System and method for event-driven ultrasonic sampling

Abstract

The present invention relates, in general, to ultrasound imaging, and more specifically, to a system and method for providing event-driven adjustment of the sampling rate in an ultrasound imaging device. The event-driven sampling algorithm causes a processor to analyze received data, identify one or more characteristics associated with the received data, and further detect the occurrence of one or more events. One or more events include changes in one or more characteristics compared to defined thresholds for one or more characteristics. Furthermore, the event-driven algorithm causes the processor to adjust the sampling rate of the imaging device based on the detected occurrence of one or more events.

Description

Technical Field

[0001] (Cross - reference to Related Applications) This application claims the priority and benefit of U.S. Provisional Application No. 63 / 525,274, filed on July 6, 2023, the content of which is incorporated herein by reference in its entirety.

[0002] (Field of the Invention) The present invention generally relates to ultrasonic imaging, and more specifically, to systems and methods for providing event - driven ultrasonic imaging.

Background Art

[0003] (Background) Ultrasonic imaging is a medical imaging technique for imaging organs and soft tissues within the human body. Ultrasonic images are produced based on the reflection of high - frequency sound waves from body structures. The intensity (amplitude) of the sound signal, combined with the time required for the wave to travel through the body, provides the information necessary to produce an image.

[0004] Ultrasonic imaging can be useful for physicians to evaluate, diagnose, and treat various medical conditions. When making a diagnosis based on an ultrasound examination, a physician must rely on proper image quality, access to appropriate views, and sufficient quantification of all relevant structures and flows.

[0005] It should be noted that there seems to be a misspelling in line , where "十七" is likely an error. I translated it as it is presented, but it might need to be corrected in the original text.For example, catheter-based intravascular ultrasound imaging techniques used within blood vessels (e.g., intravascular ultrasound (IVUS) or intracardiac echocardiography (ICE)) are generally performed using two-dimensional (2D) ultrasound imaging. In an IVUS / ICE imaging system, an ultrasound transducer assembly is attached to the distal end of a catheter. The catheter is carefully maneuvered through the patient's body to the area of ​​interest, such as within the coronary arteries (in the case of IVUS) or the right atrium (in the case of ICE). The transducer assembly transmits ultrasound waves and receives echoes from those waves. The received echoes are then converted into electrical signals and transmitted to a processing device, where the resulting ultrasound image of the area of ​​interest may be displayed.

[0006] In typical ultrasound systems configured to visualize internal body regions, dynamic forces are often employed, resulting in dynamic movement of the body region over time. These dynamic forces and movement make it difficult to stabilize the internal imaging device and generate consistent and accurate images, especially when imaging of structures cannot be enabled in real time (e.g., >20 Hz). As a result, captured images often lack the necessary quality required to prescribe appropriate treatment or therapy. Due to the dynamic forces and movement at play, internal real-time imaging is limited to small two-dimensional areas or limited three-dimensional volume regions, respectively. Furthermore, difficult engineering trade-offs exist between system complexity, achievable image quality, and the resources required for imaging. Thus, the final quality of images acquired through ultrasound scanning is limited by the technical specifications of the equipment, the propagation of ultrasound through the tissue being analyzed, and the method used to reconstruct the image. [Overview of the project] [Means for solving the problem]

[0007] (summary) This invention recognizes the limitations of current ultrasound imaging systems, particularly the limitations on resources required to provide the sustainable data rate required for imaging. The systems and methods of the present invention utilize one or more novel algorithms to reduce the required average sampling rate for ultrasound received data, particularly in the context of digitization at the tip of a catheter ultrasound device where available space is significantly constrained. In particular, the present invention provides a system and method for ultrasound image sampling that optimizes the resources required for obtaining meaningful information and transferring it to an external processing system. By sampling only meaningful information, the average data rate transmission requirements are reduced, overall power consumption and heat dissipation within the imaging device are minimized, and an optimized interconnection assembly is provided between different modules of the signaling chain.

[0008] The system and method of the present invention utilize the properties of ultrasound data generated by sampling only when the received data has changed sufficiently according to a previously defined threshold that can be classified as an “event.” This threshold may be determined, for example, based on the clinical relevance of the structure being imaged and the characteristics of the received data. In this way, clinically relevant structures are sampled at a high data rate, while other anatomical areas are not sampled at a high data rate, thus reducing overall power consumption and heat dissipation within the imaging device. The system and method of the present invention includes a novel event-driven architecture that utilizes a novel sampling algorithm that enables sampling beyond a clearly defined level crossing at a set voltage level in the conventional sampling architecture.

[0009] Therefore, the present invention provides improved imaging, particularly in catheter-based ultrasound imaging, to minimize data transmission rates and maximize clinically relevant image quality.

[0010] An aspect of the present invention provides a system for image sampling, comprising a hardware processor coupled to a non-transient computer-readable memory containing instructions, the instructions being executable by the processor to cause the processor to receive data from an imaging device and to invoke an event-driven sampling algorithm. The event-driven sampling algorithm causes the processor to analyze the received data, identify one or more characteristics associated with the received data, and further detect the occurrence of one or more events. One or more events include changes in one or more characteristics compared to defined thresholds relating to one or more characteristics. Furthermore, the event-driven algorithm causes the processor to adjust the sampling rate of the imaging device based on the detected occurrence of one or more events.

[0011] In some embodiments, the defined threshold is determined, at least in part, based on the clinical relevance of the structure to be imaged and / or one or more characteristics of the received data.

[0012] In some embodiments, the received data comprises a digitized voltage signal. Therefore, the processor is configured, in some embodiments, to access the raw digitized voltage signal and to adjust one or more sampling parameters to minimize the data transmission rate and maximize the image quality of clinically relevant images. The defined threshold is, in some embodiments, a digitized voltage encoded at a bit depth lower than the full sampling bit depth. In some embodiments, the defined threshold is a change in output voltage by a defined parameter relative to a previous output voltage. For example, image sampling by the imaging device is triggered at a full sampling rate over a set time for each event associated with a change in output voltage by a defined parameter relative to a previous output voltage. In certain embodiments, the defined threshold is a change in output voltage by a set parameter at a previously stored threshold, and the threshold is updated and stored when image sampling by the imaging device is triggered. Furthermore, in some embodiments, the defined threshold is one or more logarithmically spaced voltage levels.

[0013] In other embodiments, the received data comprises an analog voltage signal.

[0014] In some embodiments, the defined threshold is the change in amplitude signal by a set parameter in a previously stored threshold amplitude signal, and only the carrier amplitude and phase are sampled, with the phase sampled in parallel with the amplitude and encoded at a bitrate lower than 16 bits. For example, in some embodiments, the carrier amplitude and phase are extracted via I / Q demodulation.

[0015] In certain embodiments, the imaging device comprises a transducer having an array of individual imaging elements. For example, in some embodiments, the transducer comprises a micro-electromechanical system (MEMS) based micromachined ultrasonic transducer configured as a two-dimensional (2D) array structure. In other embodiments, the imaging element is an acoustic sensor activated by a processor to transmit and / or receive multiple incident acoustic wave signals as wave data. For example, the wave data comprises at least one of plane wave data and divergent wave data associated with one or more wave transmission-reception cycles performed by the imaging element. In some embodiments, the wave data is 3D image data.

[0016] Furthermore, in some embodiments, the imaging device comprises a catheter-based ultrasound imaging device configured to transmit ultrasound pulses into intravascular and / or intracardiac tissue and to receive echoes of the ultrasound pulses.

[0017] In certain embodiments, the imaging device is a minimally invasive, implantable device. Specifically, adjusting the sampling rate reduces the overall power consumption and heat dissipation of the device. In some embodiments, adjusting the sampling rate results in a reduction of the average sampling rate of the data received from the imaging device.

[0018] In some embodiments, the processor is embedded as part of an application-specific integrated circuit (ASIC).

[0019] In another aspect, the present invention provides a method for image sampling. The method comprises providing a hardware processor coupled to a non-transient computer-readable memory containing instructions, which are executable by the processor to cause the processor to receive data from an imaging device and to invoke an event-driven sampling algorithm. The event-driven algorithm causes the processor to analyze the received data, identify one or more characteristics associated with the received data, and further detect the occurrence of one or more events, wherein the one or more events comprise a change in one or more characteristics compared to a defined threshold for one or more characteristics, and adjust the sampling rate of the imaging device based on the detected occurrence of one or more events.

[0020] In some embodiments of this method, the received data comprises a digitized voltage signal. For example, in some embodiments, the processor is configured to access the raw digitized voltage signal and to adjust one or more sampling parameters to minimize the data transmission rate and maximize the image quality of clinically relevant images. In certain embodiments of this method, the defined threshold is the digitized voltage encoded at a bit depth lower than the full sampling bit depth. In some embodiments, the defined threshold is the change in output voltage by a defined parameter relative to the previous output voltage.

[0021] For example, image sampling by the imaging device is triggered at a full sampling rate over a set time for each event associated with an output voltage change by a defined parameter relative to the previous output voltage. Furthermore, in some embodiments of the method, the defined threshold is the output voltage change by a set parameter relative to a previously stored threshold, and the threshold is updated and stored when image sampling by the imaging device is triggered. In certain embodiments, the defined threshold is one or more logarithmically spaced voltage levels.

[0022] In some embodiments of the present method, the received data comprises an analog voltage signal.

[0023] In various embodiments of the present method, the defined threshold is a change in the amplitude signal by a set parameter in a previously stored threshold amplitude signal, and only the amplitude and phase of the carrier wave are sampled, the phase is sampled in parallel with the amplitude, and is encoded at a bit rate lower than 16 bits. For example, in some embodiments, the amplitude and phase of the carrier wave are extracted via I / Q demodulation.

[0024] In some embodiments of the present method, the imaging device comprises a transducer comprising an array of individual imaging elements. Further, the transducer, in some embodiments, comprises a microelectromechanical system (MEMS)-based micromachined ultrasonic transducer configured as a two-dimensional (2D) array structure. The imaging elements are acoustic sensors that are activated by a processor to transmit and / or receive a plurality of incident acoustic wave signals as wave data. For example, in some embodiments, the wave data comprises at least one of plane wave data and divergent wave data associated with one or more wave transmission-reception cycles performed by the imaging elements. In some embodiments, the wave data is full circumference three-dimensional (3D) image data. In certain embodiments, the imaging device comprises a catheter-based ultrasonic imaging device configured to transmit ultrasonic pulses into intravascular and / or intracardiac tissue and then receive the echoes of the ultrasonic pulses. In some embodiments, the imaging device is a minimally invasive implantable device. Further, in some embodiments, adjustment of the sampling rate reduces the overall power consumption and heat dissipation of the device.

[0025] In some embodiments of the present method, adjustment of the sampling rate results in a reduction in the average sampling rate of the received data from the imaging device.

[0026] In some embodiments of the method of the present invention, the processor is embedded as part of an application specific integrated circuit (ASIC).

Brief Description of Drawings

[0027] [Figure 1] Figures 1A and 1B are schematic illustrations of an exemplary ultrasound system in which the systems and methods of the present invention can be used.

[0028] [Figure 2] Figure 2 is a perspective view of an imaging catheter in which the systems and methods of the present invention can be used.

[0029] [Figure 3] Figure 3 illustrates a system according to an embodiment of the present invention.

[0030] [Figure 4] Figure 4 is a block diagram of a method for providing event-driven sampling according to an embodiment of the present invention.

[0031] [Figure 5A] Figures 5A and 5B show exemplary results comparing an original image and an image determined using a differential algorithm according to an embodiment of the present invention. [Figure 5B] Figures 5A and 5B show exemplary results comparing an original image and an image determined using a differential algorithm according to an embodiment of the present invention.

[0032] [Figure 6] Figure 6 is a graph showing experimental results including the average mean squared error for differential algorithms, differential threshold algorithms, moving threshold algorithms, and triggered burst algorithms (using burst lengths of 5 and 25) per bit level / log2(Δ) using synthetic data.

[0033] [Figure 7]Figure 7 is a graph illustrating experimental results for the average transmission rate (%) for each bit level / log2(Δ) of differential algorithms, differential thresholding algorithms, moving thresholding algorithms, and triggered burst algorithms (using burst lengths of 5 and 25) using synthesized data.

[0034] [Figure 8] Figure 8 is a graph illustrating the experimental results of the average mean squared error (preclinical data (2022-07-28_IMMR)) for differential algorithms, differential threshold algorithms, moving threshold algorithms, and triggered burst algorithms (using burst lengths of 5 and 45) per bit level / log2(Δ) using data from anatomical imaging in RA.

[0035] [Figure 9] Figure 9 is a graph showing experimental results for the average transmission rate (%) (preclinical data (2022-07-28_IMMR)) for differential algorithms, differential threshold algorithms, moving threshold algorithms, and triggered burst algorithms (using burst lengths of 5 and 45) per bit level / log2(Δ) using data from anatomical imaging in RA.

[0036] [Figure 10] Figure 10 is a graph illustrating the average mean squared error for each bit level / log2(Δ) differential algorithm, logarithmic algorithm, differential threshold algorithm, moving threshold algorithm, triggered burst algorithm (burst length 5), triggered burst algorithm (burst length 45), and quadratic phase algorithm (bit level 3) using pullback sequences.

[0037] [Figure 11]Figure 11 is a graph illustrating the average transmission rate (%) for each bit level / log2(Δ) of differential algorithms, logarithmic algorithms, differential threshold algorithms, moving threshold algorithms, triggered burst algorithms (burst length 5), triggered burst algorithms (burst length 45), and quadratic phase algorithms (bit level 3), using pullback sequences.

[0038] [Figure 12] Figure 12 illustrates the data transmission histogram for the DIFFERENTIAL algorithm with Δ / bit level 6.

[0039] [Figure 13] Figure 13 illustrates the data transmission histogram for the DIFFERENCE_THRESHOLD algorithm with Δ / bit level 6.

[0040] [Figure 14] Figure 14 illustrates the data transmission histogram for the MOVING_THRESHOLD algorithm with Δ / bit level 6.

[0041] [Figure 15] Figure 15 illustrates the data transmission histogram for the LOGARITHMIC_bit_level_6 algorithm with Δ / bit level 6.

[0042] [Figure 16] Figures 16A and 16B illustrate the average mean squared error and average transmission rate (%) for the bit level / log2(Δ) using a sampling rate of 0.6 for differential, logarithmic, and moving threshold algorithms.

[0043] [Figure 17]Figures 17A and 17B illustrate the average mean squared error and average transmission rate (%) for the bit level / log2(Δ) using a sampling rate of 1.0 for differential, logarithmic, and moving threshold algorithms.

[0044] [Figure 18] Figure 18 shows graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for DIFFERENTIAL_f_sample_1000e-3.

[0045] [Figure 19] Figure 19 illustrates graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for DIFFERENTIAL_f_sample_800e-3.

[0046] [Figure 20] Figure 20 shows graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for DIFFERENTIAL_f_sample_600e-3.

[0047] [Figure 21] Figure 21 shows graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for LOGARITHMIC_f_sample_1000e-3.

[0048] [Figure 22] Figure 22 illustrates graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for LOGARITHMIC_f_sample_800e-3.

[0049] [Figure 23] Figure 23 illustrates graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for LOGARITHMIC_f_sample_600e-3.

[0050] [Figure 24] Figure 24 illustrates graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against Δ(bit_level) for MOVING_THRESHOLD_f_sample_1000e-3.

[0051] [Figure 25] Figure 25 illustrates graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against Δ(bit_level) for MOVING_THRESHOLD_f_sample_800e-3.

[0052] [Figure 26] Figure 26 shows graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against Δ(bit_level) for MOVING_THRESHOLD_f_sample_600e-3.

[0053] [Figure 27] Figure 27 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for DIFFERENTIAL_f_sample_1000e-3 across all events.

[0054] [Figure 28]Figure 28 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for DIFFERENTIAL_f_sample_800e-3 across all events.

[0055] [Figure 29] Figure 29 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for DIFFERENTIAL_f_sample_600e-3 across all events.

[0056] [Figure 30] Figure 30 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for LOGARITHMIC_f_sample_1000e-3 across all events.

[0057] [Figure 31] Figure 31 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for LOGARITHMIC_f_sample_800e-3 across all events.

[0058] [Figure 32] Figure 32 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for LOGARITHMIC_f_sample_600e-3 across all events.

[0059] [Figure 33]Figure 33 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against Δ(bit_level) for MOVING_THRESHOLD_f_sample_1000e-3 across all events.

[0060] [Figure 34] Figure 34 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against Δ(bit_level) for MOVING_THRESHOLD_f_sample_800e-3 across all events.

[0061] [Figure 35] Figure 35 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against Δ(bit_level) for MOVING_THRESHOLD_f_sample_600e-3 across all events. [Modes for carrying out the invention]

[0062] (Detailed explanation) This invention recognizes the limitations of current tissue analysis and visualization using ultrasound technology, namely the required average sampling rate of ultrasound-received data. This invention provides a system and method for reducing the required average sampling rate of ultrasound-received data.

[0063] For example, the sustainable data rate required for imaging exceeds 70.0 gigabits per second (8.86 GB / second). In particular, the digitized (16-bit) output voltage signal of the piezoelectric transducer corresponds to the received ultrasound and is modulated by a signal corresponding to the absorption and reflection of sound pressure by the imaging environment, with a carrier frequency f c The signal has a sinusoidal form and oscillates at a sampling frequency f. s It is sampled in, which is a 128-element transducer, fs =50MHz, imaging depth d=70mm (4,544 samples), Tx pulse repetition frequency f Tx For 10 kHz, this corresponds to a data rate of >70.8 Gb / sec (86.6 Gb / sec). Therefore, the present invention provides a system and architecture including a novel sampling algorithm for providing event-driven ultrasound image sampling.

[0064] Conventional event-driven sampling architectures primarily involve only clearly defined level crossings at set voltage levels. The oscillating nature of ultrasound data means that these defined architectures are unsuitable. This is because level crossings can occur even when clinically relevant data is not available, for example, when imaging a blood pool with low-level oscillations. In contrast, the present invention provides a flexible system and method that minimizes data transmission rates and maximizes clinically relevant image quality by allowing direct access to the raw digitized signal and enabling adjustment of specific event-driven sampling parameters, such as parameter changes.

[0065] Therefore, the present invention provides a sampling scheme that utilizes the characteristics of the data to be sampled. The present invention provides an opportunity for optimizing the resources required for obtaining meaningful information and transferring it to an external processing system. By sampling only meaningful information, the average data rate transmission requirements are reduced, minimizing overall power consumption and heat dissipation within the catheter ultrasound imaging device, for example.

[0066] The system of the present invention may be operably connected to an ultrasound system, including certain hardware and software for providing image reconstruction and imaging assembly control, as described, for example, in Hennersperger et al.'s International PCT Application No. PCT / IB2019 / 000963 (published as WO2020 / 044117), Hennersperger et al.'s U.S. Application Publication No. US2022-0287679A1, and Hennersperger et al.'s U.S. Patent No. 11,382,599 (the contents of which are incorporated herein by reference). The data may be processed using imaging protocols for extracting anatomical and functional information and tissue characteristics, as disclosed in Hennersperger et al.'s International PCT Application No. PCT / IB2019 / 000963 (published as No. WO2020 / 044117), Hennersperger et al.'s U.S. Application Publication No. US2022-0287679A1, and Hennersperger et al.'s U.S. Patent No. 11,382,599 (the contents of which are incorporated herein by reference).

[0067] The system and method of the present invention utilize the nature of ultrasound data generated by the imaging system and sample only when the received data has changed sufficiently according to a previously defined threshold, for example, that is classified as an “event.” The threshold may be determined, for example, based on the clinical relevance of the structure being imaged and the characteristics of the received data. For example, the threshold may be defined to avoid sampling a blood pool, which may be clinically irrelevant at high data rates.

[0068] An aspect of the present invention provides a system for image sampling, comprising a hardware processor coupled to non-transient computer-readable memory. The computer-readable memory contains instructions that the processor can execute to cause the processor to receive data from an imaging device and to invoke an event-driven sampling algorithm. As will be discussed in more detail below, the algorithm causes the processor to analyze the received data, identify one or more characteristics associated with the received data, and further detect the occurrence of one or more events. One or more events may include changes in one or more characteristics compared to defined thresholds for one or more characteristics. The algorithm then causes the processor to adjust the sampling rate of the imaging device based on the detected occurrence of one or more events.

[0069] (Ultrasound imaging system) In general, as is commonly understood, ultrasound imaging (echocardiography) uses high-frequency sound waves to visualize the inside of the body. Because ultrasound images are captured in real time, these images can also show the movement of internal organs as well as fluid flow (e.g., blood flowing through blood vessels). In ultrasound imaging, the imaging device (i.e., transducer, probe, or transducer probe) is placed directly on the skin or inside a body opening (e.g., intravascular ultrasound, intravascular sonication, intracardiac echocardiography).

[0070] The system of the present invention is configured to receive data such as three-dimensional (3D) ultrasound image data from an imaging device.

[0071] In some embodiments, the present invention provides event-driven sampling that enables three-dimensional visualization and tissue characterization in minimally invasive intravascular procedures. Therefore, ultrafast ultrasound imaging techniques, such as plane-wave or divergent-wave imaging, may be required, particularly within the constraints of applications for intravascular and / or intracardiac tissue assessment and analysis. The systems and methods of the present invention enable the direct use of all inherent ultrafast imaging techniques.

[0072] For example, in the context of intracardiac imaging, plane wave imaging may refer to an ultrasound imaging modality in which a plane wavefront can traverse tissue and be partially scattered back to the transducer through the flat transmission of all transducer elements (at different angles) from an angular imaging aperture. From the received radio frequency (RF) (i.e., channel) data, the overall image can be reconstructed simultaneously and in parallel by dynamically beamforming the RF data received for each target location.

[0073] Ultrafast ultrasound methods result in imaging at thousands of frames per second, limited only by the physical propagation speed of sound waves within tissue, enabling ultra-high-sensitivity blood flow tracking, shear wave imaging, super-resolution imaging, and other applications. For example, achieving optimal spatial resolution while enabling artifact-free imaging of dynamic cardiac structures requires a careful balance between spatial sampling and volume update rate, which can only be achieved using ultrafast imaging techniques. Therefore, the 3D ultrasound image data received by the system of the present invention may be real-time 3D ultrasound data. For example, the data may be 3D image data covering all directions.

[0074] The system of the present invention may be operably coupled to a catheter-based ultrasound imaging device which is operably coupled to a console, and the catheter-based ultrasound imaging device is configured to transmit ultrasound pulses into intravascular and / or intracardiac tissue and to receive echoes of the ultrasound pulses.

[0075] Figures 1A and 1B are schematic illustrations of an exemplary system 100 for providing visualization and characterization of tissues and / or blood vessels within a patient 12, which may be used in combination with the system and methods of the present invention. The system 100 may include an imaging device 101 equipped with an imaging assembly 104, and a console 106 to which the imaging device 101 should be connected. The imaging device may also be an imaging catheter 102. Thus, the system and methods for event-driven sampling disclosed herein may use a 4D intracardiac echocardiography (ICE) system to capture an anatomical structure of interest. ICE is a catheter-based form of echocardiography that collects images from within the heart, rather than by collecting images of the heart by transmitting sound waves through the chest wall. Specific embodiments of the system and methods of the present invention may be used in combination with a four-dimensional (4D) catheter-based ultrasound imaging device.

[0076] Figure 2 is a perspective view of an imaging catheter 102, which may be coupled with the system of the present invention. The catheter 102 may include a catheter body 108, which includes proximal and distal portions. An imaging assembly 104 may be provided, for example, in the distal portion, defining the distal end of the imaging catheter. A handle 110 is operably associated with the catheter body 108, allowing an operator (i.e., a surgeon or other medical professional) to manipulate and advance the imaging assembly 104 and the catheter body 108 to a desired target site in the patient's blood vessel. The handle 110 may include user-operable inputs for controlling various features and functions of the imaging assembly 104. An interface member 112 may be provided in the distal portion of the catheter body 108. The interface member 112 generally provides a connection between the imaging catheter 102, including the imaging assembly 104 and the handle 110, and a console 106 for the transmission of signals between them. The connection may include, for example, at least one of wired and wireless connections.

[0077] In one embodiment of this system, the console may be configured to receive at least 3D circumferential image data from an ultrasound imaging device in real or near real time. Thus, the console may be configured, at least partially, to reconstruct multiple images in real or near real time based on user input and / or a predefined protocol.

[0078] The console may be operably coupled to the imaging device and may generally control the operation of the transducer probe, i.e., the transmission of sound waves from the probe. The console may generally include one or more processors (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both) and a storage device such as main memory, static memory, or a combination of both, which communicate with each other via a bus or equivalent. The memory according to embodiments of the present invention may include a machine-readable medium on which one or more sets of instructions (e.g., software) embodying one or more of the methodologies or functions described herein are stored. The software may also reside in the main memory and / or processor, all or at least partially, during its execution by the computer system, and the main memory and processor also constitute the machine-readable medium. The software may further be transmitted or received over a network and via a network interface device.

[0079] (System for event-driven ultrasound imaging) As described above, the system of the present invention provides event-driven ultrasonic sampling.

[0080] Aspect of the present invention includes a system for image sampling comprising a hardware processor coupled to a non-transient computer-readable memory containing instructions, the instructions being executable by the processor to cause the processor to receive data from an imaging device and to invoke an event-driven sampling algorithm.

[0081] Figure 3 illustrates one embodiment of the system 100 of the present invention. For example, the system 100 may include a controller 200 comprising a hardware processor 201 coupled to a non-transient computer-readable memory 202 containing instructions executable by the processor 201. The controller may actively communicate with a computing system 203 configured to communicate across a network. The controller may operate as software on a microprocessor and / or be embedded as part of a custom ASIC / chip.

[0082] The computing system 203 or computing device may include one or more processors and memory, as well as input / output mechanisms (i.e., a keyboard, knobs, scroll wheel, or equivalent), which an operator can use to operate the machine and / or device, including performing other tasks described herein, such as adjusting the transmission characteristics of the ultrasonic probe and / or device, saving images, and selecting specific areas of interest for subsequent reconstruction into 2D and / or 3D images.

[0083] During operation, the CPU and / or GPU may control the transmission and reception of current, subsequently resulting in the emission and reception of sound waves from the probe. The CPU and / or GPU may also analyze the electrical pulses generated by the probe in response to the reflected waves returning, and then convert this data into an image (i.e., an ultrasound image) which can then be viewed on a display, which may be an integrated monitor. Such an image may also be stored in memory and / or printed via a printer. As disclosed herein, the system may include a console 106 to further provide control over the imaging assembly, including control over the emission of ultrasound pulses therefrom (intensity, frequency, duration, etc.) and control over the movement of the ultrasound transducer unit. Control over the imaging assembly may be provided by a controller, processor, and / or computing system. Thus, the system may communicate with the imaging device 101 and receive 3D ultrasound image data from the imaging device 101.

[0084] The computing system 203 may include a computer program comprising an event-driven algorithm 204, such that a computer-readable memory 202 containing instructions executable by the processor 201 causes the processor to receive data from the imaging device 101 and invoke one or more event-driven algorithms 204. The event-driven algorithm 204 may be part of a computer program executable by the computing system 203 and communicating with the console of system 100. The system may also include one or more algorithms for dynamically reconstructing multiple images from 3D image data to provide 3D visualization of anatomical regions of interest and targeted tissue sites.

[0085] The event-driven algorithm causes the processor to analyze the received data, identify one or more characteristics associated with the received data, and detect the occurrence of one or more events. An event involves a change in one or more characteristics compared to a defined threshold for those characteristics. The event-driven algorithm also causes the processor to adjust the sampling rate of the imaging device based on the detected occurrence of one or more events.

[0086] The event-driven algorithm may be one or more of the following: differential algorithms, differential thresholding algorithms, moving thresholding algorithms, logarithmic algorithms, triggered burst algorithms, and homeomorphic orthogonal detection algorithms.

[0087] For example, with respect to differential algorithms, the output voltage may be encoded at a bit depth lower than full sampling (16 bits). The resulting signal may then be sampled whenever the output voltage changes.

[0088] For example, with respect to a differential thresholding algorithm, the waveform may be sampled with a change in output y(t) relative to the previous value y(t-1) by a parameter ±Δ.

[0089] For example, with respect to a moving threshold algorithm, the waveform may be sampled with an output voltage change relative to a previously stored threshold by a parameter ±Δ. The threshold may be updated and stored whenever sampling is triggered.

[0090] As an example, with respect to a logarithmic algorithm, the waveform may be sampled whenever the output voltage exceeds any number of logarithmically spaced fixed voltage levels.

[0091] In another embodiment, with respect to the triggered burst algorithm, the waveform may be sampled at a full sampling rate over a set time T at any point in time when the output voltage y(t) changes with respect to a previous value y(t-1) by a parameter ±Δ.

[0092] For example, with respect to a common-mode second-order detection algorithm, only the amplitude and phase of the carrier wave (extracted via IQ demodulation) may be sampled, the amplitude signal may be sampled according to a "moving threshold" algorithm, and the phase may be sampled simultaneously with the amplitude and encoded at a bitrate lower than 16 bits.

[0093] In particular, event-driven ultrasound sampling using the algorithms described herein may be implemented in silicon such that the raw data rate can be reduced from the analog front end of the system and during further processing. Thus, the systems and methods of the present invention offer advantages by reducing the data rate on the communication link (for example, when transferring data through a limited number of wires in a small cable assembly or catheter). The systems and methods of the present invention also offer advantages by reducing the total data size (for example, when the amount of data becomes exponentially large to permanently store the information).

[0094] In some embodiments, the defined threshold is determined, at least in part, based on the clinical relevance of the structure to be imaged and / or one or more characteristics of the received data. For example, a blood pool may not be clinically relevant. Therefore, the threshold may be defined to avoid sampling the blood pool at high data rates.

[0095] The received data may consist of a digitized voltage signal or an analog voltage signal. In some embodiments, the processor is configured to access the raw digitized voltage signal and to adjust one or more sampling parameters to minimize the data transmission rate and maximize the image quality of clinically relevant images.

[0096] As disclosed herein, the data characteristic may be any characteristic associated with the received data, and the defined threshold may be any threshold defined with respect to the characteristic. For example, in some embodiments, the characteristic may be a digitized voltage, and the defined threshold may be a digitized voltage encoded at a bit depth lower than the full sampling bit depth. Bit depth as used herein is the number of bits per pixel. In some embodiments, the defined threshold may be a change in output voltage by a defined parameter relative to a previous output voltage. For example, image sampling by an imaging device may be triggered at a full sampling rate over a set time for each occurrence of an event associated with a change in output voltage by a defined parameter relative to a previous output voltage. In some embodiments, the defined threshold may be a change in output voltage by a set parameter in a previously stored threshold, and therefore the threshold is updated and stored when image sampling by the imaging device is triggered. In some embodiments, the defined threshold is one or more logarithmically spaced voltage levels.

[0097] As described in the embodiments below, the defined threshold may be a change in the amplitude signal by a set parameter in a previously stored threshold amplitude signal, and so only the amplitude and phase of the carrier wave are sampled, the phase is sampled in parallel with the amplitude and encoded at a bitrate lower than 16 bits. The amplitude and phase of the carrier wave may be extracted via I / Q demodulation. As is known to those skilled in the art, I / Q demodulation mixes in-phase and quadrature-phase sine waves with the input signal to emphasize the signal content at that frequency and reduce all other content.

[0098] In some embodiments, the imaging device comprises a transducer having an array of individual imaging elements. For example, the transducer comprises a micro-electromechanical system (MEMS) based micromachining ultrasonic transducer configured as a two-dimensional (2D) array structure.

[0099] In the present invention, the transducer may be of any type for transmitting and receiving acoustic waves. For example, the transducer may include a one- or two-dimensional array of electronic transducer elements for transmitting and receiving acoustic waves. These arrays may include micro-electromechanical system (MEMS) based transducers such as capacitive micromachined ultrasonic transducers (CMUTs) and / or piezoelectric micromachined ultrasonic transducers (PMUTs).

[0100] CMUT devices offer superior bandwidth and acoustic impedance characteristics, making these transducers preferable to conventional piezoelectric transducers. Vibration of the CMUT film can be triggered by applying pressure (e.g., using ultrasound) or electrically induced. Often, electrical connections to the CMUT device, such as integrated circuits (ICs) like ASICs, facilitate both the transmission and reception modes of the device. In reception mode, changes in film position cause changes in electrical capacitance, which can be electronically detected during transmission mode; applying an electrical signal causes vibration of the film.

[0101] Piezoelectric micromachined ultrasonic transducers (PMUTs) are based on the flexural motion of a thin film coupled with a thin piezoelectric film such as PVDF. This is in contrast to bulk piezoelectric transducers, which use the thickness mode motion of a piezoelectric ceramic plate such as PZT or single-crystal PMN-PT. Compared to bulk piezoelectric ultrasonic transducers, PMUT devices offer advantages such as increased bandwidth, flexible geometry, natural acoustic impedance matched with water, reduced voltage requirements, and especially the mixing of different resonant frequencies and potentials for integration with supporting electronic circuits for miniaturization and high-frequency applications. Current PMUT devices do not require bias to achieve imaging sensitivity.

[0102] The transducer may be a micro-electromechanical system (MEMS) based capacitive micromachined ultrasonic transducer (CMUT) configured as a two-dimensional (2D) array structure. In non-limiting embodiments, the 2D array may be a flexible structure. The cylindrical imaging array may consist of a flexible 2D array structure in the CMUT design. Flexible MEMS-based arrays may be implemented by wafer thinning (CMUT / PMUT), as described, for example, in Mimoun, 2013, "A generic platform for the fabrication and assembly of flexible sensors for minimally invasive instruments," IEEE Sensors J 13(10) 3873-3882 (incorporated herein by reference), or by using specific approaches such as combining a rigid imaging cell with a flexible interconnect.

[0103] In addition, and / or alternatively, transducers may be fabricated from electrostrictive materials, configured as a two-dimensional (2D) array structure. Electrostriction is a property of all dielectric materials and consists of mechanical displacement as a response to an electronic field, such as material compression in a region of high electric field intensity. In electrostriction, the electric field applied to a material causes deformation of the material (direct effect), and the mechanical stress applied to the material changes the polarization of the material (inverse effect). Transducers may be fabricated from electrostrictive materials such as electrostrictive polymers, or any material that can be activated using a bias voltage to achieve imaging sensitivity.

[0104] In some embodiments, the imaging element is an acoustic sensor activated by a processor to transmit and / or receive multiple incident acoustic wave signals as wave data. The wave data may be, for example, at least one of plane wave data and divergent wave data associated with one or more plane wave transmission-reception cycles performed by the imaging element. The active element used for transmission and reception may comprise all elements in both transmission and reception, the same subset of elements used for transmission and reception, or different subsets (or complete sets) of elements used for transmission and reception of wave data. The wave data may be 3D image data covering all directions.

[0105] In some embodiments, the imaging device may be a catheter-based ultrasound imaging device configured to transmit ultrasound pulses into intravascular and / or intracardiac tissue and then receive echoes of the ultrasound pulses. For example, with respect to intracardiac imaging, plane wave imaging may refer to an ultrasound imaging modality in which a plane wavefront can traverse tissue and be scattered in part back to the transducer through the planar transmission of all transducer elements (at different angles) from an angular imaging aperture. From the received radio frequency (RF) (i.e., channel) data, the overall image can be reconstructed simultaneously and in parallel by dynamically beamforming the RF data received for each target location.

[0106] In some embodiments, the imaging device may be a minimally invasive implantable device. For example, the processor may be implanted as part of an application-specific integrated circuit. The system may be implanted as part of a custom ASIC / chip to control an event-driven sampling scheme so that acoustic waves deliver power and telemetry capabilities to the implanted device for remote sensing of the physiological environment within soft biological tissue, as an alternative to inductive (near-field) and radio frequency (RF) links. The processor may be part of an integrated component on a flexible membrane chip, the core component being a radio ultrasonic energy converter that couples ultrasonic mechanical vibrations with a triboelectric generator to convert mechanical energy into electrical energy. Thus, the event-driven scheme may include adjusting the sampling rate so that the overall power consumption and heat dissipation of the device are reduced.

[0107] In some embodiments, adjusting the sampling rate results in a reduction of the average sampling rate of the data received from the imaging device.

[0108] (Method for event-driven ultrasonic sampling) An aspect of the present invention is the provision of a method for image sampling.

[0109] Figure 4 is a block diagram illustrating a method for event-driven ultrasound according to one embodiment of the present invention.

[0110] Method 400 includes step 401 providing a hardware processor coupled to a non-transient computer-readable memory containing instructions, the instructions being executable by the processor to cause the processor to receive data from an imaging device (403), to invoke an event-driven sampling algorithm (405), the event-driven sampling algorithm causing the processor to analyze the received data (407), to identify one or more characteristics associated with the received data, to further detect the occurrence of one or more events, the one or more events comprising changes in one or more characteristics compared to defined thresholds relating to one or more characteristics, and to adjust the sampling rate of the imaging device based on the detected occurrence of one or more events (409).

[0111] As disclosed herein, the controller may include a hardware processor coupled to non-transient computer-readable memory containing instructions executable by the processor. The controller may actively communicate with a computing system configured to communicate across a network. The controller may operate as software on a microprocessor and / or be embedded as part of a custom ASIC / chip. The computing system or computing device may include one or more processors and memory, as well as input / output mechanisms (i.e., a keyboard, knobs, scroll wheel, or equivalent), which an operator may interact with to operate the machine, including performing other tasks described herein, such as adjusting the transmission characteristics of a probe, saving images, and selecting specific areas of interest for subsequent reconstruction into 2D and / or 3D images.

[0112] During operation, the CPU and / or GPU may control the transmission and reception of current, subsequently resulting in the emission and reception of sound waves from the probe. The CPU and / or GPU may also analyze the electrical pulses generated by the probe in response to the reflected waves returning, and then convert this data into an image (i.e., an ultrasound image) which can then be viewed on a display, which may be an integrated monitor. Such an image may also be stored in memory and / or printed via a printer. As disclosed herein, the system may include a console (not shown) to further provide control over the imaging assembly, including control over the emission of ultrasound pulses therefrom (intensity, frequency, duration, etc.) and control over the movement of the ultrasound transducer unit. Control over the imaging assembly may be provided by a controller, processor, and / or computing system. Thus, the system may communicate with an imaging device and receive 3D ultrasound image data from the imaging device.

[0113] The computing system may include a computer program comprising an event-driven algorithm, in which computer-readable memory containing instructions executable by the processor causes the processor to receive data from an imaging device and invoke one or more event-driven algorithms. The event-driven algorithm may be part of a computer program executable by the computing system and communicating with the system's console. The system may also include one or more algorithms for dynamically reconstructing multiple images from 3D image data to provide 3D visualization of anatomical regions of interest and targeted tissue sites.

[0114] In the method of the present invention, the event-driven algorithm causes the processor to analyze the received data, identify one or more characteristics associated with the received data, and further detect the occurrence of one or more events. One or more events include changes in one or more characteristics compared to defined thresholds for one or more characteristics. The event-driven algorithm also causes the processor to adjust the sampling rate of the imaging device based on the detected occurrence of one or more events.

[0115] As disclosed herein, the event-driven algorithm of the method may be one or more of the following: a differential algorithm, a differential thresholding algorithm, a moving thresholding algorithm, a logarithmic algorithm, a triggered burst algorithm, and a home-mode orthogonal detection algorithm.

[0116] For example, with respect to differential algorithms, the output voltage may be encoded at a bit depth lower than full sampling (16 bits). The resulting signal may then be sampled whenever the output voltage changes.

[0117] For example, with respect to a differential thresholding algorithm, the waveform may be sampled with a change in output y(t) relative to the previous value y(t-1) by a parameter ±Δ.

[0118] In another embodiment, with respect to a moving threshold algorithm, the waveform may be sampled with an output voltage change relative to a previously stored threshold by a parameter ±Δ. The threshold may be updated and stored whenever sampling is triggered.

[0119] For example, with respect to logarithmic algorithms, the waveform may be sampled whenever the output voltage exceeds any number of logarithmically spaced fixed voltage levels.

[0120] Furthermore, with respect to the triggered burst algorithm, the waveform may be sampled at a full sampling rate over a set time T at any point in time when the output voltage y(t) changes with respect to the previous value y(t-1) by a parameter ±Δ.

[0121] In another embodiment, with respect to the common-mode second-order detection algorithm, only the amplitude and phase of the carrier wave (extracted via IQ demodulation) may be sampled, the amplitude signal may be sampled according to a “moving threshold” algorithm, and the phase may be sampled simultaneously with the amplitude and encoded at a bitrate lower than 16 bits.

[0122] In particular, event-driven ultrasound sampling using the algorithms described herein may be implemented in silicon such that the raw data rate can be reduced from the analog front end of the system and during further processing. Thus, the systems and methods of the present invention offer advantages by reducing the data rate on the communication link (for example, when transferring data through a limited number of wires in a small cable assembly or catheter). The systems and methods of the present invention also offer advantages by reducing the total data size (for example, when the amount of data becomes exponentially large to permanently store the information).

[0123] In some embodiments of this method, the defined threshold is determined, at least in part, based on the clinical relevance of the structure to be imaged and / or one or more characteristics of the received data. For example, a blood pool may not be clinically relevant. Therefore, the threshold may be defined to avoid sampling the blood pool at high data rates.

[0124] The received data may consist of a digitized voltage signal or an analog voltage signal. In some embodiments, the processor is configured to access the raw digitized voltage signal and to adjust one or more sampling parameters to minimize the data transmission rate and maximize the image quality of clinically relevant images.

[0125] As disclosed herein, the data characteristic may be any characteristic associated with the received data, and the defined threshold may be any threshold defined with respect to the characteristic. For example, in some embodiments of the method, the characteristic may be a digitized voltage, and the defined threshold may be a digitized voltage encoded at a bit depth lower than the full sampling bit depth. Bit depth as used herein is the number of bits per pixel. In some embodiments, the defined threshold may be a change in output voltage by a defined parameter relative to a previous output voltage. For example, image sampling by an imaging device may be triggered at a full sampling rate over a set time for each occurrence of an event associated with a change in output voltage by a defined parameter relative to a previous output voltage. In some embodiments, the defined threshold may be a change in output voltage by a set parameter in a previously stored threshold, and therefore the threshold is updated and stored when image sampling by the imaging device is triggered. In some embodiments, the defined threshold is one or more logarithmically spaced voltage levels.

[0126] As described in the embodiments below, the defined threshold may be a change in the amplitude signal by a set parameter in a previously stored threshold amplitude signal, and so only the amplitude and phase of the carrier wave are sampled, the phase is sampled in parallel with the amplitude and encoded at a bitrate lower than 16 bits. The amplitude and phase of the carrier wave may be extracted via I / Q demodulation. As is known to those skilled in the art, I / Q demodulation mixes in-phase and quadrature-phase sine waves with the input signal to emphasize the signal content at that frequency and reduce all other content.

[0127] In some embodiments of this method, the imaging device comprises a transducer having an array of individual imaging elements. For example, the transducer may include a micro-electromechanical system (MEMS) based micromachining ultrasonic transducer configured as a two-dimensional (2D) array structure.

[0128] In the present invention, the transducer may be of any type for transmitting and receiving acoustic waves. For example, the transducer may include a one- or two-dimensional array of electronic transducer elements for transmitting and receiving acoustic waves. These arrays may include micro-electromechanical system (MEMS) based transducers such as capacitive micromachined ultrasonic transducers (CMUTs) and / or piezoelectric micromachined ultrasonic transducers (PMUTs).

[0129] CMUT devices offer superior bandwidth and acoustic impedance characteristics, making these transducers preferable to conventional piezoelectric transducers. Vibration of the CMUT film can be triggered by applying pressure (e.g., using ultrasound) or electrically induced. Often, electrical connections to the CMUT device, such as integrated circuits (ICs) like ASICs, facilitate both the transmission and reception modes of the device. In reception mode, changes in film position cause changes in electrical capacitance, which can be electronically detected during transmission mode; applying an electrical signal causes vibration of the film.

[0130] Piezoelectric micromachined ultrasonic transducers (PMUTs) are based on the flexural motion of a thin film coupled with a thin piezoelectric film such as PVDF. This is in contrast to bulk piezoelectric transducers, which use the thickness mode motion of a piezoelectric ceramic plate such as PZT or single-crystal PMN-PT. Compared to bulk piezoelectric ultrasonic transducers, PMUT devices offer advantages such as increased bandwidth, flexible geometry, natural acoustic impedance matched with water, reduced voltage requirements, and especially the mixing of different resonant frequencies and potentials for integration with supporting electronic circuits for miniaturization and high-frequency applications. Current PMUT devices do not require bias to achieve imaging sensitivity.

[0131] The transducer may be a micro-electromechanical system (MEMS) based capacitive micromachined ultrasonic transducer (CMUT) configured as a two-dimensional (2D) array structure. In non-limiting embodiments, the 2D array may be a flexible structure. The cylindrical imaging array may consist of a flexible 2D array structure in the CMUT design. Flexible MEMS-based arrays may be implemented by wafer thinning (CMUT / PMUT), as described, for example, in Mimoun, 2013, "A generic platform for the fabrication and assembly of flexible sensors for minimally invasive instruments," IEEE Sensors J 13(10) 3873-3882 (incorporated herein by reference), or by using specific approaches such as combining a rigid imaging cell with a flexible interconnect.

[0132] In addition, and / or alternatively, transducers may be fabricated from electrostrictive materials, configured as a two-dimensional (2D) array structure. Electrostriction is a property of all dielectric materials and consists of mechanical displacement as a response to an electronic field, such as material compression in a region of high electric field intensity. In electrostriction, the electric field applied to a material causes deformation of the material (direct effect), and the mechanical stress applied to the material changes the polarization of the material (inverse effect). Transducers may be fabricated from electrostrictive materials such as electrostrictive polymers, or any material that can be activated using a bias voltage to achieve imaging sensitivity.

[0133] In some embodiments of this method, the imaging element is an acoustic sensor activated by a processor to transmit and / or receive multiple incident acoustic wave signals as wave data. The wave data may be, for example, at least one of plane wave data and divergent wave data associated with one or more plane wave transmission-reception cycles performed by the imaging element. The active element used for transmission and reception may comprise all elements for both transmission and reception, the same subset of elements used for transmission and reception, or different subsets (or complete sets) of elements used for transmission and reception of wave data. The wave data may be 3D image data covering all directions.

[0134] In some embodiments of this method, the imaging device may be a catheter-based ultrasound imaging device configured to transmit ultrasound pulses into intravascular and / or intracardiac tissue and then receive echoes of the ultrasound pulses. For example, with respect to intracardiac imaging, plane wave imaging may refer to an ultrasound imaging modality in which a plane wavefront can traverse tissue and be scattered in part back to the transducer through the planar transmission of all transducer elements (at different angles) from an angular imaging aperture. From the received radio frequency (RF) (i.e., channel) data, the overall image can be reconstructed simultaneously and in parallel by dynamically beamforming the RF data received for each target location.

[0135] In some embodiments of this method, the imaging device may be a minimally invasive implantable device. For example, the processor may be implanted as part of an application-specific integrated circuit. The system may be implanted as part of a custom ASIC / chip to control an event-driven sampling scheme so that acoustic waves deliver power and telemetry capabilities to the implanted device for remote sensing of the physiological environment within soft biological tissue, as an alternative to induction (near-field) and radio frequency (RF) links. The controller may be part of an integrated component on a flexible membrane chip, the core component being a radio ultrasonic energy converter that couples ultrasonic mechanical vibrations with a triboelectric generator to convert mechanical energy into electrical energy. Thus, the event-driven scheme may include adjusting the sampling rate so that the overall power consumption and heat dissipation of the device are reduced.

[0136] In some embodiments of this method, adjusting the sampling rate results in a reduction of the average sampling rate of the data received from the imaging device.

[0137] As used in any embodiment of this specification, the term “module” may mean software, firmware, and / or a circuit configured to perform any of the operations described herein. Software may be embodied as software packages, code, instructions, instruction sets, and / or data recorded on a non-transient computer-readable storage medium. Firmware may be embodied as code, instructions, or instruction sets, and / or data hardcoded (e.g., non-volatile) within a memory device. “Circuitry” may, as used in any embodiment of this specification, include, for example, a wired network, a programmable network such as a computer processor comprising one or more individual instruction processing cores, a state-machine network, and / or firmware that stores instructions executed by a programmable network, either alone or in any combination. Modules may be embodied as a network that forms part of a larger system, collectively or individually, such as an integrated circuit (IC), a system-on-a-chip (SoC), a desktop computer, a laptop computer, a tablet computer, a server, a smartphone, and the like.

[0138] Any of the operations described herein may be implemented in a system including one or more storage media having instructions for performing the method, which are executed individually or in combination by one or more processors stored thereon. Here, the processors may include, for example, a server CPU, a mobile device CPU, and / or other programmable circuits.

[0139] Furthermore, it is intended that the operations described herein may be distributed across multiple physical devices, such as processing structures, in more than one different physical locations. The storage medium may include any type of tangible medium, such as any type of disk, including hard disks, floppy disks, optical disks, compact disk read-only memory (CD-ROM), rewritable compact disks (CD-RW), and magneto-optical disks; semiconductor devices such as read-only memory (ROM), random access memory (RAM) such as dynamic and static RAM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, solid-state disks (SSDs); magnetic or optical cards; or any type of medium suitable for storing electronic instructions. Other embodiments may be implemented as software modules executed by a programmable control device. The storage medium may be non-transient.

[0140] As described herein, various embodiments may be implemented using hardware elements, software elements, or any combination thereof. Examples of hardware elements may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, etc.), integrated circuits, application-specific integrated circuits (ASICs), programmable logic devices (PLDs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), logic gates, registers, semiconductor devices, chips, microchips, chipsets, and the like.

[0141] Throughout this specification, any reference to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in relation to an embodiment is included in at least one embodiment. Therefore, the expressions “in one embodiment” or “in an embodiment” in various places throughout this specification do not necessarily all refer to the same embodiment. Furthermore, certain features, structures, or characteristics may be combined in any preferred manner in one or more embodiments.

[0142] The term "non-transitory" is understood to exclude only the transient signals themselves from the scope of the claims, and not to waive rights to all standard computer-readable media other than the transient signals themselves. In other words, the terms "non-transitory computer-readable medium" and "non-transitory computer-readable storage medium" should be interpreted as excluding only those types of transient computer-readable media that were found to fall outside the scope of patentable subject matter under Section 101 of the U.S. Patent Act in the In Re Nuijten case.

[0143] The terms and expressions used herein are for illustrative purposes only, not limitation, and in using such terms and expressions, there is no intention to exclude any equivalents of the features (or parts thereof) shown and described, and it should be recognized that various modifications are possible within the scope of the claims. Therefore, the claims are intended to encompass all such equivalents.

[0144] (Examples) The development of the event-driven sampling architecture was tested using the protocol disclosed herein and the algorithm described below. • DIFFERENTIAL: Reduces the bit depth of the signal according to bit_level and samples only when the signal changes. The output bit level is calculated as 16bit_level, and therefore a higher bit_level corresponds to a reduced bit depth of the sampled signal. • DIFFERENCE_THRESHOLD: Samples are performed only when the signal changes by Δ relative to the most recently measured value. • MOVING_THRESHOLD: Samples only when the signal changes by Δ amount relative to the threshold that is updated when sampling is triggered. • TRIGGERED_BURST: A hybrid of normal and event-driven sampling, where a finite number of samples (parameter burst_length) are collected as normal when the signal changes by a set Δ amount. • QUADRATURE_PHASE: The amplitude and phase of the modulated carrier wave at frequency f_c are extracted, and the amplitude is sampled using the following: ○ Amplitude A(t) and phase φ(t) are calculated using the in-phase and orthogonal components of the channel data, and channel data for a single channel and event are:

number

number

number

number

[0145] (Algorithm training and testing) The OPSF was corrected and beamformed as usual. The quality of the resulting beamformed image (orbital slice or composite volume) was compared to the uncorrected image using mean squared error (MSE). Other similarity metrics included structural similarity (SSIM) and peak signal-to-noise ratio (PSNR).

[0146] (Synthetic data) The generated data was corrected, and the resulting beamformed image was compared. (OPSF analyzed) 〇 Shared data\SimulationStudies\Event-Driven_Analysis\sp2sp\sp2sp\train.opsf Snapshots: 00 and 01 ○ Frame: All (00000-00004 for both snapshots)

[0147] (Preclinical data) Several representative datasets identified from preclinical stages in the July 2022 IMMR (OPSF analyzed) 2022-07-28__IMMR\002_VeraStudy_c56ac7a0-c940-4ba3-aaef-d13e386d0a0c 〇 Snapshot 01: RA Home View Anatomical Rotation Imaging 〇 Frames: 0001-00141, 10 frames each 2022-07-28__IMMR\004_VeraStudy_5390e68c-4a62-4600-8b35-953c915c5877.opsf 〇 Snapshot 01: Pullback from SVC to IVC Frames: 0001-00401, each containing 50 frames.

[0148] (Reduced sampling rate) The results from the QUADRATURE_PHASE algorithm suggested sampling the signal at a lower rate because the envelope fluctuates at frequencies much lower than the carrier. Sampling below the Nyquist rate (exemplary carrier frequency is 6.25 MHz, sampling rate is 32 MHz), and therefore, it is likely that the sampling rate can be reduced without significant loss of quality.

[0149] (result) (Synthetic data) Figures 5A and 5B show illustrative results comparing the original image with an image created using the differential algorithm. Figure 5A illustrates the original PNG file at 50%, 1,580×516 pixels, 8 bits, and 796K. Figure 5B illustrates the image from the differential algorithm differential_bit_level_5 at 50%, 1,580×516 pixels, 8 bits, and 796K.

[0150] (Overview of Algorithm Performance - Synthetic Data) Figure 6 is a graph illustrating the average mean squared error for the differential algorithm, differential thresholding algorithm, moving thresholding algorithm, and triggered burst algorithm (using burst lengths of 5 and 25) for each bit level / log2(Δ).

[0151] Figure 7 is a graph illustrating the average transmission rate (%) for differential algorithms, differential threshold algorithms, moving threshold algorithms, and triggered burst algorithms (using burst lengths of 5 and 25) for each bit level / log2(Δ).

[0152] (Overview of Algorithm Performance - Anatomical Imaging in RA) Figure 8 is a graph illustrating the average mean squared error (preclinical data (2022-07-28_IMMR)) for the differential algorithm, differential threshold algorithm, moving threshold algorithm, and triggered burst algorithm (using burst lengths of 5 and 45) for each bit level / log2(Δ).

[0153] Figure 9 is a graph illustrating the average transmission rate (%) for each bit level / log2(Δ) for differential algorithms, differential threshold algorithms, moving threshold algorithms, and triggered burst algorithms (using burst lengths of 5 and 45) (preclinical data (2022-07-28_IMMR)).

[0154] (Overview of Algorithm Performance - Pullback Sequence) Figure 10 is a graph illustrating the average mean squared error for each bit level / log2(Δ) for the differential algorithm, logarithmic algorithm, differential threshold algorithm, moving threshold algorithm, triggered burst algorithm (burst length 5), triggered burst algorithm (burst length 45), and quadratic phase algorithm (bit level 3).

[0155] Figure 11 is a graph illustrating the average transmission rate (%) for each bit level / log2(Δ) for differential algorithms, logarithmic algorithms, differential threshold algorithms, moving threshold algorithms, triggered burst algorithms (burst length 5), triggered burst algorithms (burst length 45), and quadratic phase algorithms (bit level 3).

[0156] (Data transmission histogram) The histogram for each algorithm represents the six fixed parameter levels for pullback sequences using preclinical data.

[0157] Figure 12 illustrates the data transmission histogram for the DIFFERENTIAL algorithm with Δ / bit level 6.

[0158] Figure 13 illustrates the data transmission histogram for the DIFFERENCE_THRESHOLD algorithm with Δ / bit level 6.

[0159] Figure 14 illustrates the data transmission histogram for the MOVING_THRESHOLD algorithm with Δ / bit level 6.

[0160] Figure 15 illustrates the data transmission histogram for the LOGARITHMIC_bit_level_6 algorithm with Δ / bit level 6.

[0161] (Reduced sampling rate) The DIFFERENTIAL, MOVING_THRESHOLD, and LOGARITHMIC algorithms were evaluated. Image quality appeared poor with the QUADRATURE_PHASE algorithm, due to high-pass filtering of the second harmonic.

[0162] Figures 16A and 16B illustrate the average mean squared error and average transmission rate (%) for the bit level / log2(Δ) using a sampling rate of 0.6 for differential, logarithmic, and moving threshold algorithms.

[0163] Figures 17A and 17B illustrate the average mean squared error and average transmission rate (%) for the bit level / log2(Δ) using a sampling rate of 1.0 for differential, logarithmic, and moving threshold algorithms.

[0164] The results show that LOGARITHMIC works surprisingly well.

[0165] (Analysis of event-driven sampling algorithms) ○ Best image quality: DIFFERENTIAL algorithm, second to last performance in terms of data rate reduction. 〇 Minimum sampling rate: QUADRATURE_PHASE algorithm, however, the image quality is very poor. 〇 Combination: MOVING_THRESHOLD algorithm: 3rd best data rate reduction, 2nd best image quality. ○ Combined with a reduced sampling rate: LOGARITHMIC algorithm, lowest transmission rate (average <25%) at 60% of the original sampling frequency, same quality as DIFFERENTIAL. These results may vary depending on the imaging mode. 〇 Usage may depend on the method that is easiest to implement in hardware.

[0166] (Comparison with microbeam formation) The algorithm emulation results indicate findings regarding other concepts for reducing microbeam formation and data rate. Microbeam formation using two channels (a uniform 50% data rate reduction) achieves 60–100 MSEs for three different datasets (single frames), which has the advantage of a deterministic data rate rather than the variability seen with these event-driven sampling architectures. Microbeam formation achieved the previously analyzed metrics for IMMR pullback on 2022-07-28: transmission rate of 50%, MSE of 157, PSNR of 26 dB, and NRMSE of 0.126.

[0167] (Plot of average transmission across all events) The following plots show the average transmission rate (error bars indicate minimum and maximum transmission), mean squared error, PSNR, and NRMSE across all events for three of the most promising event-driven algorithms. Since this performance will likely be inferior to microbeamforming, the plots are capped at 200 for MSE to indicate when this threshold is exceeded. Data related to 2022-09-08 012_VeraStudy snapshot 14 frames 30 (preclinical) Microbeam formation (n=2): 50% data transmission, MSE of 65.9 / PSNR of 30dB / NRMSE of 0.226

[0168] Figure 18 includes graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for DIFFERENTIAL_f_sample_1000e-3.

[0169] Figure 19 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for DIFFERENTIAL_f_sample_800e-3.

[0170] Figure 20 includes graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for DIFFERENTIAL_f_sample_600e-3.

[0171] Figure 21 includes graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for LOGARITHMIC_f_sample_1000e-3.

[0172] Figure 22 includes graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for LOGARITHMIC_f_sample_800e-3.

[0173] Figure 23 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for LOGARITHMIC_f_sample_600e-3.

[0174] Figure 24 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against Δ(bit_level) for MOVING_THRESHOLD_f_sample_1000e-3.

[0175] Figure 25 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against Δ(bit_level) for MOVING_THRESHOLD_f_sample_800e-3.

[0176] Figure 26 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against Δ(bit_level) for MOVING_THRESHOLD_f_sample_600e-3.

[0177] (Plot of average transmission across all events) The following plots show the average transmission rate (error bars indicate minimum and maximum transmission), mean squared error, PSNR, and NRMSE across all events for three of the most promising event-driven algorithms. Since this performance will likely be inferior to microbeamforming, the plots are capped at 200 for MSE to indicate when this threshold is exceeded. Data related to 2022-10-18 000_Verastudy snapshot 00 frame 25 (CIRS Phantom) Microbeam formation (n=2): 50% data transmission, 113 MSE / 28dB PSNR / 0.0186 NRMSE

[0178] Figure 27 includes graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for DIFFERENTIAL_f_sample_1000e-3.

[0179] Figure 28 includes graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for DIFFERENTIAL_f_sample_800e-3.

[0180] Figure 29 includes graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for DIFFERENTIAL_f_sample_600e-3.

[0181] Figure 30 includes graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for LOGARITHMIC_f_sample_1000e-3.

[0182] Figure 31 includes graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for LOGARITHMIC_f_sample_800e-3.

[0183] Figure 32 includes graphs of the average data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against the bit level for LOGARITHMIC_f_sample_600e-3.

[0184] Figure 33 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against Δ(bit_level) for MOVING_THRESHOLD_f_sample_1000e-3.

[0185] Figure 34 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against Δ(bit_level) for MOVING_THRESHOLD_f_sample_800e-3.

[0186] Figure 35 includes graphs of the mean data transmission rate (%), mean squared error, peak signal-to-noise ratio, and normalized mean squared error against Δ(bit_level) for MOVING_THRESHOLD_f_sample_600e-3.

[0187] The results suggest that the moving threshold algorithm yields the best combination of reduced data rate (a data rate averaged across all frames <30%) and image quality (measured in terms of the mean square error of the beamformed image relative to full sampling).

[0188] (Integrated by reference) References and citations to other documents, such as patents, patent applications, patent publications, journals, books, articles, and web content, are made throughout this disclosure. All such documents are incorporated herein by reference to this specification for any purpose.

[0189] (Equal portions) Various modifications of the present invention and many further embodiments thereof will be apparent to those skilled in the art from the entirety of this document, including, in addition to those shown and described herein, references to scientific and patent documents cited herein. The subject matter of this specification contains important information, examples, and guidance that may be adapted to the practice of the invention in its various embodiments and equivalents thereof.

Claims

1. A system for image sampling, comprising a hardware processor coupled to a non-transient computer-readable memory containing instructions, wherein the instructions are executable by the processor to cause the processor to receive data from an imaging device and to activate an event-driven sampling algorithm. The event-driven sampling algorithm is provided to the processor, The process involves analyzing the received data, identifying one or more characteristics associated with the received data, and detecting the occurrence of one or more events, wherein the one or more events include a change in the one or more characteristics compared to a defined threshold related to the one or more characteristics. Based on the detection of one or more of the aforementioned events, the sampling rate of the imaging device is adjusted. A system that enables this to happen.

2. The system according to claim 1, wherein the defined threshold is determined at least in part based on the clinical relevance of the structure to be imaged and / or one or more characteristics of the received data.

3. The system according to claim 1, wherein the received data comprises an analog voltage signal.

4. The system according to claim 1, wherein the received data comprises a digitized voltage signal.

5. The aforementioned processor, Accessing raw, digitized voltage signals, To minimize data transmission rates and maximize image quality for clinically relevant images, adjust one or more sampling parameters. The system according to claim 4, configured to perform the following:

6. The system according to claim 4, wherein the defined threshold is a digitized voltage encoded at a bit depth lower than the full sampling bit depth.

7. The system according to claim 4, wherein the defined threshold is a change in the output voltage by a defined parameter relative to the previous output voltage.

8. The system according to claim 7, wherein image sampling by the imaging device is triggered at a full sampling rate over a set time for each occurrence of an event associated with a change in the output voltage by a defined parameter relative to the previous output voltage, with respect to the previous output voltage.

9. The system according to claim 4, wherein the defined threshold is a change in output voltage by a set parameter in a previously stored threshold, and the threshold is updated and stored when image sampling by the imaging device is triggered.

10. The system according to claim 4, wherein the defined threshold is one or more logarithmically separated voltage levels.

11. The system according to claim 1, wherein the defined threshold is a change in the amplitude signal by a set parameter in a previously stored threshold amplitude signal, and only the amplitude and phase of the carrier wave are sampled, the phase is sampled in parallel with the amplitude and encoded at a bit rate lower than 16 bits.

12. The system according to claim 11, wherein the amplitude and phase of the carrier wave are extracted via I / Q demodulation.

13. The system according to claim 1, wherein the imaging device comprises a transducer having an array of individual imaging elements.

14. The system according to claim 13, wherein the transducer comprises a micro-electromechanical system (MEMS) based micromachining ultrasonic transducer configured as a two-dimensional (2D) array structure.

15. The system according to claim 13, wherein the imaging element is an acoustic sensor activated by the processor for transmitting and / or receiving a plurality of incident acoustic wave signals as wave data.

16. The system according to claim 15, wherein the wave data comprises at least one of plane wave data and divergent wave data associated with one or more wave transmission-reception cycles performed by the imaging element.

17. The system according to claim 16, wherein the wave data is 3D image data covering the entire circumference.

18. The system according to claim 17, comprising a catheter-based ultrasound imaging device configured to transmit ultrasound pulses into intravascular and / or intracardiac tissue and to receive echoes of the ultrasound pulses from intravascular and / or intracardiac tissue.

19. The system according to claim 13, wherein the imaging device is a minimally invasive implantable device.

20. The system according to claim 19, wherein the adjustment of the sampling rate reduces the overall power consumption and heat dissipation of the device.

21. The system according to claim 1, wherein the adjustment of the sampling rate results in a reduction of the average sampling rate of the data received from the imaging device.

22. The system according to claim 1, wherein the processor is embedded as part of an application-specific integrated circuit (ASIC).

23. A method for image sampling, wherein the method is The invention includes providing a hardware processor coupled to a non-transient computer-readable memory containing instructions, wherein the instructions are executable by the processor to cause the processor to receive data from an imaging device and to invoke an event-driven sampling algorithm. The event-driven sampling algorithm is provided to the processor, The process involves analyzing the received data, identifying one or more characteristics associated with the received data, and detecting the occurrence of one or more events, wherein the one or more events include a change in the one or more characteristics compared to a defined threshold related to the one or more characteristics. Based on the detection of one or more of the aforementioned events, the sampling rate of the imaging device is adjusted. A method for making someone do something.

24. The method according to claim 23, wherein the defined threshold is determined at least in part based on the clinical relevance of the structure to be imaged and / or one or more characteristics of the received data.

25. The method according to claim 23, wherein the received data comprises an analog voltage signal.

26. The method according to claim 23, wherein the received data comprises a digitized voltage signal.

27. The aforementioned processor, Accessing raw, digitized voltage signals, To minimize data transmission rates and maximize image quality for clinically relevant images, adjust one or more sampling parameters. The method according to claim 26, configured to perform the following:

28. The method according to claim 26, wherein the defined threshold is a digitized voltage encoded at a bit depth lower than the full sampling bit depth.

29. The method according to claim 26, wherein the defined threshold is a change in output voltage by a defined parameter relative to the previous output voltage.

30. The method according to claim 29, wherein the image sampling by the imaging device is triggered at a full sampling rate over a set time for each occurrence of an event associated with a change in the output voltage by a defined parameter relative to the previous output voltage, with respect to the previous output voltage.

31. The method according to claim 26, wherein the defined threshold is a change in output voltage by a set parameter in a previously stored threshold, and the threshold is updated and stored when image sampling by the imaging device is triggered.

32. The method according to claim 26, wherein the defined threshold is one or more logarithmically separated voltage levels.

33. The method according to claim 23, wherein the defined threshold is a change in the amplitude signal by a set parameter in a previously stored threshold amplitude signal, and only the amplitude and phase of the carrier wave are sampled, the phase is sampled in parallel with the amplitude and encoded at a bit rate lower than 16 bits.

34. The method according to claim 33, wherein the amplitude and phase of the carrier wave are extracted via I / Q demodulation.

35. The method according to claim 23, wherein the imaging device comprises a transducer having an array of individual imaging elements.

36. The method according to claim 35, wherein the transducer comprises a micro-electromechanical system (MEMS) based micromachining ultrasonic transducer configured as a two-dimensional (2D) array structure.

37. The method according to claim 35, wherein the imaging element is an acoustic sensor activated by the processor for transmitting and / or receiving a plurality of incident acoustic wave signals as wave data.

38. The method according to claim 37, wherein the wave data comprises at least one of plane wave data and divergent wave data associated with one or more wave transmission-reception cycles performed by the imaging element.

39. The method according to claim 38, wherein the wave data is full-circumference three-dimensional (3D) image data.

40. The method according to claim 39, further comprising a catheter-based ultrasound imaging device configured to transmit ultrasound pulses into intravascular and / or intracardiac tissue and to receive echoes of the ultrasound pulses from intravascular and / or intracardiac tissue.

41. The method according to claim 35, wherein the imaging device is a minimally invasive implantable device.

42. The method according to claim 41, wherein the adjustment of the sampling rate reduces the overall power consumption and heat dissipation of the device.

43. The method according to claim 23, wherein the adjustment of the sampling rate results in a reduction of the average sampling rate of the data received from the imaging device.

44. The method according to claim 23, wherein the processor is embedded as part of an application-specific integrated circuit (ASIC).

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