FIELD OF THE PRESENT SYSTEM:

The present system relates to an ultrasound thermometry system which employs model- based motion compensation methods and, more particularly, to an ultrasound thermometry system which can determine and remove respiratory motion induced speckle shifts to more accurately predict ablation-induced temperature changes.

BACKGROUND OF THE PRESENT SYSTEM: Observing temperature changes in vivo has a wide range of applications in medical procedures and can be used to determine tissue temperature during various medical procedures which may locally heat tissue, such as ablations and therapeutic thermal systems. Ablations can include radio-frequency ablation (RFA), microwave ablation (MWA), and high- intensity focused ultrasound (HIFU) methods which can be performed to, inter alia, eradicate malignant or benign tumors. Therapeutic thermal systems can be used to heat tissue to cause mild temperature increases in the tissue (e.g., hyperthermia) which can therapeutically improve the local delivery of drugs or otherwise sensitize the tissue to other forms of therapy such as radiotherapy. In these and other similar procedures, spatially and temporally accurate monitoring of the induced temperature changes in the tissue is important for optimal delivery of the therapeutic effect while minimizing undesired side-effects.

Ultrasound can be used for physical and/or thermal imaging during the above-stated and other procedures in which the heating of tissue is required. For example, ultrasound

thermometry (UT) can provide images and thermal imaging which can be used for guidance during suitable procedures. Thermal imaging can include one-, two-, three-, or four-dimensional (e.g., 1 -D, 2-D, 3-D, 4-D, respectively) thermal imaging, depending upon a type of ultrasound probe and/or system settings. The sound speed in tissue changes with temperature, leading to apparent temperature- induced shifts of ultrasonic speckle patterns when observed over time. Ultrasound thermometry is based on quantifying these apparent speckle shifts in the received radio-frequency (RF) data for each beam with high accuracy during a period of tissue heating or cooling (e.g. for tissue ablation or hyperthermia). Assuming a linear relationship (or after calibrating a non-linear relationship) between temperature and sound speed, a temperature image can be created based on the measured, local speckle shifts throughout the image. Physiological motion (in particular, respiratory) also causes speckle shifts and can substantially affect the accuracy of temperature estimation. Respiratory motion gating can reduce but not entirely eliminate these physiological displacements, and the remaining physiological speckle shifts may be of the same magnitude or larger than the temperature-induced shifts. Thus, when tracking speckle shifts for thermometry, it is not known which part of the observed total speckle shift is due to temperature change, which makes accurate temperature calculations impossible.

Many previous attempts at ultrasound thermometry in vivo had failed, due to

physiological motion. Motion makes it very difficult to compare the location of ultrasound echoes before/after heating with micrometer accuracy, which is required for the thermometry processing of the data.

Thus, embodiments of the present system may overcome these and other

disadvantages of conventional ultrasound thermometry systems and methods. SUMMARY OF THE PRESENT SYSTEM:

The system(s), device(s), method(s), arrangements(s), user interface(s), computer program(s), processes, etc. (hereinafter each of which will be referred to as system, unless the context indicates otherwise), described herein address problems in prior art systems.

Embodiments of the present system may provide a system and method for estimating physiological speckle shifts in an area of heating within a region-of-interest (ROI). These speckle shifts may then be subtracted from a total of observed speckle shifts before estimating temperatures based upon temperature-induced speckle shifts. Thus, embodiments of the system may provide accurate temperature estimates based upon the temperature-induced speckle shifts.

In accordance with embodiments of the present system, there is disclosed an ultrasound thermometry system, comprising a controller and/or processor which is configured to:

obtain ultrasound information of a region-of-interest (ROI);

generate first and second sets of data frames based upon the ultrasound information of the ROI, each of the first and second sets of data frames comprising data frames with first and second zones which are substantially mutually exclusive to each other;

determine motion fields in the first and second zones in each of the data frames of the first and second sets of data frames;

form a model which correlates the motion fields between the first and second zones within the respective data frames of the first set of data frames;

estimate motion fields due to a physiological component within the first zone of a current data frame selected from the second set of data frames in accordance with motion fields in the second zone of the current data frame and the model; and

update the current data frame by correcting the first zone of the current data frame in accordance with a difference between the determined motion and the estimated motion fields in the first zone.

The controller is further configured to form a temperature map based upon the updated current data frame, and to render and/or cause rendering of the temperature map on the display. The controller is further configured to acquire and/or cause acquisition of the first set of data frames prior to heating tissue within the ROI, and acquire and/or cause acquisition of the second set of data frames after heating the tissue within the ROI. In the second set of data frames, the first zone is heated by a source and the second zone is substantially not heated by a source controlled by the controller and comprising at least one of radio-frequency and laser sources. The controller may be further configured to control the source to heat the tissue so as to obtain the first set of data frames immediately prior to heating the tissue within the ROI and obtain the second set of data frames immediately after heating of the tissue within the ROI. The first set of data frames comprises a plurality of data frames and the second set of data frames comprises at least one data frame and is acquired after the first set of data frames is acquired.

Another embodiment includes a method for ultrasound thermometry system having at least one ultrasound probe and at least one controller, the method being performed by the least one controller and comprising acts of:

obtaining ultrasound information of a region-of-interest (ROI);

generating first and second sets of data frames based upon the ultrasound information of the ROI, each of the first and second sets of data frames comprising data frames with first and second zones which are substantially mutually exclusive to each other;

determining motion fields in the first and second zones in each of the data frames of the first and second sets of data frames;

forming a model which correlates motion fields between the first and second zones within the respective data frames of the first set of data frames;

estimating motion fields due to a physiological component within the first zone of a current data frame selected from the second set of data frames in accordance with motion fields in the second zone of the current data frame and the model; and

updating the current data frame by correcting the first zone of the current data frame in accordance with a difference between the determined motion and the estimated motion fields in the first zone, where the first set of data frames is acquired prior to heating tissue within the ROI, and/or the second set of data frames is acquired after heating the tissue within the ROI. In the second set of data frames, the first zone is heated by a source and the second zone is substantially not heated by the source. The method may further comprise acts of forming a temperature map based upon the updated current data frame; rendering the temperature map on a display; and controlling a source to heat tissue within the ROI. The controller may be configured to control the source to heat the tissue so as to obtain the first set of data frames immediately prior to heating the tissue within the ROI and obtain the second set of data frames immediately after heating of the tissue within the ROI. The first set of data frames comprises a plurality of data frames and the second set of data frames comprises at least one data frame and is acquired after the first set of data frames is acquired.

A further embodiment includes a non-transitory computer readable medium, comprising computer instructions which, when executed by a processor, configure the processor to perform the acts of:

obtaining ultrasound information of a region-of-interest (ROI);

generating first and second sets of data frames based upon the ultrasound information of the ROI, each of the first and second sets of data frames comprising data frames with first and second zones which are substantially mutually exclusive to each other;

determining motion fields in the first and second zones in each of the data frames of the first and second sets of data frames;

forming a model which correlates motion fields between the first and second zones within the respective data frames of the first set of data frames;

estimating motion fields due to a physiological component within the first zone of a current data frame selected from the second set of data frames in accordance with motion fields in the second zone of the current data frame and the model;

updating the current data frame by correcting the first zone of the current data frame in accordance with a difference between the determined motion and the estimated motion fields in the first zone; and

forming a temperature map based upon the updated current data frame. BRIEF DESCRIPTION OF THE DRAWINGS:

The present invention is explained in further detail in the following exemplary embodiments and with reference to the figures, where identical or similar elements are partly indicated by the same or similar reference numerals, and the features of various exemplary embodiments being combinable. In the drawings:

FIG. 1 shows a schematic block diagram of a portion of an ultrasound thermometry (UT) system operating in accordance with embodiments of the present system;

FIG. 2 shows a functional flow diagram performed by a process in accordance with embodiments of the present system;

FIG. 3 shows a graph of a k ^th data frame (e.g., image frame) and associated values for the calculation of speckle shifts in accordance with embodiments of the present system;

FIG. 4 shows a graph of a computation of the motion model using a linear relationship in accordance with embodiments of the present system;

FIG. 5A shows a portion of a graph of a reference ultrasound data frame showing speckle shifts for a region-of-interest (ROI) having a heating zone and a non-heating zone (NHZ) surrounding the heating zone (HZ) in accordance with embodiments of the present system; FIG. 5B shows a portion of a graph which is based upon the graph shown in FIG. 5A with the heating zone excluded in accordance with embodiments of the present system;

FIG. 5C shows a portion of a graph which is based upon the graph shown in FIG. 5A with multiple overlapping sub-ROIs in accordance with embodiments of the present system;

FIG. 5D shows a portion of graphs showing speckle shifts for a ROI of a data frame in accordance with embodiments of the present system; FIG. 6 shows a graphical depiction of motion compensation processing operations in accordance with embodiments of the present system;

FIG. 7 shows a portion of a temperature map in accordance with embodiments of the present system; FIG. 8 shows portions of a series of graphs showing detected temperature

superimposed upon ultrasound images in accordance with embodiments;

FIG. 9 shows a portion of a system in accordance with embodiments of the present system;

FIG. 10 shows a graph of an image acquisition process in which image frames acquired in accordance with embodiments of the present system;

FIG. 11 shows a graph of a structure of an image frame including reference points and target points in accordance with embodiments of the present system; and

FIG. 12 shows a functional flow diagram performed by a process 1200 in accordance with embodiments of the present system.

DETAILED DESCRIPTION OF THE PRESENT SYSTEM:

The following are descriptions of illustrative embodiments that when taken in conjunction with the following drawings will demonstrate the above noted features and advantages, as well as further ones. In the following description, for purposes of explanation rather than limitation, illustrative details are set forth such as architecture, interfaces, techniques, element attributes, etc. However, it will be apparent to those of ordinary skill in the art that other embodiments that depart from these details would still be understood to be within the scope of the appended claims. Moreover, for the purpose of clarity, detailed descriptions of well known devices, circuits, tools, techniques, and methods are omitted so as not to obscure the description of the present system. It should be expressly understood that the drawings are included for illustrative purposes and do not represent the entire scope of the present system. In the accompanying drawings, like reference numbers in different drawings may designate similar elements. The term and/or and formatives thereof should be understood to mean that only one or more of the recited elements may need to be suitably present (e.g., only one recited element is present, two of the recited elements may be present, etc., up to all of the recited elements may be present) in a system in accordance with the claims recitation and in accordance with one or more embodiments of the present system. FIG. 1 shows a schematic block diagram of a portion of an ultrasound thermometry (UT) system 100 (hereinafter system 100 for the sake of clarity) operating in accordance with embodiments of the present system. The system 100 may include one or more of an ultrasound processor (UP) 102, an ultrasound scanner controller 104, an ultrasound probe 106 (hereinafter probe for the sake of clarity unless the context indicates otherwise), sensors 108, a user interface (Ul) 122, and a network 128 which may be coupled to communicate with each other via wired and/or wireless methods using any suitable protocol(s). For example, one or more of the ultrasound processor (UP) 102, the ultrasound scanner control 104, the probe 106, the sensors 108, and the user interface 122, and/or portions thereof, may communicate with each other or with external systems using wired and/or wireless methods over a suitable

communication link such as via the network 128.

The system may further include a thermal source (e.g., a heating source) which may include any suitable thermal source such as a radiofrequency (RF), microwave, laser, high- intensity ultrasound, etc., sources. The source may be controlled by a controller of the system such as the ultrasound processor 102 or may be controlled manually. For example, the controller may control power, position, orientation, and/or settings of the source. Sensors may provide feedback information which may be provided to the controller to adjust the source.

The network 128 may include any suitable network or networks such as a local-area network (LAN), a wide-area network (WAN), the Internet, an intranet, a proprietary bus, a controller-area network (CAN) bus, an ad-hoc network, a Bluetooth™ network, a WiFi™ network, a telephony network, a proprietary network, etc. It is also envisioned that one or more of the ultrasound processor (UP) 102, the ultrasound scanner controller 104, the probe 106, the sensors 108, the user interface 122 may be formed integrally with one another or with portions thereof, as may be desired. The ultrasound processor (UP) 102 may include a controller 110 which may control the overall operation of the system 100. The controller 1 10 may include a single controller or a plurality of controllers the latter of which may be which may be distributed throughout the system 100 as may be desired. The controller 1 10 may include any suitable logic device or devices such as one or more processors (e.g., a micro-processor (μΡ)), logic gates, shift registers, programmable controller, and/or the like. The controller 1 10 may obtain information such as operating code and/or other information from a memory of the system such as the memory 126 and may store information generated by the system 100 in the memory of the system, such as in the memory 126.

The memory 126 may include any suitable non-volatile memory in which information such as operating instructions, information generated by the system, user inputs and/or settings, historical information, operating settings and/or parameters, identification information, user information, patient information, medical images, thermal images, ultrasound images, etc., may be stored.

The ultrasound scanner controller 104 may include any suitable ultrasound scanner controller and may be operatively coupled to the ultrasound processor 102 and the probe 106. The ultrasound scanner controller 104 may receive operating instructions from the controller 1 10 and may control the probe 106 accordingly to scan a region-of-interest (ROI) within subject- of-interest (SOI) such as a patient 101 . The ROI may include an area or region in which temperature changes are to be observed and determined by the system 100. These temperature changes may be due to heating of tissue within a heating zone within the ROI. Thus, the ROI may include the heating zone in which tissue may be heated by external sources (e.g., by an RF probe, an US probe and/or laser). However, the ROI may further include a non- heating zone in which the tissue is not substantially heated by external sources.

The heating zone may be determined by the system automatically and/or in accordance with a user input. For example, the system may determine settings of the source, location and/or parameters of the source and may determine a corresponding location of the heating area. For example, after the source (e.g., an RF ablation (RFA) source having a needle with a tip) may be inserted with in a SOI, the system may determine the position and/or orientation of the RFA source and determine that heating may occur in within approximately a 4cm sphere around needle tip and set this area as a heating zone.

In yet other embodiments, a graphical user interface may be generated and rendered showing the location and/or orientation of the source and the user may be provided with an option to select the heating zone by adjusting a size and/or location of a circle about the tip of the rendered source. Accordingly, the user may adjust a size and/or location of the heating zone. For example, a diameter of this circle may initially represent a maximum area in the image in which heating is expected (e.g. 4cm). Accordingly, the system may employ tracking systems to determine a location and/or orientation of the source relative to the SOI.

The probe 106 may include any suitable ultrasound probe or probes which may include one or more ultrasound transducers which may be arranged in a desired order to form, for example, a transducer array. The probe 106 may transmit ultrasound waves and acquire ultrasound information of at least one selected ultrasound image plane. The probe 106 may include any suitable ultrasound probe for a procedure being performed. For example, in the present embodiments, the probe 106 may be assumed to include a 2-D or 3-D ultrasound probe which may obtain ultrasound information (USI) of the ROI which may then be processed by the system 100 to construct images of the ROI which may include one or more ultrasound image planes. The USI may be processed in accordance with embodiments of the present system to determine thermal information which may be used to build a thermal map. The USI may include radiofrequency (RF) information (RFI). The ultrasound probed may include, for example, a one- or two-dimensional array of transducers. During operation, the probe 106 may be controlled by the controller 1 10 to scan the ROI and form corresponding USI which may be provided to the UP 102 via the ultrasound scanner controller 104. Accordingly, the system may employ a robotic arm to spatially position the probe 106 as may be desired. The system may further control an output of the probe 106 in accordance with system and/or user settings as may be desired. However, in yet other embodiments, the system may passively receive USI. The USI may include radiofrequency information generated by the ultrasound probe 106 and/or processed information such as reconstructed image information. The USI may be obtained in any suitable format and may include, for example, beamformed raw RF or IQ data. This raw data may be transmitted in analog or digital format, and may include information about the geometry or spatial origin of the corresponding data (e.g. the number/index of ultrasound beam in the current image that the data corresponds to.

The USI may include any suitable format such as an analog and/or digital format and may be transmitted to the ultrasound scanner controller 104 for further processing. It is further envisioned that the USI may include corresponding time information (e.g., a time stamp) indicating an acquisition time, which information may be used for synchronization of various information as may be desired.

The sensors 108 may include one or more sensors which analyze the physiology of the SOI in real time and generate corresponding motion sensor information (MSI), such as respiratory sensor information (RSI), which may be related to physical motion of at least a portion of the SOI within at least the ROI. For example, the sensors 108 may include a respiratory sensor 124 which may sense respiratory phases of the SOI and may form

corresponding RSI which may then be provided within the MSI to the controller 1 10. Thus, the MSI may include the RSI. The MSI may further include motion information related to motion of the SOI, such as in motion of other tissues in the ROI within the SOI. However, for the sake of clarity, only RSI will be shown.

The controller 1 10 may analyze the RSI to determine a time point corresponding to a particular phase (e.g., end-expiration) of a respiratory (e.g., breathing) cycle of the SOI. This may be generally referred to as determining a respiratory cycle of the SOI. One or more respiratory cycles and/or portions thereof may be determined by the controller 1 10.

The respiratory sensor 124 may include any suitable respiratory sensor such as a capnograph sensor. However, other sensor-types are also envisioned. For example, it is envisioned that the respiratory sensor 124 may include any suitable sensor or sensors which may detect breathing and/or other motion of the SOI and form corresponding RSI. For example, the respiratory sensor may include a respiratory belt, an optical-type, gas-type (e.g., a capnograph), and/or mechanical-type sensors (e.g., inertial, rotational, etc. type sensors) whose sensor information may be processed to detect a respiratory cycle of the SOI. However, in yet other embodiments, image information (e.g., ultrasound, MRI, CT-scan, X-ray, etc.) of the SOI may be analyzed to determine a respiratory cycle of the SOI. Without limitation, although a respiratory cycle is discussed in the present embodiments for the sake of clarity, other periodic motions such as motion due to a heartbeat, etc., may be determined, if desired.

It is further envisioned that the sensors 108 may include other types of sensors which may detect motion and/or acceleration of one or more portions of tissue of the SOI, such as acceleration sensors, stress sensors, etc. which may be detect corresponding physiological motion within the SOI and form corresponding sensor information (e.g., MSI) which may be processed to determine acceleration and/or motion of the tissue of the SOI within the ROI and/or portions thereof. The sensors 108 may be situated at a single location or may be distributed throughout the system 100. It is further envisioned that the sensors 108 may include a camera or other imaging device which may image tissue of a user over time. Image analysis may then be used to perform motion estimation of the tissue concurrently with ultrasound image acquisition of the ROI. This information may then be used for motion estimation. Accordingly, the system may determine when motion estimation is minimal (e.g., below a threshold value). This motion information may then be used for motion gating. It is envisioned that the system may employ frame-to-frame correlation calculations to determine the amount of tissue motion

However, for the sake of clarity, the MSI may be assumed to include respiratory sensor information (RSI) generated by a respiratory sensor 124.

The ultrasound scanner controller 104 may receive the USI and process this information and forward the processed USI to the ultrasound processor (UP) 102 for further processing in accordance with embodiments of the present system. For example, the ultrasound scanner controller 104 may receive analog USI and may convert it to digital USI which may then be provided to the controller 102 of the ultrasound processor 102 for further processing in accordance with embodiments of the present system. However, it will be assumed that the USI include analog RSI for the sake of clarity. The functions of one or more of portions of the system may be integrated within the controller 1 10. Further, for the sake of clarity, it will be assumed that the controller 1 10 may control operation of the ultrasound scanner controller 104, the probe 106, and/or the sensors 108. The Ul 121 may include any suitable user interface which may render information for the convenience of user, such as a display 124 (e.g., a touch-sensitive display) which may render image information (e.g., graphical user interfaces (GUIs), reconstructed ultrasound image information, thermal information, etc.) that may be generated, or otherwise obtained, by the system 100, a speaker which may render audio information generated, or otherwise obtained, by the system, a haptic-device which may render haptic information generated, or otherwise obtained, by the system. The Ul 121 may further include a user input device (e.g., keyboard, a mouse, a trackball, or other user entry devices.) for receiving an entry (e.g., data, a

command(s), etc.) from one or more users of the system 100. The Ul 121 may be distributed throughout the system 100, if desired. For example, the ultrasound scanner controller 104 may include a display 130 and a user entry device 132 if desired. However, it is also envisioned that a smart phone communicating with the system 100 via the network 128 may communicate with the system 100 remotely.

During operation, the system may perform a motion-model-building (MMB) process and may thereafter perform a thermometry process as may be described elsewhere in this document.

Referring to the UP 102, during the MMB process, a speckle shift tracker 114 may obtain ROI information (RON) corresponding to a ROI for a current procedure from any suitable source, such as from an ROI definition memory 1 12. However, it is also envisioned that the RON may be obtained in real time, such as from a user via the user interface 122 of the system, and this RON may then be used and/or stored in a memory of the system such as the ROI definition memory 1 12. The RON may include information which may define a region-of-interest in which temperature changes are to be observed (e.g., within a heating zone) and/or in a non-heating zone (NHZ). The ROI may be defined by the user and/or the system and may include a plurality of sub ROIs, such as sub ROIs within the heating zone and sub ROIs outside of, or substantially outside of, the heating zone. Thus, the RON for the defined ROI may include information related to the plurality of sub-ROIs within the ROI.

The speckle shift tracker 1 14 may obtain the RON and USI (including the RF information) and may form corresponding speckle-shift information (SSI). The SSI may include speckle shift information for the ROI, such as SSI for each of the sub ROIs within the ROI. These sub ROIs may correspond with regions that are situated in a corresponding area of the ROI, such as within heating and non-heating zones.

The speckle shift tracker 1 14 may provide the SSI to a motion model builder 1 16 which may compute corresponding motion model information (MMI) and provide this information to the motion model 1 18. At this time, the MMB process may be complete and the system may begin the thermometry process (TP). The MMB process may last for a desired amount of time, such as for a few respiratory cycles of the SOI.

During the thermometry process (TP), the speckle shift tracker 1 14 may continue to receive the USI and may form corresponding speckle shift information (SSI), as discussed above with respect to the MMB process, and provide this SSI to both of the motion model 1 18 and a temperature image calculator 120.

The motion model 1 18 may then estimate a physiological speckle shift component and form corresponding physiological speckle shift component information (PSSCI) based upon the MMI and the corresponding SSI, and provide the estimated physiological speckle shift component as the PSSCI to the temperature image calculator 120. The temperature image calculator 120 may receive the SSI and the corresponding PSSCI and compute a temperature image and form corresponding temperature image information (Til). The Til may then be provided to the Ul 122 for rendering on the display 124. For example, the Til may be processed to form a temperature image. It is further envisioned that the temperature image may be superimposed upon a corresponding reconstructed ultrasound image of the ROI as may be desired.

A more detailed description of operation of an ultrasound thermometry system in accordance with embodiments of the system may now be described with respect to FIG. 2, which shows a functional flow diagram performed by a process 200 in accordance with embodiments of the present system. The process 200 may be performed using one or more processors, computers, controllers, etc., communicating over a network and may obtain information from, and/or store information to one or more memories which may be local and/or remote from each other. The process 200 may include one of more of the following acts. In accordance with embodiments of the present system, the acts of process 200 may be performed using one or more suitable ultrasound thermometry systems operating in accordance with embodiments of the present system. Further, one or more of these acts may be combined and/or separated into sub-acts, as desired. Further, one or more of these acts may be skipped depending upon settings. For the sake of clarity, the process may be described with reference to a thermometry system employing a single probe. However, without limitation, it should be understood that the process may employ a plurality of probes each of which may be include a separate workflow. In operation, the process may start during act 201 and then proceed to act 203. An MMB process may be performed during acts 203 through 215 and a motion- compensated thermometry (MCT) process (e.g., a thermometry process) may be performed thereafter. During act 203, the system may obtain a ROI for a current procedure. Accordingly, the system may obtain RON which defines a ROI for a current procedure from a suitable source of the system. With regard to the ROI, the ROI may include heating and non-heating zones (HZs and NHZs, respectively) and a plurality of sub-ROIs. For example, it is envisioned that the ROI may include a plurality of sub-ROIs, such as one or more sub-ROIs within a heating zone and one or more sub-ROIs within a non-heating zone. Generally, a heating zone may define an area (or volume if in 3-D) of the ROI in which direct heating of tissue due (e.g., due to a thermal source such as an RF source, etc.), and a non-heating zone is an area (or volume if in 3-D) of the ROI which is outside of the heating area but may be subject to residual heat due to the heating source, where this residual heat is negligible in comparison with heat in the heating zone. Thus, the ROI may be defined as an area (or volume if in 3-D) including an area in which temperature changes are to be observed, such as in the heating zone and in a non-heating zone.

It is envisioned that the ROI, or portions thereof (e.g., sub ROIs), may be selected by the system in accordance with a selected procedure type and/or by a user. For example, at least one procedure type (e.g., pulmonary-vein-type ablation, an aortic-type ablation, etc.) may be rendered on a display of the system for selection by a user. It is envisioned that there may be plurality of procedure types and corresponding ROI(s) defined for each procedure type.

Similarly, there may be a plurality of ROIs from which a user may select a desired ROI. After selection of an ROI, the system may obtain the corresponding RON for the selected procedure (or ROI) from a memory of the system. It is further envisioned that the ROIs may be defined based upon a type of probe and/or thermal heating device being used. For example, if using a 2-D probe (or probe setting), a 2-D ROI may be defined and if using a 3-D probe (or probe setting) a 3-D ROI may be defined. Similarly, an ROI may be defined based upon a type of thermal heating device being used and/or settings thereof so that the heating and non-heating zones may be selected for the ROI which correspond with zones that will be heated by the thermal heating device.

It is envisioned that the embodiments of the system may obtain an ROI based upon an input of the user (e.g., a user-defined ROI or user-selected ROI). For example, the system may provide an interactive GUI with which a user may select the ROI and/or a procedure type (e.g., soft tissue ablation-elliptical (2D) or ellipsoidal (3D) ROI; hyperthermia-general polygon ROI). For example, it is envisioned that the system may be operative to acquire US I, reconstruct this USI, and render a corresponding ultrasound (US) image. Then, the system may provide a GUI with which a user may define an ROI within this ultrasound image for further use. This ultrasound image may include a two- or three-dimensional ultrasound images. The system, may further select sub-ROIs based upon the selected ROI. In accordance with embodiments of the present system, the heating-zone (HZ) may be defined/selected by the system and/or user. Thereafter, the system may automatically generate a 1 cm margin around the defined heating zone (HZ) within the ROI. The shape and/or size of this margin zone may be define by a shape and/or size of the heating zone (HZ) (e.g. the system may form a ring around a circular HZ ROI or a box around a square HZ, etc.). This margin zone may be defined as a non-heating zone (NHZ) ROI. After completing act 203, the process may continue to act 205.

During act 205, the system may begin a data acquisition process to synchronously acquire RSI and USI for the selected ROI in real time. Accordingly, the controller 110 may be operative to control an ultrasound scanner controller 104 to control a probe to scan the selected ROI (e.g., of the SOI) and acquire corresponding RF information (RFI) which may be included within the USI.

In accordance with embodiments of the present system, the RSI may be obtained from any suitable source such as directly from a respiratory sensor, such as a respiratory-phase- detecting band (belt) that may be coupled to the chest of the SOI or situated within the SOI. However, it is also envisioned that other types of sensors may be employed to obtain the RSI (e.g., a capnograph, etc.), or the RSI may be determined by the system using other methods such as from an analysis of image data. For example, embodiments of the system may employ an image analysis method which may detect motion from images of the SOI and form corresponding RSI or other cyclical motion information. These image may be obtained from any suitable source such as from ultrasound, magnetic resonance imaging (MRI), X-ray imaging, CT-scan, etc. in real time.

Thus, it is envisioned that the RSI may be obtained using any suitable source and method such as being obtained from a respiratory sensor coupled to the SOI, through an analysis of images of the SOI, etc. which information may be obtained synchronously with the USI in real time. Accordingly, the system and/or user may select a source of the information suitable for obtaining or otherwise determining the RSI.

In accordance with embodiments of the present system, the data acquisition of the MMB process may be performed for a certain duration such as a duration sufficient to acquire K image frames, where K is an integer corresponding to a number of data frames (e.g., ultrasound image frames in the current example) acquired by the system and may be set by a user or the system. Accordingly, K may be sufficiently high such that a sufficient number of data frames may be acquired to cover at least one respiratory cycle prior to beginning of a thermometry process as will be described below. In yet other embodiments, an acquisition time period may be used rather than K frames. The system may employ any suitable frame rate which may be sufficiently high to capture small motion changes in the tissue within the ROI. For example, a frame rate of 30Hz may be used. However, other frame rates and/or ranges of frame rates such as a frame rate range from about 8 Hz to 100Hz or other ranges are also envisioned.

Accordingly, the MMB process may last for one or more respiratory cycles prior to beginning a thermometry process (TP) which may perform temperature estimation. During the MMB process, the probe 106 should be stationary and remain in the same position for the thermometry process as will be described below. After completing act 205, the process may continue to act 207.

During act 207, the system may perform motion gating. The motion gating may analyze the RSI to determine at least one time point corresponding to a particular phase or phases (e.g., end-expiration, etc.) of cyclic motion of the SOI, such as motion that may occur during a respiratory cycle of the SOI. For example, assuming for the sake of clarity in the present embodiments, that the cyclical motion may include respiratory motion only, the system may determine at least one time point corresponding to a particular phase (e.g., end-expiration, etc.) of the respiratory cycle of the SOI based upon the RSI.

The motion gating may serve (e.g., during act 217 at which a target data frame is acquired) to prevent part of the ultrasound data acquired during the strongest motion (e.g.

during full inspiration) from being used to acquire the target frame. For example, motion amplitudes during inspiration are so high that motion may not be compensated effectively during that time. In order to reduce the amount of data the system has to process, it may be desirable to determine the "quiescent" phase of respiration, typically during full expiration, and only use data obtained during that phase (this could be e.g. only 20% of the total respiratory cycle). Thus, the system may ignore about 80% of the total data that the ultrasound probe may generate during a target frame acquisition as may be described below. This may reduce system load. However, it is also envisioned that motion compensation may be applied to all the data ultrasound data (e.g., ultrasound frames) obtained as target frames without performing gating.

Thus, the system may pre-screen data using the respiratory gaiting and may process the motion compensation and thermal estimation for that part of the data for which would most likely provide data from which accurate results may be obtained. It is envisioned that the MMB process may be performed prior to a temperature change of tissue within the ROI due to heating (e.g., as a result of an ablation, etc.). After detecting phases of motion, the system may correlate RF information obtained the by speckle shift tracker 1 14 with the determined time point of the detected cyclical motion, which in the present embodiments is assumed to be respiratory motion. However, in yet other embodiments, other types of motion (e.g., cardiac motion, etc.) may be determined. Motion gating may be performed for the K data frames discussed above. Data acquisition in accordance with embodiments of the present system may be performed for a reference data (e.g., a reference image) and a total of K additional data frames (K additional image frames). K may be set such that a sufficient number of image frames may be acquired to perform a motion-gated data acquisition and computation of speckle shifts for several respiratory cycles with sufficient accuracy as will be described below. After completing act 207, the process may continue to act 209 at which point the system may begin an MMB process.

During act 209, the system may determine speckle shifts for all locations and/or points (referred to as locations for the sake of clarity) within the K image frames which may be denoted as M and N locations, where M and N are integers and may have a corresponding model location (location point) with respect to a data frame k wherein each of the M and N locations represents a speckle shift location. These M and N speckle shift locations may be differentiated by location with respect to the heating zone (HZ). For example, there may be M speckle shift locations within the heating zone (HZ) and N speckle shift locations outside of the heating zone (HZ) (e.g., in the non-heating zone (NHZ)). For the sake of clarity, it may be assumed that each of the M speckle shift locations may be evenly distributed across the heating zone (HZ) for each of the data frames. Similarly each of the N speckle shift locations may be evenly distributed across the non-heated zone (HZ) for each of the data frames. Values for the determined speckle shifts may then be stored in a memory of the system in any suitable format such as in a matrix format.

For example, the system may determine speckle shifts x in each of the acquired K data frames (e.g., the K image frames) relative to the reference data frame for each of the N model speckle shift locations outside of the heating zone (e.g., in the NHZ). The determined values for x (e.g., Xn _k ) may then be stored in a memory 1 18 of the system 100 in any suitable format, such as in an N x K matrix X assuming speckle shifts in a single dimension (e.g., 1 -D) in the present examples.

Similarly, the system 100 may further determine speckle shifts y for each of the acquired K data frames relative to the reference data frame for each of the M model speckle shift locations inside of the heating zone (HZ) which denotes a future heating zone. Thus, M may correspond with of a number of thermometer locations (TLs) inside of the (future) heating zone (HZ). The determined values for y (e.g., y _m ,k) may then be stored in a memory of the system in any suitable format, such as in an M x K matrix Y assuming speckle shifts in a single dimension (e.g., 1 -D similarly to matrix X) in the present example. Thus, it may be assumed that X = (x _n,k ) = N x K matrix, and Y = (y _m ,k) = M x K matrix.

Thus, the system may compute local speckle shifts inside and outside the future heating zone for each of K data frames during a motion model building (MMB) process.

Values of M and N as well as associated information (e.g., speckle shift locations, etc.) may be stored in the RON and may be obtained by the system when desired. For example, the RON may include information related to the heating-zones and the non-heating zones (HZs and NHZs, respectively) and associated values and/or physical locations with regard to each corresponding k ^th data frame of the K data frames. For example, FIG. 3 shows a graph 300 of a k ^th data frame (e.g., image frame) and associated values for the calculation of speckle shifts in accordance with embodiments of the present system. The system may compute speckle shifts for points y _m ,k (e.g., y,,k) inside of a heating zone 309 and points x _n ,k (e.g., x _p ,k and x _q ,k) in non- heating zones 305 and 307, respectively (e.g., outside of the heating zone).

Without limitation, tracking of speckles shifts are illustrated in a single direction (e.g., 1 - D) in the present embodiments for the sake of clarity. However, it is also envisioned that tracking of speckle shifts may be performed in multiple directions in which case the matrixes X and/or Y may be higher dimensionally to reflect the desired movement. For example, speckle shifts may be observed in multiple dimensions when a 3-D (volumetric) RF and/or ultrasound acquisition is performed. After completing act 209, the system may continue to act 211 .

During act 21 1 , the system may determine model parameters (A _m0 dei) (which is a model matrix) in accordance with the determined speckle shifts (e.g., the speckle shifts determined during act 209 above) using any suitable method such as by solving a matrix equation Y = A _m0 dei * X, which yields A _m0 dei = Y * X ^"1 , where indicates the pseudo-inverse of a matrix.

Thus, based upon motion model assumptions, the system may compute the model parameters (e.g., A _m0 dei). For the sake of clarity, these motion model assumptions may assume a linear model where there is a linear relationship between y _m ,k and x _n ,k.

However, it is envisioned that other models may also be used to compute the model parameters. For example, the system may employ non-linear models where there is a nonlinear relationship between points y _m ,k and x _n ,k . Other models may take into account time history, i.e., observed speckle shifts from previous frames can be used to build a model and estimate speckle shifts in future data frames. Accordingly, the system may employ a learning process which may store previous results, update and/or build models in accordance with these stored results, and may apply these later models to a current model, as may be desired.

FIG. 4 shows a graph 400 of a computation of the motion model using a linear relationship in accordance with embodiments of the present system. Speckle shifts for points y _m ,k (e.g., y,,k) inside of a heating zone 409 and x _n ,k (e.g., x _p ,k and x _q ,k points) in a non-heating zones 405 and 407, respectively (e.g., outside of the heating zone) are shown. Lines 41 1 and 413 illustrate the linear relationship between points y _m ,k and x _p ,k and x _q ,k, respectively, where point y _m ,k is inside of a (future) heating zone 409 and points x _p ,k and x _q ,k are in a non-heating zones 405 and 407, respectively, (e.g., outside of the heating zone). After completing act 21 1 , the process may continue to act 213.

During act 213, the process may determine whether model parameters (A _m0 dei) have been determined. Accordingly, if it is determined that the model parameters (A _m0 dei) have been determined, the system may continue to act 215. However, if it is determined that the model parameters (A _m0 dei) have not been determined, the process may repeat act 205. The system may determine that model parameters (A _m0 dei) have been determined when, for example, the model matrix is populated. Conversely, the system may determine that the model parameters (Amodei) have not been determined when, for example, there is an error in the calculation of the model parameters such as when the model matrix is not populated. At this time the model parameters (A _m0 dei) may form a current model. In accordance with some embodiments, the system may increase K prior to repeating act 205 such that a sufficient number of data frames may be acquired to build the model.

During act 215, the system may optionally output an indication that it is ready to begin a thermometry phase (TP). For example, this outputs an indication signal which may be rendered on a rendering device of the system to information a user that it is ready to begin the thermometry phase. For example, the output indication may include a ready indication which may be rendered on an illumination device (e.g., a light-emitting diode (LED)), a display, a speaker (e.g., as a tone, a beep, etc.), a haptic device (e.g., a vibration, etc.).

It is also envisioned that the system may transmit enable signal which may enable a thermal heating system (e.g., an ablation system, a therapeutic heating system, etc.) to begin a tissue heating process. Absent this enable signal, the tissue heating process may be prevented. Thus, a user may command a heating device to start, however, the system may then begin an MMB process and upon successful completion of the MMB process, the system may transmit an enable signal to enable the heating device. This process may take a short period of time (e.g., usually one respiratory cycle of the SOI) so as not to inconvenience the user. After completing act 215, the system may continue to act 217.

At this time, the motion modeling building (MMB) process may be considered to be successfully completed and a motion-compensated thermometry (MCT) process may now begin. During act 217, the system may acquire a current data frame corresponding with the same ROI as defined previously. Thus, the current data frame may include a heating zone (HZ) and a non-heating zone (NHZ) as defined by the ROI. This current data frame may be referred to as a target data frame or target frame. For the sake of clarity, only a single target frame is acquired. However, it is envisioned that that the system may process a plurality of target frames as may be desired. After completing act 217, the process may continue to act 219.

During act 219, the system may determine actual speckle shifts x for the current data frame for the N locations within the current data frame where these N locations correspond with the N locations outside of the heating zone (e.g., in the NHZ) as defined previously with respect to the reference image frame. These speckle shifts may be referred to as observed speckle shifts x of the heating zone (HZ) (e.g., in the NHZ) which is the same non heated zones as used during the MMB process. However, speckle shifts y within the heating zone (HZ) may be ignored at the present time. After completing act 219, the system may continue to act 221 .

During act 221 , the system may estimate physiological speckle shifts Yest t for the N locations within the heating zone (HZ) for each current data frame (e.g., the current image frame), where t represents time. Accordingly, the system may apply the current model (e.g., the model parameters (Amodei)) to the determined observed speckle shifts x for current data frame kt in the same M locations outside the heating zone (HZ) and may determine the estimated physiological speckle shifts Y _es t _, t for the N locations inside the heating zone (HZ) for which the current model was formed. Thus, Y _es t, t may be represented as Y _es t, t = A _m0 dei *Xt, where Y _es t, t = Mx1 vector of estimated shifts inside the heating zone (HZ), and Xt = Nx1 vector of observed shifts outside of the heating zone.

Thus, for each image data frame k _t , the system may determine speckle shifts y outside the heating zone and may apply the current model (A _m0 dei) to estimate physiological speckle component such as speckle physiological shifts Y _es t, t for each of the N locations within the heating zone (HZ) for a current data frame. The estimated physiological speckle shifts Y _es t _, t for the N locations within the heating zone (HZ) may thus, at least in part, be based upon the determined observed speckle shifts x as applied to the model. Thus, the estimated

physiological speckle shifts Y _es t, tfor each of the N locations within the heating zone (HZ) for a current data frame may be considered motion inside the heating zone that is substantially due to external motion, as opposed to motion that is substantially due to heating effects. After completing act 221 , the system may continue to act 223.

During act 223, the process may then determine actual speckle shifts Yact. tfor each of N locations within the heating zone (HZ) for the current data frame. After completing act 223, the system may continue to act 225. During act 225, the process may determine corrected speckle shifts Y _∞ rr, t for N locations in the heated zone (HZ) based upon a difference between the actual speckle shifts Y _ac t, t and the estimated physiological speckle shifts Yest, t . In other words, Y _∞ rr, t = Yact, t - Yest, t . Then, the system may continue to act 227.

During act 227, the process may form a corrected speckle shift map including the determined observed speckle shifts x for the current data frames in the same M locations outside of the heating zone (HZ) and the corrected speckle shifts Y _∞ rr, t in the same N locations within the heating zone (HZ). Thus, the system may replace the determined observed speckle shifts Y with the corrected speckle shifts Y _∞ rr, t■ This corrected speckle shift map may be considered a corrected motion field map. After completing act 227, the process may continue to act 229.

During act 229, the process may form a corrected temperature map based upon the corrected speckle shift map. Thus, the process may determine temperature calculations based upon the corrected speckle shift map and form a corresponding temperature map in accordance with the determined temperature calculations. The process may do this using any suitable method. After completing act 229, the system may continue to act 231.

During act 231 , the process may render the temperature field map on a rendering device of the system, such as on a display 124, 130 of the system 100 as may be desired. In accordance with embodiments of the present system, the temperature field map may be superimposed upon the corresponding reconstructed ultrasound data frame as may be desired. After completing act 231 , the process may continue to act 233.

During act 233, the system may store information obtained and/or determined by the system, such as results of the current process, USI, RSI, etc., in a memory 126 of the system 100 for later use, as may be desired. After completing act 231 , the process may continue to act 233, where it may end. Thus, embodiments of the present system may calculate a temperature map based upon a corrected speckle shift map which may include corrected speckle shifts within a heating zone and actual (e.g., observed) speckle shifts outside of this heating zone. The corrected speckle shifts may be determined by subtracting estimated physiological shifts (or their derivative, the estimated physiological strain) from the observed shifts (or strain) to determine corrected speckle shifts for the heating zone. The process may then render the temperature map. In other words, the process may correct speckle shifts within the HZ before proceeding with temperature calculations. Thus, training, modeling and/or corrections are done in the spatial shift domain instead of the temperature domain for obtaining a proper temperature map that does not need to be corrected.

It is envisioned that measured and estimated tissue motion (e.g., speckle shift) may include affine or higher-order deformations as may be desired. For this, each of the

components in matrices X and Y above may be replaced with the number of parameters necessary to describe the modelled local deformation (e.g., 5 parameters, for a 2-D affine deformation with tissue incompressibility).

Accordingly, the system may provide a process by which, during a thermometry process when actual tissue heating is to be monitored within a ROI including heating- and non-heating zones, speckle shifts outside a heating zone may be measured and fed into a model matrix (e.g., Amodei) in order to estimate the physiological speckle shifts inside the heating zone.

In accordance with embodiment of the present system, underlying a model building process may be assumptions about a relationship between speckle motion inside and outside of a defined heating zone. For the sake of clarity, a linear relationship may be assumed.

However, it should be understood that non-linear relationships may also be employed by embodiments of the present system.

FIG. 5A shows a portion of a graph 500A of a reference ultrasound data frame showing speckle shifts for a ROI having a heating zone and a non-heating zone (NHZ) surrounding the heating zone (HZ) in accordance with embodiments of the present system. These zones may be substantially mutually exclusive of each other. Apparent motion may be measured using speckle tracking of ultrasound data and is shown as vector lines 501 within the ROI. FIG. 5B shows a portion of a graph 500B which is based upon the graph 500B with the heating zone (HZ) excluded in accordance with embodiments of the present system. Portions of a first and second overlapping sub ROIs (ROh and ROI ₂ ) of a plurality of sub ROIs are shown for illustration. Multiple overlapping ROIs may be created in which the motion may be modeled as an affine transform. In overlapping regions, the motion estimates may be averaged for additional smoothness. The ROI as well as the sub-ROIs may have any suitable shape (e.g., square, rectangular, round, polygonal, etc.) and size with rectangular shown for the sake of clarity.

FIG. 5C shows a portion of a graph 500C which is based upon the graph 500B with multiple overlapping sub-ROIs in accordance with embodiments of the present system. The shading of the sub-ROIs illustrates overlapping of the sub-ROIs with areas overlapped by multiple sub-ROIs being lighter in shade and areas with little or no overlap being darkest. For example, areas 51 1 are dark and each has only a single sub-ROI and area 509 is lightest and is overlapped by 5 sub-ROIs in the present example. However, it is also envisioned that sub-ROIs or portions thereof may be mutually exclusive to each other with regard to overlap.

Once heating begins, motion due to heating within a heating zone, which may be considered an excluded area, may be estimated via any suitable method (e.g., by interpolation based methods, etc.) based upon a motion within individual overlapping motion sub-ROIs that are outside the heating zone (HZ) for a current data frame as applied to a model. For example, FIG. 5D shows a portion of graphs 500D and 500E showing speckle shifts for a ROI of a data frame in accordance with embodiments of the present system. More particularly, in graph 500D speckle shifts are excluded within a heating zone (HZ) and shown as current ultrasound data for other areas outside of the heating zone (e.g., the non-heating zone); and graph 500E shows a multi-regional affine transformation including current ultrasound data in the non-heating zone and estimated motion within the heating zone. After determining the multi-regional affine transformation, estimated motion inside the heating zone (HZ) that is substantially due to external motion (e.g., due to physiological motion such as respiration motion and not substantially due to heating effects) may be subtracted from the total observed motion (e.g., actual motion) within the heating zone, thus yielding a motion field that is only due to heating effects. This is illustrated with respect to FIG. 6 which shows graphical depictions 600A-C of motion compensation processing operations in accordance with embodiments of the present system.

More particularly, the graphical depiction includes graphs 600A, 600B, and 600C, where graph 600A shows a data frame including original (e.g., actual) measured motion during a motion-compensated thermometry (MCT) process corresponding to the same ROI, including HZ, and NHZ as shown in graph 500A. Graph 600B shows a multi-affine motion estimate determined for the graph 600A. A difference between the original measured motion and the multi-affine motion estimate is a corrected motion field and is shown as graph 600C. This corrected motion field is then used to determine a resulting temperature map after motion compensation. For example, FIG. 7 shows a portion of a temperature map 700 in accordance with embodiments of the present system. The temperature map may be superimposed upon a corresponding ultrasound image if desired.

FIG. 8 shows portions of a series of graphs 800A through 800C including detected temperatures superimposed upon ultrasound images in accordance with embodiments. Graphs 800A through 800C were reconstructed by embodiments of the present system and illustrate motion compensation prior to determining temperature maps. The detected temperatures were obtained using temperature mapping methods of the present system. With reference to graph 800A, there was no shear when the ultrasound data frame was acquired. However, graphs 800B and 800C show temperature mapping with 1 mm and 2mm shear, respectively, within a heating zone (shown in rectangular frame within image frame) when the underlying ultrasound data frame was acquired. Accordingly, it is seen that temperature maps at up to 2mm of shear and beyond can be accurately reconstructed, including compensation of a sheer up to 5mm, for example.

FIG. 9 shows a portion of a system 900 in accordance with embodiments of the present system. For example, a portion of the present system may include a processor 910 (e.g., a controller) operationally coupled to a memory 920, a user interface (Ul) including a rendering device such as a display 930, sensors 940, and a user input device 970. The memory 920 may be any type of device for storing application data as well as other data related to the described operation. The application data and other data are received by the processor 910 for configuring (e.g., programming) the processor 910 to perform operation acts in accordance with the present system. The processor 910 so configured becomes a special purpose machine particularly suited for performing in accordance with embodiments of the present system.

The operation acts may include configuring an ultrasound thermal imaging system in accordance with system settings. The processor 910 may determine motion from ultrasound information received from a medical imaging probe such as an ultrasound probe. The processor 910 may further determine a temperature map based upon processed ultrasound information obtained from the probe. The processor 910, thereof may process received signals such as sensor information, transform the ultrasound information and build a corresponding temperature map and may generate content which may include ultrasound image information (e.g., still or video images (e.g., video ultrasound information)), data, and/or graphs (e.g., temperature maps, etc.) that may be rendered on, for example, a Ul of the system such as on the display 930, a speaker, etc. The content may include image information as may be generated by a medical imaging system of the present system and/or may include guidance information (e.g., move right, left, arrows, etc.) to guide a user during a procedure. Further, the content may then be stored in a memory of the system such as the memory 920 for later use. Operational acts may further include requesting, providing, and/or rendering of content. The processor 910 may render the content such as video information on a Ul of the system such as a display of the system.

The user input 970 may include a keyboard, a mouse, a trackball, or other device, such as a touch-sensitive display, which may be stand alone or part of a system, such as part of a personal computer, a personal digital assistant (PDA), a mobile phone (e.g., a smart phone), a monitor, a smart or dumb terminal or other device for communicating with the processor 910 via any operable link such as a wired and/or wireless communication link. The user input device 970 may be operable for interacting with the processor 910 including enabling interaction within a Ul as described herein. Clearly the processor 910, the memory 920, display 930, and/or user input device 970 may all or partly be a portion of a computer system or other device such as a client and/or server.

The methods of the present system are particularly suited to be carried out by a computer software program, such program containing modules corresponding to one or more of the individual steps or acts described and/or envisioned by the present system. Such program may of course be embodied in a computer-readable medium, such as an integrated chip, a peripheral device or memory, such as the memory 920 or other memory coupled to the processor 910.

The program and/or program portions contained in the memory 920 may configure the processor 910 to implement the methods, operational acts, and functions disclosed herein. The memories may be distributed, for example between the clients and/or servers, or local, and the processor 910, where additional processors may be provided, may also be distributed or may be singular. The memories may be implemented as electrical, magnetic or optical memory, or any combination of these or other types of storage devices. Moreover, the term "memory" should be construed broadly enough to encompass any information able to be read from or written to an address in an addressable space accessible by the processor 910. With this definition, information accessible through a network is still within the memory, for instance, because the processor 910 may retrieve the information from the network for operation in accordance with the present system. The processor 910 is operable for providing control signals and/or performing operations in response to input signals from the user input device 970 as well as in response to other devices of a network and executing instructions stored in the memory 920. The processor 910 may include one or more of a microprocessor, an application-specific or general-use integrated circuit(s), a logic device, etc. Further, the processor 910 may be a dedicated processor for performing in accordance with the present system or may be a general-purpose processor wherein only one of many functions operates for performing in accordance with the present system. The processor 910 may operate utilizing a program portion, multiple program segments, or may be a hardware device utilizing a dedicated or multi-purpose integrated circuit. Embodiments of the present system may provide imaging methods to acquire and/or reconstruct images. Suitable applications may include imaging systems such as ultrasound. However, without limitation it should be understood that embodiments of the present system may further include imaging systems such as MRI, computer-aided tomography (CAT), optical, X-ray, and/or combinations thereof. Further, embodiments of the present system may be ideally suited for surgical interventional techniques which may generate and render image and/or sensor information from one or more imaging systems (e.g., ultrasound, CAT scans, MRI, X-ray etc.) having different coordinate systems in real-time with a unified coordinate system. The system may determine pose of the probe and may register the probe and/or image information obtained from the probe with these other systems. Accordingly, the system may determine velocity and/or pose of the probe for registration with these other systems. FIG. 10 shows a graph 1000 of an image acquisition process in which image frames acquired in accordance with embodiments of the present system. A series of training frames (e.g., K frames) may be obtained and may over time and may include a reference frame which may be a first frame of the K frames. The system may determine (e.g., using a learning process) a relationship between reference and target points within the K image frames and form a corresponding motion model. This model may then be used to predict motion of target points based upon reference points when a new frame such as the target frame is given. Motion gating may be applied to determine a quiescent phase (QP) within a cycle of motion within the K frames. Thereafter, motion gating may be applied to acquire the target frame during a corresponding quiescent phase (QP).

FIG. 11 shows a graph 1100 of a structure of an image frame including reference points and target points in accordance with embodiments of the present system. The image frame may include a heating zone (HZ) and a non-heating zone (NHZ) at least partially surrounding the HZ. The target points may be included within the heating zone (HZ) and the reference points may be situated within the non-heating zone (NHZ). The motion of the reference points may be used to predict the motion of the target points. The non-heating zone (NHZ) may be defined as a certain area situated about a periphery of the heating zone (HZ). For example, the NHZ may extend a distance Ad about the outer periphery of the heating zone (HZ). Ad may have a value determined by the system and/or user for example, Ad may be equal to about 4 cm. However, other values and/or ranges of values are also envisioned.

FIG. 12 shows a functional flow diagram performed by a process 1200 in accordance with embodiments of the present system. The process 1200 may be performed using one or more processors, computers, controllers, etc., communicating over a network and may obtain information from, and/or store information to one or more memories which may be local and/or remote from each other. The process 1200 may include one of more of the following acts. In accordance with embodiments of the present system, the acts of process 1200 may be performed using one or more suitable ultrasound thermometry systems operating in accordance with embodiments of the present system. Further, one or more of these acts may be combined and/or separated into sub-acts, as desired. Further, one or more of these acts may be skipped depending upon settings. For the sake of clarity, the process may be described with reference to a thermometry system employing a single probe. In operation, the process may start during act 1203.

During act 1203 the system may compute motion vectors for both reference and target points within a plurality of training frames acquired by the system. The system may then continue to act 1205 and 1209 which may be performed concurrently or sequentially with each other as may be desired.

During act 1205 the system may analyze the reference points and the target points within each of the training frames and learn a form a corresponding motion model. Thus, the system may lean a motion model based upon the reference and target points in each of the training frames. After forming the corresponding motion model, the system may continue to act 1207.

During act 1209, the system may compute motion (vectors) for both reference and target points within an acquired target frame. The system may then continue to act 1207.

During act 1207, the system may estimate motion (vectors) of the target points based upon the reference points using the learned motion model as may be applied to the reference points within the target frame. After completing act 1207, the system may continue to act 1201 1.

During act 121 1 , the system may subtract the estimated motion from the computed motion (vectors) for each of the estimated motion points within the target frame and may form a corresponding motion compensation result for thermometry. This result may then be stored in a memory of the system for later use, may be used to compute a thermal map which may be rendered on a user interface such as a display for the convenience of the user, and/or employed for other processing such as to determine whether a sufficient thermal dose been applied to the heating zone. After completing act 21 1 , the process may end.

Accordingly, embodiments of the present system may process ultrasound information in accordance with embodiments of the present system and correct motion error to provide for ultrasound thermometry with enhanced results.

Without limitation, embodiments of the present system may be applied in various scenarios such as scenarios in which temperature estimates are to be obtained in an ROI that is subject to cyclical motion such as when monitoring of ablations or hyperthermia applications in the liver, kidneys, pancreas, pleura, etc.

Embodiments of the present system provide a system and method to separate

"unwanted" physiological motion from ablation-induced tissue motion within a ROI. A model of local tissue motion may be built and trained immediately prior to a period of tissue heating, in order to be able to predict the physiological speckle shift component during the heating. The model may be based on measuring local tissue motion (via speckle shifts) in areas outside a future heating zone, and correlating it with speckle shifts inside the future heating zone, during motion-gated respiration. The correlation allows a prediction of the motion of tissue inside the heating zone, based on the measured motion of tissue outside the heating zone. During the actual heating process, the motion of tissue outside the heating zone but within the same ROI will continue to be monitored by the system in order to feed the motion model, thus predicting the physiological speckle shifts inside the heating zone. These predicted physiological speckle shifts may then be subtracted from the total observed speckle shifts before calculating local temperature. Therefore, the proposed approach would remove the respiratory motion induced speckle shift prior to temperature estimation and consequently make it possible to predict ablation induced temperature changes.

Further variations of the present system would readily occur to a person of ordinary skill in the art and are encompassed by the following claims. Finally, the above-discussion is intended to be merely illustrative of the present system and should not be construed as limiting the appended claims to any particular embodiment or group of embodiments. Thus, while the present system has been described with reference to exemplary embodiments, it should also be appreciated that numerous modifications and alternative embodiments may be devised by those having ordinary skill in the art without departing from the broader and intended spirit and scope of the present system as set forth in the claims that follow. In addition, any section headings included herein are intended to facilitate a review but are not intended to limit the scope of the present system. Accordingly, the specification and drawings are to be regarded in an illustrative manner and are not intended to limit the scope of the appended claims. In interpreting the appended claims, it should be understood that: a) the word "comprising" does not exclude the presence of other elements or acts than those listed in a given claim; b) the word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements; c) any reference signs in the claims do not limit their scope; d) several "means" may be represented by the same item or hardware or software implemented structure or function; e) any of the disclosed elements may be comprised of hardware portions (e.g., including discrete and integrated electronic circuitry), software portions (e.g., computer programming), and any combination thereof; f) hardware portions may be comprised of one or both of analog and digital portions; g) any of the disclosed devices or portions thereof may be combined together or separated into further portions unless specifically stated otherwise; h) no specific sequence of acts or steps is intended to be required unless specifically indicated; i) the term "plurality of" an element includes two or more of the claimed element, and does not imply any particular range of number of elements; that is, a plurality of elements may be as few as two elements, and may include an immeasurable number of elements; and j) the term and/or and formatives thereof should be understood to mean that only one or more of the listed elements may need to be suitably present in the system in accordance with the claims recitation and in accordance with one or more embodiments of the present system.