Patent Description:
Acoustic (e.g., ultrasound) imaging systems are increasingly being employed in a variety of applications and contexts. For example, ultrasound imaging is being increasingly employed in the context of ultrasound-guided medical procedures.

Typically, in ultrasound-guided medical procedures the physician visually locates the current position of the needle tip (or catheter tip) in acoustic images which are displayed on a display screen or monitor. Furthermore, a physician may visually locate the current position of the needle on a display screen or monitor when performing other medical procedures. The needle tip generally appears as bright spot in the image on the display screen, facilitating its identification.

However, visualization of an interventional device, or devices, (e.g., surgical instrument(s), needle(s), catheter(s), etc.) employed in these procedures using existing acoustic probes and imaging systems is challenging in many cases. It has been shown that acoustic images may contain a number of artifacts caused by both within-plane (axial and lateral beam axes) and orthogonal-to-the-plane (elevation beam width) acoustic beam formation and it can be difficult to distinguish these artifacts from the device whose position is of interest.

To address these problems, special interventional devices, such as echogenic needles, with enhanced visibility are successfully on the market and provide some improvement at moderate extra cost.

However, due to noise, false echoes, and various other factors, consistently correct identification of the location of the interventional device in acoustic images remains a problem.

Documents <CIT>, <CIT> disclose known ultrasound imaging systems for visualization and localisation of an intervention device.

Accordingly, it would be desirable to provide an ultrasound system and a method which can provide enhanced acoustic imaging capabilities during interventional procedures. In particular it would be desirable to provide an ultrasound system and a method which can provide improved device tracking estimates during an interventional procedure.

In one aspect of the invention, An acoustic imaging instrument connectable to an acoustic probe having an array of acoustic transducer elements, the acoustic imaging instrument, when connected to the acoustic probe, configured to provide transmit signals to least some of the acoustic transducer elements to cause the array of acoustic transducer elements to transmit an acoustic probe signal to an area of interest, and further configured to produce acoustic images of the area of interest in response to acoustic echoes received from the area of interest in response to the acoustic probe signal, the acoustic imaging instrument including:.

In another aspect of the invention, a system comprises: an acoustic probe having an array of acoustic transducer elements; and an acoustic imaging instrument connected to the acoustic probe. The acoustic imaging instrument is configured to provide transmit signals to least some of the acoustic transducer elements to cause the array of acoustic transducer elements to transmit an acoustic probe signal to an area of interest, and is further configured to produce acoustic images of the area of interest in response to acoustic echoes received from the area of interest in response to the acoustic probe signal. The acoustic imaging instrument includes: a display device configured to display the acoustic images; a receiver interface configured to receive one or more sensor signals from at least one passive sensor disposed on a surface of an intervention device disposed in the area of interest, the one or more sensor signals being produced in response to the acoustic probe signal; and a processor. The processor is configured to ascertain, from the one or more sensor signals from the passive sensor, an estimated location of the passive sensor in the area of interest, by: identifying one or more candidate locations for the passive sensor based on localized intensity peaks in sensor data produced in response to the one or more sensor signals from the passive sensor, and using intra-procedural context-specific information to identify a one of the candidate locations which best matches the intra-procedural context-specific information as the estimated location of the passive sensor. The display device displays a marker in the acoustic images to indicate the estimated location of the passive sensor.

In some embodiments, the intra-procedural context-specific information includes at least one of: information identifying an anatomical structure where the sensor is expected to be located; information identifying a likely location of the intervention device in the acoustic images; and information identifying previous estimated locations of the sensor in previous ones of the acoustic images.

In some versions of these embodiments, the intra-procedural context-specific information includes the information identifying the anatomical structure where the sensor is expected to be located, and wherein the processor is configured to execute a region detection or segmentation algorithm to identify the anatomical structure where the sensor is expected to be located in the acoustic images.

In some versions of these embodiments, the intra-procedural context-specific information includes the information identifying the anatomical structure where the sensor is expected to be located, wherein the acoustic imaging instrument is configured to produce color Doppler images of the area of interest in response to one or more receive signals received from the acoustic probe, and wherein the processor is configured to identify the anatomical structure where the sensor is expected to be located by identifying blood flow in the color Doppler images.

In some versions of these embodiments, the intra-procedural context-specific information includes the information identifying a likely location of the intervention device in the acoustic images, and wherein the processor is configured to execute a region detection algorithm or segmentation algorithm to identify the likely location of the intervention device in the acoustic images.

In some versions of these embodiments, the intra-procedural context-specific information includes the information identifying the previous estimated locations of the sensor in previous ones of the acoustic images, and wherein the processor is configured to employ one of a state estimation filter applied to each current candidate location and the previous estimated locations of the sensor; a decomposition of all previous locations of the sensor to identify sensor motion trajectory and compare the sensor motion trajectory to each candidate location; a region of interest (ROI) spatial filter defined around an estimated location of the sensor in a previous frame and applied to each candidate location.

In some embodiments, the intra-procedural context-specific information includes: information identifying an anatomical structure where the sensor is expected to be located; information identifying a likely location of the intervention device in the acoustic images; and information identifying previous estimated locations of the sensor in previous ones of the acoustic images.

In some versions of these embodiments, identifying the one or more candidate locations for the passive sensor based on the localized intensity peaks in the one or more sensor signals at times corresponding to the candidate locations, includes: determining, for each candidate location, a weighted sum or other form of weighted integration of a match between the candidate location and each of: the information identifying the anatomical structure where the sensor is expected to be located; the information identifying the likely location of the intervention device in the acoustic images; and the information identifying the previous estimated locations of the sensor in the previous ones of the acoustic images; and selecting as the estimated location of the passive sensor a one of the candidate locations which has a greatest weighted sum or other form of weighted integration.

In another aspect of the invention, a computer program for performing a method is provided where the method comprises: producing acoustic images of an area of interest in response to one or more receive signals received from an acoustic probe in response to acoustic echoes received by the acoustic probe from the area of interest in response to an acoustic probe signal; receiving one or more sensor signals from a passive sensor disposed on a surface of an intervention device in the area of interest, the one or more sensor signals being produced in response to the acoustic probe signal; identifying one or more candidate locations for the passive sensor based on localized intensity peaks in sensor data produced in response to the one or more sensor signals from the passive sensor; using intra-procedural context-specific information to identify a one of the candidate locations which best matches the intra-procedural context-specific information as an estimated location of the passive sensor; displaying the acoustic images on a display device; and displaying on the display device a marker in the acoustic images to indicate the estimated location of the passive sensor.

In some versions of these embodiments, the intra-procedural context-specific information includes the information identifying the anatomical structure where the sensor is expected to be located, and wherein the method includes executing a region detection algorithm or segmentation algorithm to identify the anatomical structure where the sensor is expected to be located in the acoustic images.

In some versions of these embodiments, the intra-procedural context-specific information includes the information identifying the anatomical structure where the sensor is expected to be located, and the method includes: producing color Doppler images of the area of interest in response to the one or more receive signals received from the acoustic probe; and identifying the anatomical structure where the sensor is expected to be located by identifying blood flow in the color Doppler images.

In some versions of these embodiments, the intra-procedural context-specific information includes the information identifying the previous estimated locations of the sensor in previous ones of the acoustic images, and the method includes one of: applying a state estimation filter to each current candidate location and the previous estimated locations of the sensor; performing a decomposition of all previous locations of the sensor to identify sensor motion trajectory, and comparing the sensor motion trajectory to each candidate location; and applying a region of interest (ROI) spatial filter, defined around an estimated location of the sensor in a previous frame, to each candidate location.

In some versions of these embodiments, identifying the one of the candidate locations which best matches the intra-procedural context-specific information as the estimated location of the passive sensor includes: determining, for each candidate location, a weighted sum or other form of weighted integration of a match between the candidate location and each of the information identifying the anatomical structure where the sensor is expected to be located; the information identifying the likely location of the intervention device in the acoustic images; and the information identifying the previous estimated locations of the sensor in the previous ones of the acoustic images; and selecting as the estimated location of the passive sensor a one of the candidate locations which has a greatest weighted sum or other form of weighted integration.

In yet another aspect of the invention, an acoustic imaging instrument comprises: a receiver interface configured to receive one or more sensor signals from at least one passive sensor disposed on a surface of an intervention device which is disposed in an area of interest; and a processor. The processor is configured to ascertain from the one or more sensor signals an estimated location of the passive sensor in the area of interest, by: identifying one or more candidate locations for the passive sensor based on localized intensity peaks in sensor data produced in response to the one or more sensor signals from the passive sensor, and using intra-procedural context-specific information to identify a one of the candidate locations which best matches the intra-procedural context-specific information as the estimated location of the passive sensor. The processor is further configured to cause a display device to display the acoustic images and a marker in the acoustic images to indicate the estimated location of the passive sensor.

In some embodiments, identifying the one of the candidate locations which best matches the intra-procedural context-specific information as the estimated location of the passive sensor includes: determining, for each candidate location, a weighted sum or other means of weighted integration of a match between the candidate location and each of: the information identifying the anatomical structure where the sensor is expected to be located; the information identifying the likely location of the intervention device in the acoustic images; and the information identifying the previous estimated locations of the sensor in the previous ones of the acoustic images; and selecting as the estimated location of the passive sensor a one of the candidate locations which has a greatest weighted sum or other weighted integration.

In some embodiments, determining, for each candidate location, a weighted sum or other means of weighted combination of different information sources, the exact numerical method for combining the information sources, as well as the actual values of the weights, are determined through an empirical optimization. The optimization may be carried out for example on training data specific to the desired application.

In some embodiments, a measure of the certainty or uncertainty of the final output may be additionally provided.

The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which preferred embodiments of the invention are shown. This invention may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided as teaching examples of the invention, where the scope of the invention is defined by the appended claims. Herein, when something is said to be "approximately" or "about" a certain value, it means within <NUM>% of that value.

<FIG> shows one example of an acoustic imaging system <NUM> which includes an acoustic imaging instrument <NUM> and an acoustic probe <NUM>. Acoustic imaging instrument <NUM> include a processor (and associated memory) <NUM>, a user interface <NUM>, a display device <NUM> and optionally a receiver interface <NUM>.

In various embodiments, processor <NUM> may include various combinations of a microprocessor (and associated memory), a digital signal processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), digital circuits and/or analog circuits. Memory (e.g., nonvolatile memory) associated with processor <NUM> may store therein computer-readable instructions which cause a microprocessor of processor <NUM> to execute an algorithm to control acoustic imaging system <NUM> to perform one or more operations or methods which are described in greater detail below. In some embodiments, a microprocessor may execute an operating system. In some embodiments, a microprocessor may execute instructions which present a user of acoustic imaging system <NUM> with a graphical user interface (GYI) via user interface <NUM> and display device <NUM>.

In various embodiments, user interface <NUM> may include any combination of a keyboard, keypad, mouse, trackball, stylus /touch pen, joystick, microphone, speaker, touchscreen, one or more switches, one or more knobs, one or more lights, etc. In some embodiments, a microprocessor of processor <NUM> may execute a software algorithm which provides voice recognition of a user's commands via a microphone of user interface <NUM>.

Display device <NUM> may comprise a display screen of any convenient technology (e.g., liquid crystal display). In some embodiments the display screen may be a touchscreen device, also forming part of user interface <NUM>.

In some embodiments, acoustic imaging instrument <NUM> may include receiver interface <NUM> which is configured to receive one or more electrical signals (sensor signals) from an external passive acoustic sensor, for example an acoustic receiver disposed at or near a distal end (tip) of an interventional device, as will be described in greater detail below, particularly with respect to <FIG>.

Of course it is understood that acoustic imaging instrument <NUM> may include a number of other elements not shown in <FIG>, for example a power system for receiving power from AC Mains, an input/output port for communications between processor <NUM> and acoustic probe <NUM>, a communication subsystem for communicating with other eternal devices and systems (e.g., via a wireless, Ethernet and/or Internet connection), etc..

Beneficially, acoustic probe <NUM> may include an array of acoustic transducer elements <NUM> (see <FIG>). At least some of acoustic transducer elements <NUM> receive transmit signals from acoustic imaging instrument <NUM> to cause the array of acoustic transducer elements <NUM> to transmit an acoustic probe signal to an area of interest, and receive acoustic echoes from the area of interest in response to the acoustic probe signal.

<FIG> illustrates one example embodiment of an interventional device <NUM> having an acoustic sensor (e.g., a passive acoustic sensor) <NUM> disposed at a distal end thereof. Although only one passive acoustic sensor <NUM> is shown for interventional device <NUM>, other embodiments of interventional devices may include two or more passive acoustic sensor(s) <NUM>.

As described in greater detail below, in some embodiments processor <NUM> of acoustic imaging instrument <NUM> may use one or more sensor signals received by receiver interface <NUM> from one or more passive acoustic sensors <NUM> disposed on interventional device <NUM> to track the location of interventional device in acoustic images produced from acoustic data produced by echoes received by acoustic probe <NUM>.

In various embodiments, interventional device <NUM> may comprise a needle, a catheter, a medical instrument, etc..

<FIG> illustrates example embodiment of a process of overlaying imaging produced from one or more sensor signals received from an acoustic sensor such as passive acoustic sensor <NUM> with an acoustic image produced from acoustic echoes received by an acoustic probe such as acoustic probe <NUM>.

As illustrated in <FIG>, acoustic probe <NUM> illuminates an area of interest <NUM> with an acoustic probe signal <NUM> and receives acoustic echoes received area of interest <NUM> in response to acoustic probe signal <NUM>. An acoustic imaging instrument (e.g., acoustic imaging instrument <NUM>) produces acoustic images <NUM> of area of interest <NUM> in response to acoustic echoes received from area of interest <NUM> in response to acoustic probe signal <NUM>. In particular, acoustic probe <NUM> may communicate one or more receive signals (electrical signals) to acoustic imaging instrument <NUM> in response to acoustic echoes received from area of interest <NUM> in response to acoustic probe signal <NUM>, and acoustic imaging instrument <NUM> may produce acoustic images <NUM> from the receive signal(s).

Meanwhile, a receiver interface (e.g., receiver interface <NUM>) receives one or more sensor signals from at least one passive acoustic sensor (e.g., passive acoustic sensor <NUM>) disposed on a surface of an intervention device (e.g., device <NUM>) disposed in area of interest <NUM>, the one or more sensor signals being produced in response to acoustic probe signal <NUM>. A processor (e.g., processor <NUM>) executes an algorithm to ascertain or determine, from the one or more sensor signals from passive acoustic sensor <NUM> an estimated location <NUM> of passive acoustic sensor <NUM> in area of interest <NUM>. Image <NUM> illustrates sensor data obtained by processor <NUM>, showing estimated location <NUM> of passive acoustic sensor <NUM>. For example, processor <NUM> may employ an algorithm to detect a maximum value or intensity peak in sensor data produced from the one or more sensor signals from passive acoustic sensor <NUM>, and may determine or ascertain that estimated location <NUM> of passive acoustic sensor <NUM> corresponds to the location of intensity peak in the sensor data. Then acoustic imaging instrument <NUM> may overlay the sensor data illustrated in image <NUM> with acoustic image <NUM> to produce an overlaid acoustic image <NUM> which includes a marker to identify estimated location <NUM> of passive acoustic sensor <NUM>.

<FIG> illustrates a process of identifying an estimated location <NUM> of passive acoustic sensor <NUM> in acoustic image <NUM> when there is only one intensity peak in the sensor data. As shown in <FIG>, image <NUM> illustrates sensor data obtained by processor <NUM> from the sensor signal(s) output by passive acoustic sensor <NUM>, and the single intensity peak is identified as the estimated location <NUM> of passive acoustic sensor <NUM>. The sensor data is overlaid with the acoustic image data to produce the overlaid acoustic image <NUM>, and a marker is added to indicate estimated location <NUM> of passive acoustic sensor 210in the overlaid acoustic image <NUM>.

However, as explained above, often the location of passive acoustic sensor <NUM> in the sensor data is not clear from the sensor data alone. Multiple intensity peaks may occur due to noise and various acoustic aberrations or artifacts. For example, if there is a segment of bone in the imaging plane, an ultrasound beam can bounce off the bone and insonify passive acoustic sensor <NUM> (an indirect hit), producing a signal that arrives later in time (and that can often be stronger) than the direct insonification. In another example, in tracked needle applications where interventional device <NUM> is a needle, an ultrasound beam can intersect with the needle shaft and travel down the shaft to passive acoustic sensor <NUM>, resulting in passive acoustic sensor <NUM> being insonified earlier in time than the direct hit (due to the higher sound speed in the needle shaft compared to that in tissue). In yet another example, random electromagnetic interference (EMI) can cause the system to choose a noise spike as the estimated position of passive acoustic sensor <NUM>.

<FIG> illustrates an image <NUM> showing multiple candidate locations (<NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM> and <NUM>-<NUM>) of passive acoustic sensor <NUM> based on localized intensity peaks in one or more sensor signals produced by passive acoustic sensor <NUM> at times corresponding to the candidate locations.

In this situation, it is not immediately apparent what the best estimated location of passive acoustic sensor <NUM> is. Indeed, as explained above, it is possible that a "false" intensity peak produced by a reflection or travelling of the shaft of interventional device <NUM> could be stronger than the intensity peak produced by direct insonification of passive acoustic sensor <NUM>, so simply choosing the greatest intensity peak will often produce a bad estimate for the sensor location.

However, the inventors have appreciated that it is often possible for a processor (e.g., processor <NUM>) of an acoustic imaging instrument and system (e.g., acoustic imaging instrument <NUM> and acoustic imaging system <NUM>) to identify the best estimated location of a passive acoustic sensor (e.g., passive acoustic sensor <NUM>) disposed on the surface of an interventional device (e.g., interventional device <NUM>), from among a number of candidate locations, during an interventional procedure by factoring into account intra-procedural context-specific information which is available to the processor. Here, intra-procedural context-specific information refers to any data which may be available to the processor pertaining to the context of a specific intervention procedure at the time that the processor is attempting to determine the location of the passive acoustic sensor within the area of interest which is being insonified by the acoustic probe. Such information may include, but is not limited to, the type of interventional device whose sensor is being tracked, known size and/or shape characteristics of the interventional device, known anatomical characteristics within the area of interest where the sensor may be located, a surgical or other procedural plan detailing an expected path for the interventional device and/or sensor to follow within the area of interest during the current intervention procedure; previous known paths, locations, and/or orientations of the interventional device and/or sensor during the current intervention procedure; etc..

<FIG> illustrates one example embodiment of a method of improving sensor tracking estimates in interventional acoustic imaging by employing intra-procedural context-specific information.

<FIG> shows an image <NUM> of sensor data produced in response to one or more sensor signals <NUM> from passive acoustic sensor <NUM>, as illustrated in <FIG> above. One can see several candidate locations for passive acoustic sensor <NUM>, indicated by the bright spots in image <NUM>. Without additional data, it is a difficult if not impossible problem to identify the best estimated location for passive acoustic sensor <NUM> just from the sensor data of image <NUM>. <FIG> illustrates how several different types of intra-procedural context-specific information can be employed as constraints on sensor tracking estimates, eliminating some candidate locations as possibilities and/or selecting one candidate location as the best estimated location.

Consider first the top row of <FIG>. For endovascular procedures, acoustic imaging system <NUM> can be operated in Color Doppler mode and the presence of flow is indicative of a blood vessel. Alternately, if acoustic imaging system <NUM> is operated in B-mode, processor <NUM> can run segmentation or vessel object detection routines to identify the location and boundaries of the vessel. Since the tracked wire/catheter is being navigated in the vessel, any intensity peaks or "bright spots" in the sensor data matrix that are outside the blood vessel can be considered to be artifacts (except in the rare cases of vessel perforation by a wire/catheter). Processor <NUM> may employ standard scan conversion routines to convert from B-mode/Color Doppler space to sensor data space, and the intensity peaks or "bright spots" in overlaid acoustic image <NUM> that are outside the blood vessel can be suppressed or eliminated as possible estimated locations for passive acoustic sensor <NUM>.

Consider next the middle row of <FIG>. For needle interventions, the estimated sensor location has to be located on the needle shaft. Processor <NUM> has identified the needle shaft in acoustic image <NUM>-<NUM>. This constraint can, thus, be used to weed out incorrect sensor position estimates in overlaid acoustic image <NUM>. Even in cases where the needle shaft is not visible in the acoustic image, the general position and orientation of the needle can be approximately known during the needle insertion. Sensor position estimates that are far away from the approximated needle position and orientation may be weeded out or penalized compared to sensor position estimates that are closer.

Finally, consider the bottom row of <FIG>. The location of passive acoustic sensor <NUM> in the current frame or acoustic image <NUM>-<NUM> cannot be inconsistent with history. In other words, if passive acoustic sensor <NUM> has been progressing smoothly along a certain trajectory, it should not suddenly appear in a totally different location that is not along the path or near the location where it was found in the immediately preceding frame(s) or acoustic image(s). Thus sensor position estimates that are far away from the previous trajectory of the needle may be weeded out or otherwise penalized compared to sensor estimates that are more closely in line with the previous trajectory.

In various embodiments, one or more or all of the intra-procedural context-specific information-based constraints illustrated in the top, middle, and bottom rows of <FIG> may be employed to ascertain estimated location <NUM> of passive acoustic sensor <NUM>. In some embodiments, a weighted combination of constraints may be employed. In particular, in some embodiments, this may include determining, for each candidate location <NUM> of passive acoustic sensor <NUM> identified in the sensor data, a weighted sum of matches between the candidate location <NUM> and each of: information identifying an anatomical structure where passive acoustic sensor <NUM> is expected to be located; information identifying the likely location of intervention device <NUM> in acoustic images <NUM>; and information identifying the previous estimated locations <NUM> of passive acoustic sensor <NUM> in previous acoustic images <NUM>. The candidate location <NUM> which has the greatest weighted sum or other form of weighted combination may be selected as estimated location <NUM> of passive acoustic sensor <NUM>. A marker identifying estimated location <NUM> may be provided in acoustic images <NUM> which are displayed on display device <NUM> to a user or operator of acoustic imaging system <NUM>, including for example to a physician performing an interventional procedure using interventional device <NUM>. In some embodiments, thresholding may be employed such that if none of candidate locations <NUM> provides a good enough match to one, more, or all of the various intra-procedural context-specific information-based constraints, then acoustic imaging system <NUM> can decline to select and display a marker for an estimated location <NUM> of passive acoustic sensor <NUM>.

In some embodiments, determining, the exact numerical method for combining the different information sources, as well as the actual values of the weights, may be done via an empirical optimization routine. The optimization may be carried out for example on training data specific to the desired application. Methods based on statistics or machine learning, for example, may be applied to optimize for a metric of accuracy or reliability on this training data.

In some embodiments, a measure of the certainty or uncertainty of the final determined sensor position may be additionally provided. A highly certain final position determination may in turn be used as a stronger prior constraint when computing the sensor position in the next time frame, particularly when incorporating history information. In contrast, a less certain final result could be made to impose a weaker prior constraint on the position estimate in the subsequent frame.

<FIG> illustrate in further detail various examples of using intra-procedural context-specific information to ascertain estimated location <NUM> of passive acoustic sensor <NUM>. Intra-procedural context-specific information may be employed to eliminate candidate locations <NUM> from consideration for selection as estimated location <NUM>. Intra-procedural context-specific information may be employed to select one of candidate locations <NUM> which best matches or agrees with the intra-procedural context-specific information as estimated location <NUM>.

<FIG> illustrates an example embodiment of a method of improving sensor tracking estimates in interventional acoustic imaging by employing anatomical structure constraints.

The left side of <FIG> illustrates a case where no intra-procedural context-specific information-based constraints are employed in estimating the location of passive acoustic sensor <NUM>. Here, image <NUM> of sensor data shows multiple candidate locations <NUM>-<NUM> and <NUM>-<NUM> for passive acoustic sensor <NUM>. Without further constraints, processor <NUM> chooses candidate location <NUM>-<NUM> as an incorrect estimated location <NUM> for passive acoustic sensor <NUM>, for example because its peak intensity is greater than the peak intensity of candidate location <NUM>-<NUM>.

The right side of <FIG> illustrates a case where an intra-procedural context-specific information-based constraint is employed in estimating the location of passive acoustic sensor <NUM>. In particular, the right side of <FIG> illustrates a case where an anatomical structure constraint is employed in selecting one of the candidate locations <NUM>-<NUM> and <NUM>-<NUM> as estimated location <NUM> of passive acoustic sensor <NUM>. In particular, here is illustrated a case where processor <NUM> executes a region detection algorithm or segmentation algorithm to identify an anatomical structure <NUM> (e.g., a blood vessel) where passive acoustic sensor <NUM> is expected to be located in acoustic images <NUM>. Based on the constraint that passive acoustic sensor <NUM> should be located within anatomical structure <NUM>, processor <NUM> selects candidate location <NUM>-<NUM> as estimated location <NUM>.

<FIG> illustrates one example embodiment of a method of improving sensor tracking estimates in interventional acoustic imaging by employing constraints based on a structure of a device on which the sensor is provided.

For needle interventions, estimated location <NUM> of passive acoustic sensor <NUM> has to be on the needle shaft. This constraint can, thus, be used to weed out incorrect candidate locations <NUM> of passive acoustic sensor <NUM>. In <FIG>, multiple candidate locations <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM> and <NUM>-<NUM> exist for the sensor position (shown scan converted in B-mode space in the leftmost figure). Without employing any context-specific information-based constraints, processor <NUM> will select incorrect estimated location <NUM> shown in the central image in <FIG>. However, when a needle shaft segmentation-based constraint is applied, processor <NUM> selects the correct estimated position <NUM> for passive acoustic sensor <NUM>, as shown in the rightmost image in <FIG>. The different straight lines <NUM> in the rightmost image in <FIG> indicate possible candidates for the shaft of the needle, based on the automated shaft segmentation algorithm used in this example. The correct result is the one where the segmented shaft culminates in the correct estimated position <NUM> for passive acoustic sensor <NUM>.

<FIG> illustrates one example embodiment of a method of improving sensor tracking estimates in interventional acoustic imaging by employing previous estimated locations of the sensor.

The location of passive acoustic sensor <NUM> in the current frame or acoustic image <NUM> cannot be inconsistent with history (i.e., its locations in previous frames or acoustic images <NUM>). Reliance on sensor history can be modelled in different ways. For example, a Kalman filter model framework can be tweaked to either place more weight on the current estimate or rely more on the historical locations. Alternately, principal component analysis (PCA) of all previous estimated locations <NUM> of passive acoustic sensor <NUM> can be performed and the first principal component indicates device motion trajectory. In another example, the search space in the current frame or acoustic image <NUM> can be reduced to a region of interest (ROI) around the estimated location <NUM> in the previous frame(s) or acoustic image(s) <NUM>. <FIG> shows an example where this last method of history-based constraint is used to weed out incorrect sensor location estimates, such as incorrect estimated position <NUM>.

<FIG> illustrates graphically an example of improving sensor tracking estimates in interventional acoustic imaging by employing intra-procedural context-specific information, as described above with respect to <FIG>. In the illustrated example, multiple candidate locations <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM> and <NUM>-<NUM> are identified in the sensor data, and then intra-procedural context-specific information is employed to select one of the candidate locations (e.g., candidate location <NUM>-<NUM>) as the estimated location of passive acoustic sensor <NUM>. Here, the intra- procedural context-specific information includes anatomical constraint, the known shape of the structure of an interventional device on which passive acoustic sensor <NUM> is provided, and previous estimated locations of passive acoustic sensor <NUM>.

<FIG> illustrates a flowchart of an example embodiment of a method of improving sensor tracking estimates in interventional acoustic imaging by employing intra-procedural context-specific information.

An operation <NUM> includes providing transmit signals to least some of the acoustic transducer elements of an acoustic probe to cause the array of acoustic transducer elements to transmit an acoustic probe signal to an area of interest.

An operation <NUM> includes producing acoustic images of the area of interest in response to acoustic echoes received from the area of interest in response to the acoustic probe signal.

An operation <NUM> includes receiving one or more sensor signals from at least one passive acoustic sensor disposed on a surface of an intervention device disposed in the area of interest, the one or more sensor signals being produced in response to the acoustic probe signal.

An operation <NUM> includes identifying one or more candidate locations for the passive acoustic sensor based on localized intensity peaks in sensor data.

An operation <NUM> includes using intra-procedural context-specific information to identify one of the candidate locations which best matches the intra-procedural context-specific information as the estimated location of the passive acoustic sensor.

An operation <NUM> includes displaying the acoustic images including a marker to indicate the estimated location of the passive acoustic sensor in the acoustic image.

It should be understood that the order of various operations in <FIG> may be changed or rearranged, and indeed some operations may actually be performed in parallel with one or more other operations. In that sense, <FIG> may be better viewed as a numbered list of operations rather than an ordered sequence.

<FIG> illustrates a flowchart of an example embodiment of operation <NUM> in <FIG>. In particular, <FIG> illustrates a method <NUM> of employing anatomical structure constraints to improve sensor tracking estimates in interventional acoustic imaging.

An operation <NUM> includes identifying an anatomical structure where the sensor is expected to be located. In some embodiments, this may include executing a region detection algorithm or segmentation algorithm of an acoustic image. In other embodiments, the acoustic imaging instrument is configured to produce color Doppler images of the area of interest in response to one or more receive signals received from the acoustic probe, and the processor is configured to identify the anatomical structure where the sensor is expected to be by identifying blood flow in the color Doppler images.

An operation <NUM> includes eliminating candidate locations for the sensor which are not disposed in an expected relationship to the anatomical structure.

<FIG> illustrates a flowchart of another example embodiment of operation <NUM> in <FIG>. In particular, <FIG> illustrates a method <NUM> of employing constraints based on a structure of a device on which a sensor is provided to improve sensor tracking estimates in interventional acoustic imaging.

An operation <NUM> includes identifying a likely location of the intervention device in the acoustic images. In some embodiments, this may include executing a region detection algorithm or segmentation algorithm of an acoustic image.

An operation <NUM> includes eliminating candidate locations for the passive acoustic sensor which are not disposed at likely location of interventional device.

<FIG> illustrates a flowchart of yet another example embodiment of operation <NUM> in <FIG>. In particular, <FIG> illustrates a method <NUM> of employing previous estimated locations of the sensor to improve sensor tracking estimates in interventional acoustic imaging.

An operation <NUM> includes identifying previous estimated locations of the passive acoustic sensor in previous acoustic images.

An operation <NUM> includes eliminating candidate locations for the passive acoustic sensor which are not consistent with previous estimated locations of the passive acoustic sensor.

Although not illustrated with a separate flowchart, as explained in detail above, in some embodiments operation <NUM> in FIG. <NUM> may be performed by employing two or more of the approaches illustrated in <FIG> and weighting the results of each algorithm.

A non-exhaustive set of examples of algorithms for using intra-procedural context-specific information to identify one of the candidate locations which best matches the intra-procedural context-specific information as the estimated location of the passive acoustic sensor has been presented here for illustration purposes. Of course other algorithms for using intra-procedural context-specific information to identify one of the candidate locations which best matches the intra-procedural context-specific information as the estimated location of the passive acoustic sensor would become apparent to those skilled in the art after reading the present disclosure, and such algorithms are intended to be encompassed by the broad claims and disclosure presented here.

Claim 1:
An acoustic imaging instrument (<NUM>) connectable to an acoustic probe (<NUM>) having an array of acoustic transducer elements, the acoustic imaging instrument, when connected to the acoustic probe, configured to provide transmit signals to at least some of the acoustic transducer elements to cause the array of acoustic transducer elements to transmit an acoustic probe signal to an area of interest, and further configured to produce acoustic images of the area of interest in response to acoustic echoes received from the area of interest in response to the acoustic probe signal (<NUM>), the acoustic imaging instrument including:
- a receiver interface (<NUM>) connectable to an intervention device and configured to receive one or more sensor signals from at least one passive sensor disposed on a surface of the intervention device when the intervention device is connected to the receiver interface and the at least one passive sensor is disposed in the area of interest, the one or more sensor signals having been produced in response to the acoustic probe signal (<NUM>); and
- a processor (<NUM>) configured to ascertain, from the one or more sensor signals from the passive sensor, an estimated location of the passive sensor in the area of interest, by:
- identifying one or more candidate locations for the passive sensor based on localized intensity peaks in sensor data produced in response to the one or more sensor signals from the passive sensor (<NUM>), and
- using intra-procedural context-specific information to identify the candidate location of the one or more candidate locations which best matches the intra-procedural context-specific information as the estimated location of the passive sensor (<NUM>),
wherein the processor is further configured to cause a display device (<NUM>) to display acoustic images of the area of interest and a marker in the acoustic images to indicate the estimated location of the passive sensor (<NUM>).