascular shape model to a target 3D image. For example, as described in more detail below, segmentation unit <NUM> may fit a 3D vascular shape to a target image data set by minimizing one of several possible energy functions.

Post processing unit <NUM> includes logic to identify a size of an abdominal aorta that includes an aneurysm located in the abdominal aorta, as well as identify the size (e.g., diameter) and centerline of the aneurysm. For example, post processing unit <NUM> can provide a 3D reconstruction function to fully construct the aorta structure by combining all segmentation results associated with received echo data. In this manner, the measurement of the aorta diameter will be more accurate as compared to using conventional 2D imaging, as described in detail below.

The exemplary configuration illustrated in <FIG> is provided for simplicity. System <NUM> may include more or fewer logic units/devices than illustrated in <FIG>. For example, system <NUM> may include multiple data acquisition units <NUM> and multiple processing units that process the received data. In addition, system <NUM> may 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 in a region of interest. Furthermore, while illustrations and descriptions herein primarily refer to blood vessel applications (e.g., identifying an abdominal aorta and/or an aneurism within the abdominal aorta), other embodiments may be applied to detecting boundaries of organs, such as the bladder, prostate/kidney boundary, thyroid, etc..

<FIG> illustrates an exemplary data acquisition unit <NUM> used to obtain ultrasound image data. Referring to <FIG>, data acquisition unit <NUM> may include a single transducer element coupled to two rotational motors. In this implementation, ultrasound probe <NUM> may include a base <NUM> connected to dome <NUM>, a theta motor <NUM>, a spindle <NUM>, a phi motor <NUM>, and a transducer bucket <NUM> with a transducer <NUM>. Theta motor <NUM>, phi motor <NUM>, transducer bucket <NUM> and/or transducer <NUM> may include wired or wireless electrical connections that electrically connect theta motor <NUM>, phi motor <NUM>, transducer bucket <NUM> and/or transducer <NUM> to base unit <NUM> via cable <NUM> (not shown in <FIG>).

Base <NUM> may house theta motor <NUM> and provide structural support to ultrasound probe <NUM>. Base <NUM> may connect to dome <NUM> (connection not shown in <FIG>) and may form a seal with dome <NUM> to protect the components of ultrasound probe <NUM> from the external environment. Theta motor <NUM> may rotate spindle <NUM> with respect to base <NUM> in a longitudinal direction with respect to transducer <NUM>, by rotating around a vertical axis referred to herein as a theta (θ) rotational axis <NUM>. Spindle <NUM> may terminate in a shaft <NUM> and phi motor <NUM> may be mounted onto shaft <NUM>. Phi motor <NUM> may rotate around an axis orthogonal to the theta rotational axis <NUM> around a horizontal axis referred to herein as a phi (<NUM>) rotational axis <NUM>. Transducer bucket <NUM> may be mounted to phi motor <NUM> and may move with phi motor <NUM>.

Transducer <NUM> may be mounted to transducer bucket <NUM>. Transducer <NUM> may include a piezoelectric transducer, a capacitive transducer, and/or another type of ultrasound transducer. Transducer <NUM>, along with transceiver circuitry associated with transducer <NUM>, may convert electrical signals to ultrasound signals at a particular ultrasound frequency or range of ultrasound frequencies, may receive reflected ultrasound signals (e.g., echoes, etc.), and may convert the received ultrasound signals to electrical signals. Transducer <NUM> may transmit and receive ultrasound signals in a signal direction <NUM> that is substantially perpendicular to the surface of transducer <NUM>.

Signal direction <NUM> may be controlled by the movement of phi motor <NUM> and the orientation of phi motor <NUM> may be controlled by theta motor <NUM>. For example, phi motor <NUM> may rotate back and forth across an angle that is less than <NUM> degrees (e.g., <NUM> degrees) to generate ultrasound image data for a particular plane and theta motor <NUM> may rotate to particular positions to obtain ultrasound image data for different planes.

In an aiming mode, theta motor <NUM> may remain stationary while phi motor <NUM> rotates back and forth to obtain ultrasound image data for a particular aiming plane. In the aiming mode, theta motor <NUM> may move back and forth between multiple aiming planes and phi motor <NUM> may rotate back and forth to obtain ultrasound image data. As an example, theta motor <NUM> may move between two orthogonal planes while the aiming mode is selected. As another example, theta motor <NUM> may sequentially rotate through three planes offset by <NUM> degrees to each other during the aiming mode.

In a 3D scan mode, theta motor <NUM> may cycle through a set of planes (or "slices") one or more times to obtain a full 3D scan of an area of interest. Higher scan resolution may be obtained by using more scanning planes. Thus, in contrast with a conventional <NUM>-plane scan, implementations described herein may use a set of <NUM> planes to achieve resolutions that support the shape fitting methods described herein. In other implementation, more or fewer planes than <NUM> may be used. In each particular plane of the set of planes, phi motor <NUM> may rotate to obtain ultrasound image data for the particular plane. The movement of theta motor <NUM> and phi motor <NUM> may be interlaced in the 3D scan motor. For example, the movement of phi motor <NUM> in a first direction may be followed by a movement of theta motor <NUM> from a first plane to a second plane, followed by the movement of phi motor <NUM> in a second direction opposite to the first direction, followed by movement of theta motor <NUM> from the second plane to a third plane, etc. Such interlaced movement may enable ultrasound probe <NUM> to obtain smooth continuous volume scanning as well as improve the rate at which the scan data is obtained.

In addition, theta motor <NUM> and phi motor <NUM> can be configured to increase the scanning line numbers, change the rotational scanning to a "fan scanning" method, when the entire aorta cannot be captured via a first set of scan planes and a first set of reconstructed slices, as illustrated in <FIG>. For example, <FIG> illustrates a scenario in which an initial ultrasound cone <NUM>-<NUM> from a rotational scan did not capture the complete length of the aorta based on the length of the aorta. In this case, theta motor <NUM> and phi motor <NUM> can modify the rotational angles associated with transducer <NUM> to capture and evaluate vascular structures quantitatively based on cross-sectional slices to capture additional data so that the entire aorta structure can be analyzed. The subsequent ultrasound cone <NUM>-<NUM> from this fan scanning may capture a larger portion of the vascular structure than ultrasound cone <NUM>-<NUM>.

In another implementation, image acquisition unit <NUM> may capture additional data (e.g., beyond the scope of a single ultrasound cone <NUM>-<NUM>) by stitching together scans from multiple ultrasound cones to acquire a larger target image. <FIG> is a schematic of probe <NUM> collecting data using two ultrasound cones <NUM>-<NUM> and <NUM>-<NUM> spanning a target image (e.g., a patient's abdominal aorta). Images from ultrasound cones <NUM>-<NUM> and <NUM>-<NUM> may be obtained sequentially, and image acquisition unit <NUM> may stitch together or combine images/views from multiple slices of each ultrasound cone to construct a complete view of the target image.

As shown in <FIG>, in still another implementation, image acquisition unit <NUM> may use a tracking system <NUM> to control and/or monitor a relative position of a probe, such as probe <NUM>. <FIG> provides a simplified side-view illustration of probe <NUM> mounted on tracking system <NUM>. Tracking system may include a rotatable probe holder <NUM> mounted within a track <NUM>. According to one embodiment, tracking system <NUM> may move probe <NUM> along track <NUM>, monitoring a track position and rotation of probe holder <NUM> as an ultrasound cone <NUM>-<NUM> moves over an area of interest (e.g., above a patient's abdominal aorta). Tracking system <NUM> may monitor the location of probe <NUM> along track <NUM> using mechanical index tracking, electromagnetic tracking, optical tracking, or other sensors. In another implementation, an articulated arm may be used in place of track <NUM>. Using tracking system <NUM>, images from ultrasound cone <NUM>-<NUM> may be stitched together or combined based on the relative position/orientation of probe <NUM> to construct a complete view of a target image.

<FIG> provides a simplified illustration of C-mode image planes. C-mode images may generally include a representation oriented perpendicular to typical B-mode scan planes, for example. In one implementation, a C-mode image may include a cross-sectional image generated from ultrasound data of rotational scan planes at a particular depth. Thus, data acquisition unit <NUM> may use image data from different depths in each B-mode scan plane (or slice) to generate a C-mode image. The C-mode may be more representative of a portion of the abdominal aorta than the actual whole of the length of the aorta. The targeting image may be more of a binary image showing the lines and spaces that are inside the aorta versus those that are outside of the aorta. The C-mode acquired projection image can yield abdominal aorta information not confined to simply one a single plane parallel to the transducer surface, but multiple planes denoted as C-mode image planes.

Systems and methods described herein are described primarily in the context of image data obtained from an electro-mechanical probe performing rotational scanning. However, in other implementations, other types of probes may be used. For example, a matrix probe, a freehand magnetic probe, or a freehand optical probe may also be used to obtain 3D image data.

<FIG> illustrates an exemplary configuration of a device <NUM>. Device <NUM> may correspond to, for example, a component of data acquisition unit <NUM>, vessel/organ identification unit <NUM>, segmentation unit <NUM>, and/or post processing unit <NUM>. Device <NUM> may also correspond to elements in <FIG>, such as base unit <NUM>. Referring to <FIG>, device <NUM> may include bus <NUM>, processor <NUM>, memory <NUM>, input device <NUM>, output device <NUM> and communication interface <NUM>. Bus <NUM> may include a path that permits communication among the elements of device <NUM>.

Processor <NUM> may include one or more processors, microprocessors, or processing logic that may interpret and execute instructions. Memory <NUM> may include a random access memory (RAM) or another type of dynamic storage device that may store information and instructions for execution by processor <NUM>. Memory <NUM> may also include a read only memory (ROM) device or another type of static storage device that may store static information and instructions for use by processor <NUM>. Memory <NUM> may further include a solid state drive (SDD). Memory <NUM> may also include a magnetic and/or optical recording medium (e.g., a hard disk) and its corresponding drive.

Input device <NUM> may include a mechanism that permits a user to input information to device <NUM>, such as a keyboard, a keypad, a mouse, a pen, a microphone, a touch screen, voice recognition and/or biometric mechanisms, etc. Output device <NUM> may include a mechanism that outputs information to the user, including a display (e.g., a liquid crystal display (LCD)), a printer, a speaker, etc. In some implementations, a touch screen display may act as both an input device and an output device.

Communication interface <NUM> may include one or more transceivers that device <NUM> uses to communicate with other devices via wired, wireless or optical mechanisms. For example, communication interface <NUM> may include one or more radio frequency (RF) transmitters, receivers and/or transceivers and one or more antennas for transmitting and receiving RF data via a network. Communication interface <NUM> may also include a modem or an Ethernet interface to a LAN or other mechanisms for communicating with elements in a network.

The exemplary configuration illustrated in <FIG> is provided for simplicity. It should be understood that device <NUM> may include more or fewer devices than illustrated in <FIG>. In an exemplary implementation, device <NUM> performs operations in response to processor <NUM> executing sequences of instructions contained in a computer-readable medium, such as memory <NUM>. A computer-readable medium may be defined as a physical or logical memory device. The software instructions may be read into memory <NUM> from another computer-readable medium (e.g., a hard disk drive (HDD), SSD, etc.), or from another device via communication interface <NUM>. Alternatively, hard-wired circuitry, such as application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), etc., may be used in place of or in combination with software instructions to implement processes consistent with the implementations described herein.

Referring again to <FIG>, segmentation unit <NUM> may receive data from data acquisition unit <NUM> and/or vessel/organ identification unit <NUM> and analyze the data using 3D abdominal aorta segmentation based on a 3D vascular shape model and intensity model. Segmentation unit <NUM> may then provide the identified shape to post processing unit <NUM> for centerline extraction and maximum diameter measurement. In contrast with previous ultrasound based segmentation methods that analyze data on a pixel-by-pixel basis, systems and methods based on the 3D shape model described herein are more resilient to ultrasound noise and other artifacts. The systems and methods can also provide a more reliable and accurate diameter measurement method based on the resultant 3D structure.

<FIG> is a flow diagram illustrating exemplary processing <NUM> associated with identifying a target of interest, as well as identifying parameters or elements associated with the target of interest. Processing may begin with a user operating probe <NUM> to scan a target/region of interest. In this example, assume that the target is the abdominal aorta. It should be understood that features described herein may be used to identify other vessels, organs or structures within the body.

In an exemplary implementation, a 3D shape model may be defined for an abdominal aorta aneurysm (block <NUM>). Generally, according to an exemplary implementation, the 3D vascular shape model can be used to represent a patient's real aorta for quantitative analysis purposes. The simulated 3D shape model may be defined based on data from multiple human abdominal aortas. Possible variations can then be integrated into the shape model. For example, as shown in <FIG>, a simulated 3D AAA shape model may be developed based on human abdominal aorta characteristics. Generally, the 3D AAA shapes in <FIG> may include a 3D tubes <NUM> and/or 3D balls <NUM> in combination representing an aneurysm.

<FIG> represent six simulated 3D data sets that may be used for a flexible shape model. Each 3D data set includes a top view (anterior-posterior, illustrated in the upper left frame), side view (transverse, illustrated in the upper right frame), and end view (illustrated in the lower left frame) of a simulated AAA structure. <FIG> represents a simulation shape <NUM> with a normal aorta having no aneurysm. <FIG> represents a simulation shape <NUM> with an aorta having a spherical aneurysm along a centerline of the aorta. <FIG> represents a simulation shape <NUM> with an aorta having a double-spherical aneurysm along the centerline of the aorta. <FIG> represents a simulation shape <NUM> with an aorta having a spherical aneurysm off-center from a centerline of the aorta. <FIG> represents a simulation shape <NUM> with an aorta having a double-spherical aneurysm off-center from a centerline of the aorta. <FIG> represents a simulation shape <NUM> with an aorta having a tube-shaped aneurysm. In the model, tube diameters can be different for each tube, and tube diameters can be different in different tubes. Each of simulation shapes <NUM>-<NUM> may be derived from patient data (e.g., ultrasound data, CT scan data, MRI data, etc.) of normal and AAA conditions. Furthermore, ball diameters can be different in different tubes; and each ball can be a regular (e.g., spherical) shape or elongated. While six simulation shapes <NUM>-<NUM> are shown in <FIG>, in other implementations more or fewer simulation shapes may be used for the flexible shape model.

<FIG> and <FIG> provide a simplified illustration a flexible shape model <NUM>. More particularly, <FIG> and <FIG> illustrate how a simulation shape (e.g., simulation shape <NUM>) may be morphed for shape fitting analysis. Other simulation shapes (e.g., <NUM>-<NUM> and <NUM>) may be modified or modified in a similar manner. In the example of <FIG> and <FIG>, the weighted value of a double-spherical aneurysm off-center from a centerline (e.g., simulation shape <NUM>, corresponding to Eigen Shape <NUM> of <FIG> and <FIG>) is increased from a relatively small weight in <FIG> to a larger weight in <FIG> (as illustrated via the slide bar located on the right side of <FIG> and <FIG>. According to an exemplary implementation, the flexible shape model can "learn" from the training patterns of simulation shapes <NUM>-<NUM>. The flexible shape model may provide a flexible representation without using specific contours or a mesh. The flexible 3D shape model is thus more resistant to noise or shadow than conventional techniques in 2D space. While <FIG> and <FIG> show flexible shape model <NUM> that may be manually adjusted using slide bars. In other implementations, flexible shape model <NUM> may be selected/adjusted using a processor (e.g., processor <NUM>) in probe <NUM> or base unit <NUM>.

Referring back to <FIG>, image data of a patient may be acquired and image enhancement applied (block <NUM>). For example, a user may press trigger <NUM> and the transceiver (e.g., associated with transducer <NUM>) included in probe <NUM> transmits ultrasound signals and acquires B-mode data associated with echo signals received by probe <NUM>. In one implementation, data acquisition unit <NUM> may transmit ultrasound signals on <NUM> different planes through the abdominal aorta and generate <NUM> B-mode images corresponding to the <NUM> different planes. In this implementation, the data may correspond to 2D image data. In other implementations, data acquisition unit <NUM> may generate 3D image data. For example, as discussed above with respect to <FIG>, data acquisition unit <NUM> may perform interlaced scanning to generate 3D images to capture the entire aorta structure. In each case, the number of transmitted ultrasound signals/scan planes may vary based on the particular implementation.

Probe <NUM> or base unit <NUM> may also apply a noise reduction process to the ultrasound image data. For example, data acquisition unit <NUM> may receive a B-mode ultrasound image from probe <NUM> and 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 when performing an abdominal aorta scanning application, a scan conversion and/or machine learning can also be applied to make the abdominal aorta shape closer to the expected or actual shape of an abdominal aorta (e.g., elongated as opposed to round).

Base unit <NUM> (e.g., vessel/organ identification unit <NUM>) may detect a region of interest, such as detect a concentration of dark pixels within the ultrasound image. The concentration of dark pixels typically corresponds to the lumen of the abdominal aorta, which carries the blood through the abdominal aorta. For example, <FIG> illustrates a series of images from a <NUM>-slice rotational scan of a AAA phantom that may be generated by data acquisition unit <NUM>, which shows the phantom <NUM> as a concentration of dark pixels in the center of each B-mode scan plane. While phantom <NUM> is used for purposes of illustration, systems and methods described herein apply equally well to human aorta/tissues. Vessel/organ identification unit <NUM> may identify the area of dark pixels as the lumen. In another implementation, base unit <NUM> may include a user interface (e.g., a touch screen, tablet, mouse, etc.) to allow an operator to indicate or select a vessel or organ of interest, such as selecting the abdominal aorta lumen from one of the scan images via display <NUM>.

Still referring to <FIG>, once the abdominal aorta lumen is identified, vessel/organ identification unit <NUM> may generate a 3D target image of the abdominal aorta (block <NUM>). For example, 3D image data may be compiled based on B-mode scans. In one implementation, the rotational scan B-mode images from <FIG> may be used to generate 3D image data (e.g., with top, side, and end views) shown in <FIG> includes exemplary images of a cross sectional end view <NUM>, a longitudinal section <NUM>, and a C-mode representation <NUM> of the AAA phantom <NUM> of <FIG>. The renderings of phantom <NUM> in sections <NUM>, <NUM>, and <NUM> may be used together as a target 3D image data set <NUM> to be matched by the flexible shape model (e.g., flexible shape model <NUM>).

As further shown in <FIG>, the flexible 3D aorta model may be fit onto the target 3D image (block <NUM>). For example, segmentation unit <NUM> may overlay flexible shape model <NUM> onto target 3D image data set <NUM> to determine a best fit. <FIG> illustrate a shape fitting procedure <NUM>. <FIG> is an illustration of portions of sections <NUM>, <NUM>, and <NUM> from target 3D image data set <NUM> shown without overlays and aligned along center lines <NUM>, <NUM>. <FIG> is an illustration of an initial configuration of flexible shape model <NUM>-<NUM> overlaid on target 3D image data set <NUM>.

In the example of <FIG>, simulation shape <NUM> (e.g., a double-spherical aneurysm off-center from a centerline) may be initially selected as a starting image. Selection of the initial simulation shape may be performed by segmentation unit <NUM>, provided by an operator (e.g., using display <NUM>), or included as a default selection. In one implementation, fitting flexible shape model <NUM> to target 3D image data set <NUM> can be defined as minimizing an energy function.

One or more different approaches to minimizing an energy functions may be used to fit shape model <NUM> to a target 3D image data set (e.g., target image data set <NUM>). For example, resilient backpropagation (rprop) is a learning heuristic for supervised learning in feedforward artificial neural networks. Rprop takes into account only the sign of the partial derivative over all patterns (not the magnitude), and acts independently on each "weight. " For each weight, if there was a sign change of the partial derivative of the total error function compared to the last iteration, the update value for that weight is multiplied by a factor η-, where η- < <NUM>. If the last iteration produced the same sign, the update value is multiplied by a factor of η+, where η+ > <NUM>. The update values are calculated for each weight in the above manner, and finally each weight is changed by its own update value, in the opposite direction of that weight's partial derivative, so as to minimize the total error function. In one implementation, η+ is empirically set to <NUM> and η- to <NUM>.

An energy function that may be used to fit shape model <NUM> to a target 3D image data set is a data-driven statistical shape model. The data-driven statistical shape model may be more robust to the initialization and robust to noise during the segmentation task. Given a set of aligned training shapes {ϕi}i=<NUM>. N, each of the shapes can be represented by their corresponding shape vector N, In this notation, the goal of statistical shape learning is to infer a statistical distribution P(α) from the training samples.

According to implementations used herein, the data-driven statistical shape model may infer a uniform density as shown in the sample of <FIG>, a Gaussian distribution as shown in the sample of <FIG>, or a kernel density as shown in the sample of <FIG>. For uniform distribution, P(α) = constant. For Gaussian distribution: <MAT> For Kernel distribution: <MAT>.

In the example of <FIG>, a modified flexible shape model <NUM>-<NUM> is overlaid on target 3D image data set <NUM>. For example, using a data-driven statistical shape model flexible shape model <NUM>-<NUM> may be conformed to the 3D shape of the target image (e.g., phantom <NUM>). <FIG> shows an energy changing curve <NUM> that may correspond to multiple iterations to minimize the energy function for flexible shape model <NUM>-<NUM> over target 3D image data set <NUM>.

Returning to <FIG>, the best fit flexible shape model may be stored as a segmentation result (bock <NUM>) and AAA measurements may be calculated using the stored segmentation result (block <NUM>). For example, the best fit overlay (e.g., flexible shape model <NUM>-<NUM>) corresponding to target 3D image data set <NUM> may be stored for quantitative analysis in an AAA evaluation by post processing unit <NUM>. <FIG> illustrates a 3D (e.g., solid) model <NUM> that may be generated from a best-fit flexible shape model <NUM>-<NUM>. With abdominal aorta segmentation available in the form of 3D model <NUM>, post processing unit <NUM> may determine size information for both the aorta and AAA, such as a centerline of the aorta <NUM>, the diameter of the aorta <NUM>, the maximum diameter of the aneurysm <NUM>, and the volume of the aneurysm. Since the AAA is not a tubular structure, the volume of the AAA and/or the ratio of the AAA area to the overall aorta may be a useful quantitative measure. In other implementations, post processing unit <NUM> may also determine the total area of the aorta and the diameter of the AAA. In each case, post processing unit <NUM> may output the size and/or area information via, for example, display <NUM> or via a display <NUM> on probe <NUM>. Using model <NUM> for measurement and analysis enables post processing unit <NUM> to more easily identify the centerline of the aorta/AAA and determine the correct orientation for measuring the maximum abdominal aorta diameter (e.g., perpendicular to the centerline).

As described above, system <NUM> may include a probe configured to transmit ultrasound signals directed to a target blood vessel and receive echo information associated with the transmitted ultrasound signals. System <NUM> may also includes at least one processing device configured to process the received echo information and generating a three-dimensional ultrasound image of the target blood vessel; obtain a flexible three-dimensional vascular model corresponding to the target blood vessel; identify a best-fit of the flexible three-dimensional vascular model onto the three-dimensional target image; store the best fit of the flexible three-dimensional vascular model as a segmentation result; and calculate, based on the segmentation result, measurements for the target blood vessel.

The foregoing description of exemplary implementations provides illustration and description, but is not intended to be exhaustive or to limit the embodiments to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the embodiments.

For example, features have been described above with respect to identifying a target of interest, such as a patient's abdominal aorta and AAA to estimate the size of the target (e.g., the aorta and/or the AAA). In other implementations, other vessels, organs or structures may be identified, and sizes or other parameters associated with the vessels, organs or structures may be estimated. For example, the processing described herein may be used to identify and display a bladder, prostate gland, a kidney, a uterus, ovaries, a heart, etc., as well as particular features associated with these targets, such as area-related measurements.

Further, while series of blocks have been described with respect to <FIG>, the order of the acts may be different in other implementations. Moreover, non-dependent blocks may be implemented in parallel.

It will be apparent that various features described above may be implemented in many different forms of software, firmware, and hardware in the implementations illustrated in the figures. The actual software code or specialized control hardware used to implement the various features is not limiting. Thus, the operation and behavior of the features were described without reference to the specific software code - it being understood that one of ordinary skill in the art would be able to design software and control hardware to implement the various features based on the description herein.

Further, certain portions of the invention may be implemented as "logic" that performs one or more functions. This logic may include hardware, such as one or more processors, microprocessor, application specific integrated circuits, field programmable gate arrays or other processing logic, software, or a combination of hardware and software.

In the preceding specification, various preferred embodiments have been described with reference to the accompanying drawings. It will, however, be evident that various modifications and changes may be made thereto, and additional embodiments may be implemented, without departing from the broader scope of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative rather than restrictive sense.

Claim 1:
A system (<NUM>), comprising:
a probe (<NUM>) configured to:
transmit (<NUM>) ultrasound signals directed to a target blood vessel, and
receive (<NUM>) echo information associated with the transmitted ultrasound signals; and
at least one processing device (<NUM>) configured to:
process (<NUM>) the received echo information and generate a three-dimensional ultrasound image of the target blood vessel,
obtain (<NUM>) a flexible three-dimensional vascular model corresponding to the target blood vessel, wherein the flexible three-dimensional vascular model includes a statistical shape model derived from human samples and without using any mesh, and wherein the statistical shape model infers one of a statistical distribution,
perform (<NUM>) a shape fitting procedure of the flexible three-dimensional vascular model onto the three-dimensional ultrasound image of the target blood vessel, wherein the shape fitting procedure overlays the flexible three-dimensional vascular model onto the ultrasound image of the target blood vessel and morphs the flexible three-dimensional vascular model to match the ultrasound image of the target blood vessel,
store (<NUM>) a segmentation result based on the shape fitting procedure, and
calculate (<NUM>), based on the segmentation result, measurements for the target blood vessel.