Systems and methods for Kalman filter-based microvascular repair for super-resolution imaging

Abstract

Described herein are systems and methods for super-resolution imaging using ultrasound, in which a Kalman filter-based microvascular repair technique is used to facilitate robust super-resolution imaging with limited or otherwise lost microbubble signals. The systems and methods described in this disclosure can be combined with local and global microbubble tracking methods.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Patent Application Serial No. 62 / 747,990, filed on October 19, 2018, and entitled “SYSTEMS AND METHODS FOR KALMAN FILTER-BASED MICROVASCULAR REPAIR FOR SUPER-RESOLUTION IMAGING,” which is hereby incorporated by reference in its entirety.

[0003] Statement Regarding Federally Funded Research

[0004] This invention was made with government support under Grant CA214523 from the National Institutes of Health. The government has certain rights in this invention. Background Art

[0005] A common challenge of super-resolution imaging based on microbubbles is the long accumulation time required for sufficient microbubbles to fill the target microvasculature. In fact, factors including tissue motion, probe motion, microbubble dose limit, imaging noise and various physiological conditions (e.g., slow microvasculature perfusion) will limit the available total number of microbubbles for super-resolution processing. These factors may lead to discontinuous microvascular depiction, wherein lack of microvascular structure and microvascular flow velocity measurement are unreliable. Summary of the invention

[0006] The present disclosure solves the above shortcomings by providing a method for super-resolution imaging of microvessels using an ultrasound system. Ultrasound data acquired from a region of interest in an object using an ultrasound system is provided to a computer system, and a microbubble contrast agent is present in the object when the ultrasound data is acquired. Microbubble signal data is generated using a computer system by isolating microbubble signals in the ultrasound data from other signals in the ultrasound data. Microbubbles are located in the microbubble signal data by processing the microbubble signal data with a computer system to determine the spatial position associated with the microbubbles in the microbubble signal data, thereby generating located microbubble data. The located microbubble data is input to a Kalman filter, which is configured to correct or smooth at least one of the microbubble motion trajectories to generate Kalman-filtered microbubble data. Super-resolution microvascular images are generated based at least in part on the Kalman-filtered microbubble data.

[0007] The foregoing aspects and other aspects and advantages of the present disclosure will become apparent from the following description. In the specification, reference is made to the accompanying drawings, which form a part thereof, and in which preferred embodiments are shown by way of illustration. Such embodiments do not necessarily represent the full scope of the invention, and therefore reference is made to the claims and are used herein to interpret the scope of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a flow chart setting forth the steps of an exemplary method for generating super-resolution images of microvasculature using ultrasound imaging for Kalman filter-based inpainting.

[0009] Figure 2 Examples of microvessel density maps (upper row) and microvascular flow maps (lower row) generated by direct accumulation techniques (first column), microbubble motion trajectories using linear fitting constraints (second column), and accumulation using the systems and methods described in the present disclosure (third column) are shown.

[0010] Figure 3 is a block diagram of an example system that may implement the methods described in this disclosure.

[0011] Figure 4 It shows that it can be achieved Figure 3 A block diagram of the hardware components of the system is shown.

[0012] Figure 5 is a block diagram of an example ultrasound imaging system that may be used when implementing the systems and methods described in the present disclosure. DETAILED DESCRIPTION

[0013] This paper describes the system and method of using ultrasonic super-resolution imaging, wherein the microvascular repair technology based on Kalman filter is used to promote the robust super-resolution imaging with limited or otherwise lost microbubble signals.The system and method described in the present disclosure can be combined with any suitable microbubble tracking method, including local or global microbubble tracking methods.The position of a single microbubble that can be tracked in N continuous frames can be used as the input of a Kalman filter.The Kalman filter can be configured with a plurality of different states and a suitable transfer function.

[0014] As an example, a Kalman filter may be configured with four states (eg, lateral microbubble displacement d x ; Axial microbubble displacement d z ; Transverse flow velocity v x ; and axial velocity v z ) and a linear conversion function, such as d(t)=d(t-1)+v(t-1)Δt. After Kalman filtering, the estimated microbubble positions with flow velocities higher than a preset velocity limit will be rejected, and then a microbubble trajectory repair process based on interpolation (e.g., spline interpolation) is performed. The interpolation factor can be adaptively determined by the local microbubble flow velocity.

[0015] To achieve super-resolution ultrasound imaging, a common strategy is to create isolated point sources of microbubbles, whose central locations are then used to form super-resolution images. Previous super-resolution methods have facilitated microbubble separation by diluting microbubble solutions or by using high-frame-rate ultrasound to monitor the "blinking" events of microbubbles, which can isolate individual microbubble signals from a large number of microbubbles. In both approaches, the tracking and accumulation of microbubbles are susceptible to noise and tissue motion in in vivo imaging. Using the systems and methods described in the present disclosure, a series of processing and repair methods are used to enhance the quality of ultrasound super-resolution imaging and improve the accuracy of microbubble tracking.

[0016] Reference now Figure 1 , a flow chart illustrating steps of an example method for using an ultrasound system to generate a super-resolution image of microvessels in a subject to which a microbubble contrast agent has been administered. In general, super-resolution refers to a resolution that is enhanced relative to the resolution achievable by the imaging system. For example, a super-resolution ultrasound image may refer to an image having a resolution better than the diffraction limit of ultrasound.

[0017] The method includes providing ultrasound data to a computer system, as shown in step 102. In some embodiments, providing ultrasound data to a computer system may include retrieving previously acquired ultrasound data from a memory or other data storage, which may be part of or separate from the computer system. In some other embodiments, providing ultrasound data may include acquiring such data using an ultrasound system and providing the acquired data to a computer system, which may be part of or separate from the ultrasound system.

[0018] The ultrasound data may be ultrasound radio frequency data, ultrasound in-phase quadrature ("IQ") data, etc. In general, the ultrasound data includes one or more spatial dimensions, which may include a transverse dimension, an axial dimension, an altitude dimension, a time dimension, and combinations thereof. For example, the ultrasound data may include two spatial dimensions, such as a transverse and an axial dimension. The ultrasound data may also include a time dimension, such as a slow time dimension (i.e., a time direction along which a plurality of ultrasound signals are collected).

[0019] As described above, ultrasound data is obtained from an object to which a microbubble contrast agent has been administered. In some embodiments, different microbubbles (e.g., microbubbles of different sizes) with different resonant ultrasound frequencies can be used for imaging, so that by selecting a specific ultrasound frequency (e.g., transmitting or receiving at a specific frequency), only a subset of selected microbubbles will be imaged, thereby forming ultrasound data containing an isolated microbubble source. As another example, an ultrasound pulse with energy sufficient to rupture a certain number of microbubbles can be used, wherein the ruptured microbubbles then release free bubbles from the microcapsule and produce an ultrasound signal with an amplitude different from that of the complete microbubble. This effectively creates an isolated microbubble source that can be used for super-resolution imaging. As another example, using an ultrafast plane wave imaging acquisition mode, microbubble motion, rupture, and small phase changes may appear in the form of a "scintillation" event, which can be sensitively detected by ultrafast imaging. These scintillation events can be used as an isolated microbubble source for super-resolution imaging. In some cases, a post-processed spatiotemporal filter (e.g., a clutter filter based on a high-pass filter or a clutter filter based on spatiotemporal singular value decomposition (SVD)) can be used to extract the scintillation microbubble signal.

[0020] Microbubble signals can be obtained from both linear and nonlinear components of ultrasound. The linear component is usually at the fundamental frequency of the applied ultrasound, and the nonlinear component can be at two harmonic frequencies of the applied ultrasound, at the fundamental frequency of the applied ultrasound, at the superharmonic component of the applied ultrasound (e.g., 1.5 times of the fundamental frequency), at the subharmonic component of the applied ultrasound (e.g., 0.5 times of the fundamental frequency) or a combination thereof. For example, the nonlinearity introduced by the imaging method based on amplitude modulation can be at the fundamental frequency. For linear or nonlinear imaging, the above-mentioned spatiotemporal clutter filter can be used to enhance the extraction of isolated microbubble signals.

[0021] As shown in step 104, microbubbles are positioned in the microbubble signal data. Typically, the process includes identifying the position of the microbubble in each time frame of the microbubble signal data. For example, the center position of each isolated microbubble signal is positioned so that the motion of the microbubble can be tracked over time. The center position of the located microbubble can also be used to build a super-resolution microvascular image, and tracks the motion of the microbubble to calculate hemodynamic measurements, such as blood flow velocity.

[0022] As shown in step 106, persistence control is applied to the located microbubble signal to facilitate robust microbubble tracking. As an example, the persistence control technology described in PCT application number PCT / US / 18 / 035147 can be implemented, and the application is incorporated herein by reference in its entirety. When the same microbubble can be paired in p consecutive frames, persistence control regards the microbubble signal as reliable, thereby being regarded as a reliable microbubble signal. The microbubble signal that is considered to be reliable can be used in the final microbubble signal accumulation step to generate microvascular density and microvascular flow velocity images. As a non-limiting example, for p=3, the same microbubble must be paired between frame n and frame n+1, n+2 and n+3, respectively, as shown in FIG. Figure 1 The positions of individual microbubbles tracked in p consecutive frames are shown as yellow dashed boxes in FIG.

[0023] As shown in step 108, the microbubble position data is then input to a Kalman filter, which is appropriately configured to smooth the microbubble motion trajectory to obtain a more robust microvascular density map and a more reliable microvascular flow velocity estimate. The Kalman filter can be a linear Kalman filter or a nonlinear Kalman filter (e.g., a lossless Kalman filter, an extended Kalman filter).

[0024] In general, a Kalman filter is an algorithm that uses a series of measurements observed over time (containing statistical noise and measurement errors) to predict the state of a variable. By estimating the joint probability distribution over the variables in each time frame, the Kalman filtered measurements will be more accurate than results based on a single measurement alone. For a random dynamic system, the system state X at time k k and its observed value Y k as follows,

[0025] X k =F k-1 X k-1 +W k-1 (1);

[0026] Y k =H k X k +Z k (2).

[0027] NoiseW k and Z k The covariances of are Q k and R k The predicted system state at time frame k+1 is given by

[0028]

[0029]

[0030] in is based on the observed value Y 0 ,…,Y k-1 An estimate of the system state at time k, and is the error covariance matrix.

[0031] Innovation I defines the difference between observed and predicted values k And its covariance can be calculated as:

[0032]

[0033]

[0034] Then, the Kalman gain is given by:

[0035]

[0036] Estimated system status and the error covariance Updated by:

[0037]

[0038] E k+1|k+1 =(Id-K k H k )E k+1|k (9)

[0039] Here, the Kalman filter is configured to be applied to super-resolution imaging to correct and smooth the microbubble motion trajectory to obtain a more robust microvascular density map and a more reliable microvascular flow velocity estimate. For the super-resolution imaging system, the actual microbubble position X(t) and the observed microbubble position Y(t) are,

[0040] X(t+1)=FX(t)+W(t) (10);

[0041] Y(t)=HX(t)+V(t) (11).

[0042] An example implementation of microbubble trajectory correction and velocity estimation using a Kalman filter is as follows:

[0043]

[0044] New position (x 1 (t),x 2 (t)) is the old position (x 1 (t-1),x 2 (t-1)) plus the velocity (dx 1 (t-1),dx 2(t-1)) plus the noise w. The observed microbubble position is (y 1 (t),y 2 (t)), where:

[0045]

[0046] The setup for super-resolution imaging using equations (12) and (13) can then be integrated into the recursive algorithm described in equations (3)–(9) for Kalman filtering. After the correction step, a Kalman smoother can be applied to the Kalman filtered data to further remove anomalous data points. The previous steps (equations (12) and (13)) can be viewed as forward Kalman filtering, where only past observed data (e.g., t=0, 1, …, t-1) are used. For the Kalman smoother, all observed data (e.g., t=0, 1, …, T) and all data filtered by the forward Kalman filter are filtered backward in time. The Kalman filtering process can reliably exclude erroneous microbubble trajectories and flow rates. An example implementation of a Kalman filter is in Figure 1 is shown in the green box.

[0047] After Kalman filtering, two constraints can be applied to further improve the robustness of microbubble tracking and flow rate calculation. A microbubble motion direction constraint can be applied at step 110, and a microbubble motion acceleration constraint can be applied at step 112.

[0048] The motion direction constraint requires that the change in the motion direction of the microbubble within p consecutive frames of continuous microbubble pairing is not greater than a predetermined threshold. The motion angle of the microbubble from position n to position n+1 can be given as α n , where position n is the origin of the coordinates. Similarly, the angle of motion of the microbubble from position n+1 to position n+2 can be given as β n+1 , where position n+1 is the origin. The motion direction constraint restricts the change of the motion direction of the microbubble relative to its previous trajectory to no more than a threshold angle θ thr , and can be expressed as:

[0049] Δθ n =|β n+1 -α n |<θ thr (14);

[0050] The motion direction constraint rejects motions with a direction greater than a predetermined threshold θ. thr One or more movement angles Δθ n For example, such a threshold may be π / 2 (i.e., one quadrant of the Cartesian coordinate system).

[0051] For the motion acceleration constraint, sharp accelerations are often caused by erroneous microbubble signals that produce very high microbubble / blood velocity estimates. In order to reject these erroneous microbubble signals, an acceleration constraint can be imposed on the measured microbubble trajectories. This constraint can be given by:

[0052] For n = 1, 2, ..., p-1 (15);

[0053] where v n+1 and v n is the Kalman-filtered microbubble velocity in two consecutive frames, FR is the frame rate, a is the acceleration, and a thr is a threshold acceleration value, which in some cases may represent the maximum absolute value of acceleration allowed. As a non-limiting example, a thr =1000mm / s 2 The threshold value.

[0054] After applying the Kalman filter and the above constraints, the remaining microbubble signals can be used to reliably repair the tissue microvasculature. In order to effectively repair the missing data points on the trajectory of microbubble propagation, an adaptive interpolation method can be used for the repair process, as shown in step 114. Instead of using a fixed interpolation factor, the interpolation factor can be adaptively determined by the estimated flow velocity at each microbubble position. Fast-moving microbubbles will cause the gaps between adjacent detected microbubble positions to be wider, so a higher interpolation factor may be required to fill the gaps between adjacent detected microbubble positions. Slowly moving microbubbles will cause the gaps between adjacent microbubble positions to be smaller, so a lower interpolation factor may be required to fill the gaps. As a non-limiting example, in Figure 1 , four consecutive positions of microbubbles are depicted with red dots. If the microbubble flow velocity between the positions of frame n and frame n+1 is V, and between frame n+1 and frame n+2 is 2V (i.e., twice as fast as the velocity between frame n and n+1), then twice as many points will be inserted into the positions between frame n+1 and frame n+2 than between frame n and n+1.

[0055] After the microbubbles have been located and tracked in the microbubble signal data, as shown in step 116, one or more microvessel images are generated based on the location and tracking results. As an example, a microvessel image may include a microbubble position map accumulated in all acquisition frames. As another example, a microvessel image may include a blood flow velocity map, in which blood flow velocity values ​​are assigned to all positions where microbubbles are detected. Therefore, in some cases, a microvessel image may depict microvessel density or microvessel blood flow velocity. The accumulated microbubble position map depicts the number of times microbubbles appear at a certain position. Typically, larger blood vessels have more microbubbles flowing through them in a given time interval, and therefore appear brighter than smaller blood vessels, which have fewer microbubbles flowing through them in the same time interval.

[0056] The systems and methods described in this disclosure enable significant improvements over previous data accumulation and linear fit constrained accumulation techniques. Examples of microvessel density maps and microvessel flow maps generated using these various techniques are shown in Figure 2 Compared to other restoration methods such as linear fitting, the systems and methods described in the present disclosure can robustly reconstruct vessels with missing data points, better preserve microvascular curvature and size, and better resolve adjacent vessels. By allowing robust microvascular characterization under limited or missing microbubble signals, the systems and methods described in the present disclosure can accelerate and improve the robustness of super-resolution imaging.

[0057] Reference now Figure 3 , shows an example of a system 300 for generating super-resolution microvascular images according to some embodiments of the systems and methods described in the present disclosure. Figure 3 As shown, computing device 350 may receive one or more types of data (e.g., ultrasound data) from image source 302, which may be an ultrasound image source. In some embodiments, computing device 350 may execute at least a portion of super-resolution image generation system 304 to generate a super-resolution microvascular image from the data received from image source 302.

[0058] Additionally or alternatively, in some embodiments, computing device 350 may transmit information about data received from image source 302 to server 352 via communication network 354, which may execute at least a portion of super-resolution image generation system 304 to generate a super-resolution microvascular image from the data received from image source 302. In such embodiments, server 352 may return information to computing device 350 (and / or any other suitable computing device) indicating the output of super-resolution image generation system 304 to generate a super-resolution microvascular image from the data received from image source 302.

[0059] In some embodiments, computing device 350 and / or server 352 may be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine executed by a physical computing device, etc. Computing device 350 and / or server 352 may also reconstruct an image from the data.

[0060] In some embodiments, image source 302 may be any suitable source of image data (e.g., measurement data, images reconstructed from the measurement data), such as an ultrasound imaging system, another computing device (e.g., a server storing image data), etc. In some embodiments, image source 302 may be local to computing device 350. For example, image source 302 may be incorporated with computing device 350 (e.g., computing device 350 may be configured as part of a device for capturing, scanning, and / or storing images). As another example, image source 302 may be connected to computing device 350 via a cable, a direct wireless link, etc. Additionally or alternatively, in some embodiments, image source 302 may be located local and / or remote to computing device 350, and may transmit data to computing device 350 (and / or server 352) via a communication network (e.g., communication network 354).

[0061] In some embodiments, the communication network 354 may be any suitable communication network or combination of communication networks. For example, the communication network 354 may include a Wi-Fi network (which may include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, etc., which complies with any suitable standard such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), a wired network, etc. In some embodiments, the communication network 354 may be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi-private network (e.g., a company or university intranet), any other suitable type of network, or any suitable combination of networks. Figure 3 Each of the communication links shown in can be any suitable communication link or combination of communication links, such as a wired link, a fiber optic link, a Wi-Fi link, a Bluetooth link, a cellular link, and the like.

[0062] Reference now Figure 4 , shows an example of hardware 400 that may be used to implement image source 302, computing device 350, and server 352 according to some embodiments of the systems and methods described in the present disclosure. Figure 4As shown, in some embodiments, computing device 350 may include processor 402, display 404, one or more inputs 406, one or more communication systems 408, and / or memory 410. In some embodiments, processor 402 may be any suitable hardware processor or combination of processors, such as a central processing unit ("CPU"), a graphics processing unit ("GPU"), etc. In some embodiments, display 404 may include any suitable display device, such as a computer monitor, a touch screen, a television, etc. In some embodiments, input 406 may include any suitable input device and / or sensor that can be used to receive user input, such as a keyboard, a mouse, a touch screen, a microphone, etc.

[0063] In some embodiments, the communication system 408 may include any suitable hardware, firmware, and / or software for communicating information over the communication network 354 and / or any other suitable communication network. For example, the communication system 408 may include one or more transceivers, one or more communication chips and / or chipsets, etc. In more specific examples, the communication system 408 may include hardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, etc.

[0064] In some embodiments, memory 410 may include any suitable storage device or devices that may be used to store instructions, values, data, etc., which may be used, for example, by processor 402 to present content using display 404, communicate with server 352 via communication system 408, and the like. Memory 410 may include any suitable volatile memory, non-volatile memory, storage device, or any suitable combination thereof. For example, memory 410 may include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid-state drives, one or more optical drives, and the like. In some embodiments, memory 410 may have encoded thereon or otherwise stored thereon a computer program that is used to control the operation of computing device 350. In such embodiments, processor 402 may execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables), receive content from server 352, transmit information to server 352, and the like.

[0065] In some embodiments, server 352 may include processor 412, display 414, one or more inputs 416, one or more communication systems 418, and / or memory 420. In some embodiments, processor 412 may be any suitable hardware processor or combination of processors, such as a CPU, GPU, etc. In some embodiments, display 414 may include any suitable display device, such as a computer monitor, a touch screen, a television, etc. In some embodiments, input 416 may include any suitable input device and / or sensor that can be used to receive user input, such as a keyboard, a mouse, a touch screen, a microphone, etc.

[0066] In some embodiments, the communication system 418 may include any suitable hardware, firmware, and / or software for communicating information over the communication network 354 and / or any other suitable communication network. For example, the communication system 418 may include one or more transceivers, one or more communication chips and / or chipsets, etc. In more specific examples, the communication system 418 may include hardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, etc.

[0067] In some embodiments, memory 420 may include any suitable storage device or multiple storage devices that can be used to store instructions, values, data, etc., which can be used, for example, by processor 412 to present content using display 414, communicate with one or more computing devices 350, etc. Memory 420 may include any suitable volatile memory, non-volatile memory, storage device, or any suitable combination thereof. For example, memory 420 may include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid-state drives, one or more optical drives, etc. In some embodiments, memory 420 may have encoded thereon a server program for controlling the operation of server 352. In such an embodiment, processor 412 may execute at least a portion of the server program to transmit information and / or content (e.g., data, images, user interfaces) to one or more computing devices 350, receive information and / or content from one or more computing devices 350, receive instructions from one or more devices (e.g., personal computers, laptops, tablet computers, smart phones), etc.

[0068] In some embodiments, the image source 302 may include a processor 422, one or more image acquisition systems 424, one or more communication systems 426, and / or a memory 428. In some embodiments, the processor 422 may be any suitable hardware processor or combination of processors, such as a CPU, a GPU, etc. In some embodiments, the one or more image acquisition systems 424 are generally configured to acquire data, images, or both, and may include an ultrasonic transducer. Additionally or alternatively, in some embodiments, the one or more image acquisition systems 424 may include any suitable hardware, firmware, and / or software for coupling to and / or controlling the operation of an ultrasonic transducer or an ultrasonic imaging system. In some embodiments, one or more portions of the one or more image acquisition systems 424 may be removable and / or replaceable.

[0069] Note that, although not shown, image source 302 may include any suitable input and / or output. For example, image source 302 may include an input device and / or sensor operable to receive user input, such as a keyboard, a mouse, a touch screen, a microphone, a trackpad, a trackball, etc. As another example, image source 302 may include any suitable display device, such as a computer monitor, a touch screen, a television, etc., one or more speakers, etc.

[0070] In some embodiments, communication system 426 may include any suitable hardware, firmware, and / or software for communicating information to computing device 350 (and in some embodiments, via communication network 354 and / or any other suitable communication network). For example, communication system 426 may include one or more transceivers, one or more communication chips and / or chipsets, etc. In more specific examples, communication system 426 may include hardware, firmware, and / or software that can be used to establish a wired connection using any suitable port and / or communication standard (e.g., VGA, DVI video, USB, RS-232, etc.), a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, etc.

[0071] In some embodiments, memory 428 may include any suitable storage device that may be used to store instructions, values, data, etc., which may be used, for example, by processor 422 to control one or more image acquisition systems 424 and / or receive data from one or more image acquisition systems 424; acquire images from the data; present content (e.g., images, user interfaces) using a display; communicate with one or more computing devices 350; etc. Memory 428 may include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 428 may include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid-state drives, one or more optical drives, etc. In some embodiments, memory 428 may have encoded thereon or otherwise stored therein a program for controlling the operation of image source 302. In such an embodiment, the processor 422 can execute at least a portion of the program to generate images, transmit information and / or content (e.g., data, images) to one or more computing devices 350, receive information and / or content from one or more computing devices 350, receive instructions from one or more devices (e.g., a personal computer, laptop computer, tablet computer, smartphone, etc.), etc.

[0072] In some embodiments, any suitable computer-readable medium may be used to store instructions for performing the functions and / or processes described herein. For example, in some embodiments, the computer-readable medium may be temporary or non-temporary. For example, non-temporary computer-readable media may include media such as magnetic media (e.g., hard disk, floppy disk), optical media (e.g., compact disk, digital video disk, Blu-ray disk), semiconductor media (e.g., random access memory ("RAM"), flash memory, electrically programmable read-only memory ("EPROM"), electrically erasable programmable read-only memory ("EEPROM")), any suitable medium that is not transient or without any permanent representation during transmission, and / or any suitable tangible medium. As another example, transient computer-readable media may include signals on a network, in wires, conductors, optical fibers, circuits, or in any suitable medium that is not transient or without any permanent representation during transmission, and / or any suitable intangible medium.

[0073] Figure 5An example of an ultrasound system 500 is illustrated that can implement the methods described in the present disclosure. The ultrasound system 500 includes a transducer array 502 that includes a plurality of individually driven transducer elements 504. The transducer array 502 can include any suitable ultrasound transducer array, including a linear array, a curved array, a phased array, and the like. Similarly, the transducer array 502 can include a 1D transducer, a 1.5D transducer, a 1.75D transducer, a 2D transducer, a 3D transducer, and the like.

[0074] When excited by the transmitter 506, a given transducer element 504 generates a burst of ultrasonic energy. Ultrasonic energy (e.g., echoes) reflected back to the transducer array 502 from the object or subject being investigated is converted into an electrical signal (e.g., an echo signal) by each transducer element 504 and can be applied individually to the receiver 508 through a set of switches 510. The transmitter 506, receiver 508, and switches 510 operate under the control of a controller 512, which may include one or more processors. As an example, the controller 512 may include a computer system.

[0075] The transmitter 506 can be programmed to transmit unfocused or focused ultrasound waves. In certain configurations, the transmitter 506 can also be programmed to transmit diverging waves, spherical waves, cylindrical waves, plane waves, or a combination thereof. In addition, the transmitter 506 can be programmed to transmit spatially or temporally encoded pulses.

[0076] Receiver 508 may be programmed to implement an appropriate detection sequence for the imaging task at hand. In some embodiments, the detection sequence may include one or more of progressive scanning, compound plane wave imaging, synthetic aperture imaging, and compound diverging beam imaging.

[0077] In some configurations, the transmitter 506 and the receiver 508 can be programmed to achieve a high frame rate. For example, a frame rate associated with an acquisition pulse repetition frequency ("PRF") of at least 100 Hz can be achieved. In some configurations, the ultrasound system 500 can sample and store at least one hundred sets of echo signals in the time direction.

[0078] Controller 512 may be programmed to perform the imaging sequence using techniques known in the art.In some embodiments, controller 512 receives user input defining various factors used in the design of the imaging sequence.

[0079] Scanning may be performed by setting switch 510 to its transmit position, thereby instructing transmitter 506 to temporarily turn on to energize transducer elements 504 according to a desired imaging sequence during a single transmission event. Switch 510 may then be set to its receive position and measure subsequent echo signals generated by transducer elements 504 in response to one or more detected echoes and apply the signals to receiver 508. The individual echo signals from transducer elements 504 may be combined in receiver 508 to produce a single echo signal.

[0080] The echo signal is transmitted to a processing unit 514, which can be executed by a hardware processor and memory to process the echo signal or an image generated from the echo signal. For example, the processing unit 514 can use the method described in the present disclosure to generate a super-resolution microvascular image. For example, the processing unit 514 can use the method described in the present disclosure to isolate the microbubble signal to generate microbubble signal data, locate the microbubble in the microbubble signal data, track the microbubble position through a time frame, accumulate the microbubble position, and generate a microvascular image. The image generated by the processing unit 514 from the echo signal can be displayed on a display system 516.

[0081] The disclosure has described one or more preferred embodiments, and it should be understood that many equivalents, alternatives, variations and modifications other than those expressly stated are possible and within the scope of the invention.

Claims

1. A method for super-resolution imaging of microvessels using an ultrasound system, the method comprising: (a) providing ultrasound data to a computer system, the ultrasound data having been acquired by an ultrasound system from a region of interest of a subject, wherein a microbubble contrast agent is present in the subject when the ultrasound data is acquired; (b) generating microbubble signal data using the computer system by isolating microbubble signals in the ultrasound data from other signals in the ultrasound data; (c) localizing microbubbles in the microbubble signal data by processing the microbubble signal data with the computer system to determine spatial locations associated with microbubbles in the microbubble signal data, generating an output as localized microbubble data; (d) inputting the localized microbubble data into a Kalman filter configured to at least one of correct or smooth the microbubble motion trajectory to produce an output as Kalman filtered microbubble data; and (e) generating a super-resolution microvascular image based at least in part on the Kalman filtered microvesicle data, Wherein in step (d), the Kalman filtered microbubble data is processed using a microbubble motion direction constraint to exclude microbubble positions that change motion direction by an angle greater than a threshold within a selected number of time frames. The method according to claim 1 , wherein the threshold is 90 degrees.

3. The method of claim 1, wherein in step (d) the Kalman filtered microbubble data is processed using a microbubble motion acceleration constraint to exclude microbubble positions having a motion acceleration greater than a threshold within a selected number of time frames. The method according to claim 3 , wherein the threshold value is 1,000 mm / s 2 .

5. The method of claim 1, wherein step (e) comprises using an adaptive interpolation method using an adaptively determined interpolation factor based on local microbubble flow velocity.

6. The method of claim 5, wherein the interpolation factor is adaptively increased between microbubble positions associated with fast-moving microbubbles. The method of claim 1 , wherein the Kalman filter is a linear Kalman filter. The method of claim 1 , wherein the Kalman filter is a nonlinear Kalman filter.

9. The method of claim 8, wherein the nonlinear Kalman filter is a lossless Kalman filter.

10. The method of claim 1, wherein the Kalman filter is configured to correct microbubble motion trajectories using forward Kalman filtering, wherein only localized microbubble signal data observed from past time frames are used. 11 . The method of claim 1 , wherein the Kalman filter is configured to smooth microbubble motion trajectories by temporally filtering the localized microbubble signal data backward using all observed time frames in the localized microbubble signal data.

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