A functional ultrasound-based skin microcirculation imaging system and method

Abstract

The application discloses a skin microcirculation imaging system and method based on functional ultrasound, and belongs to the technical field of medical monitoring. The system comprises an inertial measurement unit, a state decision and power gating module, and an ultrasound imaging link. The inertial measurement unit continuously monitors the motion state of the system; the state decision and power gating module judges whether the system enters a continuous stationary state according to a dynamic motion intensity sequence, and accordingly wakes up or puts the ultrasound imaging link to sleep; the ultrasound imaging link collects original ultrasound echo data after being woken up. During data collection, if it is monitored that the dynamic motion intensity exceeds a preset fuse threshold, the state decision and power gating module immediately suspends data collection and puts the ultrasound imaging link to sleep. The application solves the contradiction between the power consumption of a wearable device and the effectiveness of data, and realizes high-robustness, high-diagnostic-value microcirculation monitoring under super-long endurance.

Description

Technical Field

[0001] This application relates to the field of medical electronics and signal processing technology, and in particular to a skin microcirculation imaging system and method based on functional ultrasound. Background Technology

[0002] For chronic wounds such as diabetic foot ulcers, their healing is closely related to the local microcirculation status. Functional ultrasound, especially ultramicroscopic vascular imaging (UMI), can non-invasively provide high-resolution imaging of the microvascular network and blood flow in the dermis. Applying this technology to wearable patch devices holds promise for long-term, continuous wound monitoring. However, current solutions that directly miniaturize UMI technology generally employ timed or continuous acquisition modes, leading to an irreconcilable technological bias and fundamental contradiction: to ensure continuous monitoring, the device needs to operate for extended periods, but the singular value decomposition (SVD) clutter filtering step at the core of the UMI algorithm is computationally intensive, quickly depleting the limited battery power of wearable devices; simultaneously, users' daily activities generate significant motion artifacts, and timed acquisition easily captures contaminated and invalid data during movement, wasting valuable energy and causing data loss and distortion. This inherent conflict between power consumption and data validity constitutes a theoretical ceiling for current technological development, severely restricting the realization of high-diagnostic-value wearable microcirculation monitoring devices. Summary of the Invention

[0003] To address the aforementioned technical problems, the first aspect of this application provides a skin microcirculation imaging system based on functional ultrasound, comprising: An inertial measurement and motion sensing module is configured to monitor the motion state of the system in real time and output raw motion data; A state decision and power gating module is configured to receive the raw motion data, generate a dynamic motion intensity sequence in real time, compare the dynamic motion intensity sequence with a preset static threshold to determine whether the system has entered a continuous static state, output a wake-up signal when the system enters a continuous static state, and output a stop signal if the dynamic motion intensity exceeds a preset fuse threshold during subsequent data acquisition. An ultrasound imaging link includes an ultrasound transmitting and receiving array module and an ultrasound microvascular imaging signal processing and imaging module. The ultrasound imaging link is configured to be activated in response to the wake-up signal to acquire raw ultrasound echo data, and to enter a dormant state in response to the stop signal. The state decision and power gating module is also configured to control the power supply to the ultrasound imaging link.

[0004] Optionally, the state decision and power gating module is further configured to generate the dynamic motion intensity sequence in real time by performing the following operations: Acquire the raw triaxial acceleration data and raw triaxial angular velocity data output by the inertial measurement and motion sensing module; The vector magnitude of the raw triaxial acceleration data is calculated, and the standard gravitational acceleration component is removed from the vector magnitude to generate a dynamic acceleration intensity sequence; and The vector magnitude of the raw triaxial angular velocity data is calculated to generate a resultant angular velocity sequence; The dynamic motion intensity sequence includes the dynamic acceleration intensity sequence and the resultant angular velocity sequence.

[0005] Optionally, the state decision and power gating module is further configured to determine whether the system has entered a continuous static state by performing the following operations: At each sampling moment, the real-time generated dynamic acceleration intensity is compared with a preset acceleration rest threshold, and the real-time generated resultant angular velocity is compared with a preset angular velocity rest threshold. When the dynamic acceleration intensity is less than the acceleration rest threshold and the resultant angular velocity is less than the angular velocity rest threshold, the system is determined to be in an instantaneous rest state at that sampling moment; and When the duration of the instantaneous stillness exceeds a preset stillness duration threshold, the system is determined to enter the continuous stillness state.

[0006] Optionally, the state decision and power gating module responds to the wake-up signal by turning on the main operating power supply to the ultrasound imaging link.

[0007] Optionally, the state decision and power gating module responds to the abort signal by cutting off the main operating power supply to the ultrasound imaging link and clearing the original ultrasound echo data that was acquired and temporarily stored by the ultrasound imaging link before the abort.

[0008] Optionally, the ultrasound microvascular imaging signal processing and imaging module is further configured as follows: If the acquisition of the original ultrasound echo data is successfully completed without triggering an interruption, an ultra-microvascular imaging algorithm is executed based on the original ultrasound echo data to generate a two-dimensional blood flow perfusion matrix; and After generating the two-dimensional blood perfusion matrix, the state decision and power gating module is notified to put the ultrasound imaging link into the sleep state.

[0009] Optionally, the ultrasound microvascular imaging signal processing and imaging module is further configured to perform the ultramicrovascular imaging algorithm by performing the following operations: The original ultrasonic echo data is constructed into an ultrasonic spatiotemporal data tensor; Singular value decomposition spatiotemporal filtering is performed on the ultrasound spatiotemporal data tensor, the filtering being achieved by setting the first few singular value components corresponding to tissue clutter to zero; and Based on the filtered ultrasound spatiotemporal data tensor, the Doppler frequency shift power spectrum is calculated and integrated to generate the two-dimensional blood perfusion matrix.

[0010] Optionally, the ultrasound microvascular imaging signal processing and imaging module is further configured as follows: Based on the two-dimensional blood perfusion matrix, a global adaptive binarization algorithm is used to classify the pixels in the two-dimensional blood perfusion matrix into effective microvessel pixels and background pixels; and A microvessel density parameter is generated by quantifying the number of effective microvessel pixels and the total number of pixels in a preset region of interest.

[0011] Optionally, the fuse threshold is configured as an acceleration fuse threshold, and the value of the acceleration fuse threshold is greater than the value of the acceleration rest threshold.

[0012] Secondly, this application provides a skin microcirculation imaging method based on functional ultrasound, comprising: The motion state of an imaging device is continuously monitored. The motion state is characterized by raw motion data acquired by the inertial measurement unit on the imaging device, and a dynamic motion intensity sequence is generated in real time based on the raw motion data. The determination of whether the imaging device has entered a continuous static state is based on a comparison of the dynamic motion intensity sequence with a preset static threshold. In response to a determination that the imaging device has entered the sustained static state, an ultrasound imaging link is activated, and the ultrasound imaging link is controlled to acquire raw ultrasound echo data; and If the dynamic motion intensity exceeds a preset fusing threshold during the acquisition of the raw ultrasound echo data, the acquisition of the raw ultrasound echo data will be immediately stopped, and the ultrasound imaging link will enter a dormant state.

[0013] The beneficial effects of this application are as follows: By keeping the system in microampere-level sentinel mode with only the inertial measurement unit operating for most of the time, and only briefly waking up the high-power imaging link during the identified stationary window, the total power consumption of the system can be reduced by more than an order of magnitude compared to the traditional timed or continuous working mode, making it possible to have an ultra-long battery life of more than 72 hours.

[0014] This system ensures that each energy consumption corresponds to a high-quality image under optimal conditions (i.e., without motion artifacts), eliminating the contamination of microvascular imaging by motion artifacts at the source. Combined with a real-time circuit breaker mechanism, it proactively avoids the possibility of invalid data acquisition during user movement, resulting in an effective data rate of nearly 100%, guaranteeing the integrity and diagnostic value of the monitoring data.

[0015] Because power consumption per imaging session is strictly controlled, the system is able to perform burst acquisitions at a higher frequency within each static window. This unlocks high temporal resolution monitoring capabilities that are impossible with traditional power-constrained solutions, enabling the capture of dynamic changes in microvessel density over short periods of time and providing a new data dimension for disease process assessment. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of a method performed by a skin microcirculation imaging system based on functional ultrasound, as disclosed in an embodiment of this application.

[0018] Figure 2 This is a structural block diagram of a skin microcirculation imaging system based on functional ultrasound, disclosed in one embodiment of this application.

[0019] Figure 3 This is a schematic diagram comparing the baseline scheme disclosed in one embodiment of this application with the core mechanism of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0022] This embodiment provides a skin microcirculation imaging system based on functional ultrasound. In a specific implementation, the system employs an ultra-low-power inertial measurement unit as a motion sentinel to continuously monitor the motion state of the imaging system. Based on an algorithm that includes dynamic degravity processing, dual-threshold static decision, and real-time circuit breaker protection, it identifies a suitable static window for ultra-microvascular imaging and uses this window as the sole event trigger to wake up and drive the high-power ultrasound imaging link for data acquisition. This allows for robust avoidance of motion artifacts caused by daily activities while meeting the stringent power consumption constraints of long-term wearable devices, achieving high-quality imaging and functional parameter quantification of the dermal microvascular network. This system solves the inherent technical contradiction between power consumption and data validity in existing wearable ultrasound monitoring devices that operate in timed or continuous modes. Specifically, high power consumption leads to insufficient battery life, while motion artifacts result in low data quality and information loss.

[0023] Reference Figure 1 This illustrates the flow of a method performed by a functional ultrasound-based skin microcirculation imaging system according to an embodiment of this application. This method can be applied to... Figure 2 The imaging system shown.

[0024] S100: Continuously monitor the motion state of an imaging device, the motion state being characterized by raw motion data acquired by an inertial measurement unit mounted on the imaging device, and generate a dynamic motion intensity sequence in real time based on the raw motion data.

[0025] In one specific embodiment, the imaging system is implemented as a wearable patch device designed to adhere to the skin surface where microcirculation monitoring is required, such as the area surrounding a wound in a diabetic foot ulcer patient. The system integrates an Inertial Measurement Unit (IMU), typically packaged as a Micro-Electro-Mechanical System (MEMS) chip containing at least one triaxial accelerometer and one triaxial gyroscope.

[0026] The continuous monitoring process is led by a state decision and power gating module within the system (e.g., an ultra-low-power microcontroller MCU). This module uses an internal hardware timer to periodically read raw measurement data from the inertial measurement unit at a preset, relatively high sampling frequency, such as, but not limited to, 100 Hz. A sampling rate of 100 Hz means data is acquired every 10 milliseconds (ms), a frequency sufficient to capture most of the rapid motion details in daily human activities, providing high temporal resolution input data for subsequent accurate motion state determination.

[0027] The raw motion data specifically includes raw triaxial acceleration data and raw triaxial angular velocity data. Raw triaxial acceleration data is typically represented as acceleration components on three mutually orthogonal axes (e.g., X-axis, Y-axis, and Z-axis), denoted as... These data reflect the sum of all linear accelerations experienced by the device, including both dynamic accelerations generated by the user's limb movements and the static projection component of the Earth's gravitational field in the device's coordinate system. The raw data for the three-axis angular velocities are represented as the angular velocity components of the device's rotation around these three axes, denoted as... It directly reflects the rotational motion state of the device.

[0028] After acquiring the raw motion data, the processor within the state decision and power gating module needs to preprocess it in real time to generate a dynamic motion intensity sequence that is more meaningful for subsequent decisions. One of the core objectives of this process is to eliminate the interference of the static gravity component. In a stationary state, regardless of the device's attitude, the magnitude of the vector sum of its triaxial accelerometer readings should theoretically be equal to a standard gravitational acceleration, i.e., 1g (approximately...). When the device moves, its readings are the vector sum of the gravitational acceleration vector and the dynamic acceleration vector. Therefore, in order to accurately extract the dynamic acceleration information generated purely by motion, this embodiment employs an efficient dynamic degravity processing method. This method first calculates the vector magnitude of the raw triaxial acceleration data, i.e., the magnitude of the resultant acceleration: Subsequently, the intensity of the pure dynamic acceleration is estimated by explicitly subtracting the magnitude of a standard gravitational acceleration (i.e., 1.0g) from this resultant acceleration modulus and taking its absolute value: Here, 1.0g is a scalar constant. This method of removing DC from the vector magnitude, while an approximation, avoids performing complex attitude calculations (such as quaternion filtering) requiring numerous floating-point operations to accurately separate the gravity vector on low-power MCUs with extremely limited computing resources, thus achieving a balance between accuracy and computational cost. The final generated... This constitutes a scalar time series, namely a dynamic acceleration intensity series, whose magnitude directly reflects the intensity of the device's translational motion.

[0029] Simultaneously, to characterize the intensity of the rotational motion, the processor also performs vector magnitude calculations on the raw triaxial angular velocity data to obtain the magnitude of the resultant angular velocity: The resultant angular velocity sequence ω(t) is also a scalar time series, and its magnitude reflects the speed of the device's rotational motion.

[0030] Finally, the dynamic acceleration intensity sequence Together with the resultant angular velocity sequence ω(t), they constitute a dynamic motion intensity sequence characterizing the complete motion state of the device. This sequence is stored in real time in a circular buffer within the state decision and power gating module for subsequent state decision-making in step S200.

[0031] For example, suppose at a certain moment The state decision and power gating module reads a set of raw data from the inertial measurement unit via the I2C bus. The raw triaxial acceleration data is as follows: The unit is g; the raw data for the triaxial angular velocity are... The unit is degrees per second (deg / s). The processor within the module first processes the acceleration data. It calculates the resultant acceleration modulus: Next, the dynamic acceleration intensity is calculated: This value is extremely small, intuitively indicating that the device is experiencing almost no linear acceleration other than gravity at this moment. The processor then processes the angular velocity data to calculate the resultant angular velocity: This value indicates that the device is slightly rotated. Finally, at time... The current value of the generated dynamic motion intensity sequence is These two values ​​will be used for the instantaneous state determination in step S200. At another moment... The user is walking, and the raw triaxial acceleration data read might be (0.5, -1.2, 0.8)g. The processor calculates the resultant acceleration modulus: Calculate the dynamic acceleration intensity: This value is significantly greater than zero, accurately reflecting the body's vertical movement and forward / backward acceleration during walking. This series of calculations is repeated continuously at a frequency of 100Hz, forming a continuous stream of dynamic motion intensity data.

[0032] S200: Determine whether the imaging device has entered a continuous static state, the determination being based on a comparison of the dynamic motion intensity sequence with a preset static threshold.

[0033] This step is executed in the state decision and power gating module (MCU), the core of which is to implement a highly reliable state machine to accurately identify the golden window that can be used to trigger imaging from the continuous motion data stream. The decision process is not a simple instantaneous comparison, but includes a complex logic of dual constraints and time accumulation to ensure extremely high decision robustness and effectively filter out various pseudo-stationary states.

[0034] Specifically, the decision-making process can be broken down into two sub-steps: determining the instantaneous state of stillness and confirming the state of continuous stillness.

[0035] First, during the instantaneous stillness determination phase, at each sampling moment in step S100 (i.e., every 10ms), the module compares the current value of the real-time generated dynamic motion intensity sequence with a set of preset stillness thresholds. This set of stillness thresholds includes an acceleration stillness threshold. and an angular velocity rest threshold These two thresholds together form a joint constraint matrix of translation and rotation. A Boolean logic check is performed: if and only if the real-time dynamic acceleration intensity... Strictly less than the acceleration rest threshold And (AND) the real-time resultant angular velocity ω(t) is strictly less than the angular velocity rest threshold. Only when the system determines that the device is in an instantaneous static state at sampling time t will it be considered that the device is in a static state. At this time, an internal Boolean flag, such as... If any one of the conditions is not met, the flag is set to True; otherwise, if any one of the conditions is not met, the flag is set to False.

[0036] Acceleration rest threshold The setting of the sensor is crucial. It must be small enough to filter out conscious user movements such as walking, turning, and arm swinging. However, it must also be larger than the unavoidable minute physiological vibrations of the human body when seemingly still, such as the fluctuations in the user's body surface caused by breathing and heartbeat, as well as the background noise of the sensor itself. For example, It can be set to 0.05g. This value can be chosen based on experimental data, for example, during the system initialization calibration phase (S010), having the user remain seated or in a static state and recording the data for a period of time. The data is used to set a threshold by taking the 99th percentile of its statistical distribution. This allows for personalized adaptive settings.

[0037] Angular velocity rest threshold They are extremely sensitive to rotational motion. For devices worn on the wrist, ankle, or other similar locations, even slight rotation can cause changes in the contact angle and pressure between the ultrasound probe and the skin, resulting in severe decorrelation of the ultrasound echo signal and producing artifacts. Therefore, strict limitation of angular velocity is a necessary condition for ensuring image quality. For example, It can be set to 1.5 degrees / second (deg / s).

[0038] Secondly, during the confirmation phase of a sustained static state, the system needs to address the potential misjudgment caused by momentary stillness. For example, at the instant of gait transition or during brief dead points in certain movements, the dynamic motion intensity of one or more sampling points may fall below a threshold. If imaging is triggered solely based on this momentary state, the device may enter a period of rapid motion the next instant, leading to acquisition failure. To avoid this, this embodiment introduces a time-dimensional constraint, namely a static duration threshold. .

[0039] In practice, the state decision and power gating module maintains a timer. When the aforementioned instantaneous static state flag... When it first becomes true, the timer begins to increment. If The timer continues to increment as long as the value remains true for the next few sampling points. Once a certain sampling point is reached... If the timer turns false, indicating that the device has experienced movement exceeding the threshold, the timer will be immediately reset to zero. Only when the accumulated time of the timer, i.e., the duration of the instantaneous stillness, exceeds the preset stillness duration threshold will the timer be reset. Only then does the system finally determine that the imaging device has entered a sustained static state. At this point, the module will generate a unique, high-level active imaging trigger event signal, which will drive the subsequent S300 steps.

[0040] Static duration threshold The goal of this setting is to filter out all pauses of apparent death with a duration less than a certain threshold. This value should be greater than the maximum instantaneous pause that may occur during normal human activity. For example, It can be set to 2.0 seconds. This means that the device must remain extremely quiet in both translation and rotation dimensions, and this quiet state must be maintained stably for at least 2 seconds before the system considers it a golden window with a high probability of continuing to remain stable and suitable for imaging operations.

[0041] For example, a sequence of events is traced to illustrate the complete logic of S200. The default parameters are: .

[0042] At time t=10.0s, the system detected and Instantaneous stillness marker When set to true, the steady-state timer starts counting from 0.

[0043] Over the next 1.9 seconds, from t=10.1s to t=11.9s, at each sampling point Both ω and t continuously satisfy the condition of being less than their respective thresholds. The timer steadily accumulates, and at t=11.9s, the count is 1.9 seconds.

[0044] At time t=12.0s, the monitored value still meets the condition, and the timer increments to 2.0 seconds. At this moment, because the timer value has reached... The condition for a sustained static state is met. The state decision and power gating module immediately generates an imaging trigger signal. Meanwhile, to prevent repeated triggering, the timer can be cleared or paused after triggering, and then re-enabled after the current imaging process is completed.

[0045] In another scenario, if the device stabilizes for 1.5 seconds, and at t=11.5s, the user slightly moves their wrist, causing the detected... So, the instantaneous stillness marker The status will immediately become false, and the steady-state timer will be instantly reset to zero. The system will not trigger imaging but will instead wait for the start of the next stable cycle that meets the conditions. This mechanism ensures that only a time-tested steady state can initiate the high-power imaging task.

[0046] S300: In response to the determination that the imaging device has entered the continuous static state, wake up an ultrasound imaging link and control the ultrasound imaging link to acquire raw ultrasound echo data; and during the acquisition of raw ultrasound echo data, if the dynamic motion intensity is detected to exceed a preset fuse threshold, immediately stop the acquisition of raw ultrasound echo data and put the ultrasound imaging link into a sleep state.

[0047] This step is the key transition from perception to execution in this invention, and it includes two parallel and core mechanisms: an event-based wake-up acquisition mechanism and a concurrent circuit breaker protection mechanism that runs through the entire acquisition process.

[0048] First, after the S200 step successfully generates the imaging trigger event signal, the wake-up and acquisition process is initiated. The ultrasound imaging link is the high-power core of the system, typically comprising the M300 ultrasound transmitter and receiver array module and the M400 UMI signal processing and imaging module. In the sentinel modes of S100 and S200, this entire link is completely powered down and in a deep sleep state to achieve extreme energy efficiency. The imaging trigger signal can physically be a high-level output from the GPIO (General Purpose Input / Output) pin of the state decision and power gating module (MCU). This GPIO pin directly controls a power switching element in the power management module (M600), such as a P-channel MOSFET. When the GPIO outputs a high level, the MOSFET's gate voltage is pulled high, turning it on and instantly connecting the main battery power supply to the M300 and M400 modules.

[0049] Upon power-up, the M300 and M400 modules execute a rapid startup initialization procedure. The transmit controller in the M400 (typically implemented using an FPGA) is immediately activated and generates a series of high-voltage pulses based on preset imaging sequence parameters. These pulses, via a transmit / receive (T / R) switch, drive the array elements of the high-frequency (e.g., center frequency greater than 20 MHz) miniature piezoelectric transducer (PZT) or capacitive micromechanical ultrasonic transducer (CMUT) array in the M300. These elements vibrate, transmitting ultrasonic energy into the underlying skin tissue at a specific sequence and angle (e.g., emitting plane waves).

[0050] The emitted ultrasound waves propagate through tissues, scattering and reflecting when they encounter interfaces with impedance mismatches (such as blood vessel walls or flowing red blood cell clusters). The echo signals scattered by red blood cells flowing in microvessels carry Doppler frequency shift information about blood flow velocity and direction. These extremely weak radio frequency (RF) echo signals are captured by the M300's receiving channel and amplified by a low-noise amplifier (LNA) to improve the signal-to-noise ratio. Subsequently, the amplified analog RF signals are fed into the M400's high-speed analog-to-digital converter (ADC) array for synchronous digitization. A complete imaging acquisition (often called an ensemble) may contain hundreds of such transmit-receive cycles to accumulate enough data for subsequent Doppler analysis. All the digitized raw RF data is written in real-time into the M400's internal high-speed static random access memory (SRAM), forming a large raw RF data matrix awaiting further processing.

[0051] However, although the entire wake-up and data acquisition process is triggered by a specific static event, it cannot guarantee that the user will not suddenly move during the acquisition period (which may last from hundreds of milliseconds to one second). If violent movement occurs during the acquisition process, the acquired RF data will be filled with phase noise and motion artifacts, making subsequent calculations futile and consuming a huge amount of energy. To cope with such unpredictable emergencies, this invention introduces a crucial concurrent circuit breaker protection mechanism.

[0052] This mechanism operates in parallel and independently with the ultrasound transmission and reception throughout the entire physical acquisition time of the S300. The state decision and power gating module (MCU) does not cease monitoring the motion state after outputting the imaging trigger signal. Instead, it enters a high-alert escort mode. In this mode, it continuously receives and calculates dynamic acceleration intensity at a frequency of 100Hz. This is then compared to a preset extreme motion triggering threshold that is more lenient than the static threshold but more responsive. Compare them.

[0053] Circuit breaker threshold The setting must strictly satisfy a relationship: This represents the limit of phase deviation caused by physical displacement that ultrasonic signal processing algorithms (especially SVD clutter filters) can tolerate. It allows for some very slight, sub-zero phase deviations during acquisition. The fluctuations were ignored, but once a sudden surge occurred... For strenuous exercise, immediate action is necessary. For example, It can be set to 0.15g.

[0054] This comparison process can be implemented by a hardware comparator in the MCU, with its output bound to a high-priority non-maskable interrupt (NMI). This hardware implementation ensures that the response time for the circuit breaker decision is on the microsecond level, far faster than software polling. Once the hardware detects... When this event occurs, the NMI interrupt will be triggered immediately. The sole task of the interrupt service routine (ISR) is to forcibly pull low the level of the GPIO pin previously used to power on. This will instantly turn off the power MOSFET, cutting off the main power supply to the M300 and M400. Simultaneously, the MCU can send a reset signal to the M400, clearing the RF data that has been acquired in its SRAM but is now confirmed as invalid. The entire system is forcibly reset, returning to the S100's pure sentinel listening state.

[0055] This circuit breaker mechanism acts like a circuit fuse. It sacrifices an unsuccessful acquisition to protect the system from continuing to perform extremely power-intensive processing steps such as SVD matrix operations when the data is known to be contaminated, thus achieving ultimate protection of the system's energy.

[0056] For example, a simulated fuse failure event is presented. The system successfully triggers imaging at t=22.0s, powering on the M300 and M400 and initiating a 200-frame ensemble acquisition that takes 1 second. Halfway through the acquisition, at t=22.5s, the user suddenly turns over, causing... The weight spiked instantly to 0.30g. This value is far greater than the preset circuit breaker threshold. The MCU's hardware comparator immediately detected this out-of-limit event, triggering an NMI interrupt. The interrupt service routine executed within microseconds, pulling the GPIO pin of the control MOSFET low. Power to the M300 and M400 was cut off, ultrasonic transmission and reception immediately ceased, and the first 100 frames of invalid data in the SRAM were cleared. The system returned to the S100's low-power monitoring mode. This failed acquisition consumed only 0.5 seconds of energy. Without the fuse mechanism, the system would have continued to complete the remaining 0.5 seconds of invalid acquisition and spent several seconds performing a full, power-intensive UMI calculation on this artifact-ridden 1-second data, only to find the result unusable. The fuse mechanism prevented this huge energy waste.

[0057] S400: If the acquisition of the original ultrasound echo data is successfully completed and no termination is triggered, execute the microvascular imaging algorithm based on the original ultrasound echo data to generate a two-dimensional blood flow perfusion matrix; and cause the ultrasound imaging link to enter the sleep state after generating the two-dimensional blood flow perfusion matrix.

[0058] When the data acquisition process in step S300 is successfully completed without triggering the S335 fuse protection mechanism, it means that the system has successfully acquired a set of high-quality raw RF data within a complete golden window. At this point, the control flow enters S400, where the M400 UMI signal processing and imaging module processes this data and extracts the core microcirculation information.

[0059] The first stage of this step involves executing the core Ultramicrovascular Imaging (UMI) algorithm. The algorithm's input is the raw RF data matrix stored in the M400's SRAM. To facilitate spatiotemporal filtering, this data is first logically organized into a core data structure entity: the ultrasound spatiotemporal data tensor. This tensor is a three-dimensional complex matrix, whose dimensions can be represented as... .in, (Fast time axis) represents the number of depth sampling points along the direction of ultrasonic beam propagation, for example, 1024 sampling points, which corresponds to the depth range of the imaging. The (slow time axis or ensemble axis) represents the number of pulses that are repeatedly transmitted and received at the same spatial location (or the same transmission angle), such as 200 pulses. This dimension is key to extracting Doppler information. The spatial axis represents the number of physical channels (or elements) in the ultrasonic transducer array, such as 64 channels. This dimension represents the lateral spatial extent of the imaging. This tensor is the physical carrier for all subsequent calculations.

[0060] The core step of the UMI algorithm is the Singular Value Decomposition (SVD) spatiotemporal filter. Its purpose is to separate the extremely weak blood flow signal and the extremely high-energy tissue clutter signal from the mixed signal. Tissue clutter mainly originates from slow tissue movement caused by respiration, heartbeat, etc., and the minute slippage at the probe-skin contact interface. Its energy is typically several orders of magnitude higher than that of the blood flow signal, but its Doppler frequency shift is low (i.e., its speed is slow). SVD can effectively decompose the signal into different subspaces based on its spatiotemporal correlation. Specifically, the algorithm expands the aforementioned three-dimensional data tensor along the slow time axis and spatial axis, constructing a two-dimensional spatiotemporally cascaded data matrix H. Then, singular value decomposition is performed on matrix H to obtain... Here, Σ is a diagonal matrix whose diagonal elements are... These are called singular values ​​and are arranged in descending order. U and V are unitary matrices, and their column vectors are called the left and right singular vectors, respectively. Physically, the higher the energy of a signal component, the larger its corresponding singular value. Since tissue clutter energy is much greater than that of blood flow signals, the first few largest singular values ​​and their corresponding singular vectors constitute the clutter subspace. The subsequent smaller singular values ​​and their corresponding singular vectors constitute the blood flow subspace.

[0061] The operation of an SVD filter is to force the front of the clutter subspace to be filtered out. Each singular value is set to zero, forming a new diagonal matrix Σ′. Then, using the modified singular value matrix Σ′ and the original U and V matrices, a new, clutter-free pure blood flow signal matrix is ​​reconstructed. truncation order This is a key parameter representing the number of principal clutter components that are filtered out. This value can be a fixed empirical value, such as 3. More advanced systems can dynamically analyze singular values. The distribution curve. Since clutter singularities are typically much larger than blood flow singularities, this distribution curve exhibits a distinct inflection point. The system can automatically calculate the curvature of this curve, find the index corresponding to the point of maximum curvature, and use it as an adaptive... This allows for adaptive filtering of clutter intensity in different scenarios.

[0062] After performing SVD filtering to remove high-intensity tissue interface reflections from the data, the M400's processor (e.g., a digital signal processor, DSP) then processes the filtered pure blood flow signal matrix H′ to calculate the blood flow intensity at each spatial location. This is typically achieved by calculating the Doppler shift power spectrum of the blood flow. For each spatial point (pixel) in the imaging region, its signal sequence is extracted along the slow time axis (Ensemble axis), and a Fourier transform is performed to obtain the Doppler spectrum. The power (square of the amplitude) of this spectrum is then integrated over the entire frequency domain (or a specific frequency range). This integral represents the total blood flow energy, or blood volume, at that spatial point during the acquisition time. After performing this operation for all spatial points, a two-dimensional blood perfusion matrix P(x,z) is generated, where each element... The value represents the spatial coordinates. The local blood flow integral energy at a given location. This matrix is ​​a visualized image of microcirculation perfusion.

[0063] For example, suppose the FPGA hardware accelerator within the M400 processes a data tensor of size (1024, 200, 64). First, it is rearranged into a two-dimensional matrix H of size (1024*64, 200). The FPGA performs SVD on H, obtaining a singular value vector Σ=(150.3,125.8,90.1,5.2,4.8,...,0.1). It can be seen that the first three singular values ​​are much larger than the subsequent values. System settings Therefore, the FPGA constructs a new singular value vector Σ′=(0,0,0,5.2,4.8,...,0.1) and uses it to reconstruct the filtered matrix H′. Next, the DSP core takes over H′ and restores it to its three-dimensional form. For each spatial location (1024*64 locations in total), the DSP extracts a complex time series of length 200 and performs a 200-point Fast Fourier Transform (FFT) to obtain the Doppler power spectrum. Then, the power values ​​of all points on the spectrum are summed to obtain a scalar value. Combining the scalar values ​​of all 65536 spatial points forms a 1024x64 two-dimensional blood perfusion matrix P(x,z). Regions with high values ​​in the image appear as bright spots, representing microvessels with rich blood flow.

[0064] After generating this core diagnostic image, the M400 module performs task cleanup to return the system to power-saving mode. It sends a task completion pulse signal to the Status Decision and Power Gating Module (MCU), which can be achieved via a dedicated acknowledge pin or a specific message on the shared bus. Upon receiving this signal, the MCU immediately responds by pulling low the GPIO pin voltage of the control power MOSFET. This cuts off the main imaging link power to both the M300 and M400, causing them to re-enter deep sleep mode. At this point, a complete event-driven closed loop of perception-decision-execution-sleep is perfectly completed. The system returns to the S100's ultra-low-power sentry mode, quietly awaiting the next eligible static event.

[0065] In an optional embodiment, the system is further configured to post-process the two-dimensional blood perfusion matrix to extract quantized functional parameters. A key parameter is microvessel density (MVD). To calculate MVD, the microprocessor within the M400 can utilize Otsu's Method to perform global adaptive binarization of the blood perfusion matrix P(x,z). Otsu's Method is a classic image segmentation algorithm that searches for the threshold that maximizes the inter-class variance between background pixel classes and foreground (vessel) pixel classes by iterating through all possible grayscale thresholds, denoted as . This threshold is considered the optimal segmentation threshold. Find... Then, the processor will inject all elements in the infusion matrix that satisfy the following conditions. Pixels with visible microvessels are identified as valid microvessel pixels, while the rest are considered background pixels. Finally, the total number of valid microvessel pixels is calculated and divided by the total number of pixels in a predefined region of interest (ROI) to obtain a ratio. This ratio is the quantized microvessel density (MVD) parameter. This scalar value intuitively reflects the richness of blood vessels within the monitored area and has significant clinical value for assessing angiogenesis during wound healing.

[0066] The calculated MVD parameters, along with the original two-dimensional blood perfusion matrix, can be appended with a UTC timestamp generated by a real-time clock (RTC) and serialized into a standardized diagnostic data packet. This data packet is pushed to the non-volatile Flash memory of the device's data storage and communication module (M500) via SPI or I2C bus for long-term storage. When needed, this data can be wirelessly transmitted to external smartphones or medical monitoring devices via Bluetooth Low Energy (BLE) for analysis by doctors.

[0067] In another alternative embodiment, to address special circumstances, such as for users with persistent tremors (e.g., severe Parkinson's patients), the system may be unable to find a window that meets stringent stillness conditions for an extended period, leading to a state of starvation. To address this, a forced capture attenuation mechanism can be configured in the system firmware. A timer within the state decision and power gating module monitors the time elapsed since the last successful imaging. When this time exceeds a preset long threshold (e.g., 2 hours), the system automatically activates the attenuation mechanism. This mechanism uses a dynamic stepping coefficient to temporarily and gradually amplify the decision threshold. and and circuit breaker threshold (For example, increasing the threshold by 50% each time). This lowers the requirement for complete stillness, increasing the chance of capturing an image. Images captured under these relaxed criteria are forcibly associated with a low-confidence data label after the MVD parameters are calculated. This ensures the continuity of data records over time, avoiding data gaps, and also alerts subsequent analysts to potential artifact risks associated with the data acquisition conditions through the label.

[0068] Reference Figure 2 This application also provides a skin microcirculation imaging system 10 based on functional ultrasound. This system 10 is the physical carrier of the above-described method, and its internal structure is similar to... Figure 2 The modules shown correspond to those shown.

[0069] System 10 includes: Inertial Measurement and Motion Sensing Module M100: This module is typically a MEMS chip integrating a three-axis accelerometer and a three-axis gyroscope, such as InvenSense's MPU-6050 series or Bosch's BMI series sensors. It is configured to continuously acquire motion information of the device at a preset frequency (e.g., 100Hz) and output raw motion data streams via standard digital communication interfaces (e.g., I2C or SPI).

[0070] M200 State Decision and Power Gating Module: The core of this module is an ultra-low-power microcontroller (MCU), such as the ARM Cortex-M0+ core-based series, like STMicroelectronics' STM32L0 series. This MCU runs firmware implementing the core logic of steps S100, S200, and S335. It receives raw data from the M100 and performs real-time gravity calculations using its internal hardware multiply-accumulate unit (MAC); it uses its internal high-precision hardware timers and comparators to determine the continuous static state and handle concurrent fuse triggering. One of its GPIO output pins is configured in push-pull mode to directly drive the power switch in the power management module, thereby controlling the power supply to the ultrasound imaging link.

[0071] The ultrasound imaging link consists of the following two sub-modules: M300 Ultrasonic Transmitter and Receiver Array Module: This module includes a high-frequency miniature ultrasonic transducer array. This array can be made of piezoelectric materials (such as PZT) or using CMUT technology, with a center frequency typically above 20MHz to achieve the high resolution required for superficial skin microvessels. The module also integrates analog front-end circuitry such as a high-voltage pulse generator, transmit / receive (T / R) switch, and low-noise preamplifier (LNA). It is responsible for converting electrical signals to acoustic signals and vice versa.

[0072] UMI Signal Processing and Imaging Module M400: This is a high-performance digital processing front-end, typically employing a hybrid architecture. High-speed parallel matrix operations, particularly SVD decomposition, are implemented using a low-power field-programmable gate array (FPGA) (such as the Xilinx Artix-7 series) to meet real-time processing requirements. Serial, logic control, and scalar statistical calculations, such as Otsu's MVD quantization, are performed by a cooperating digital signal processor (DSP) or another microprocessor. The module also integrates a high-speed ADC array and a large-capacity SRAM for temporarily storing raw RF data and intermediate results.

[0073] The connections and data flows between modules are as described above. The power management module M600 (not shown separately in the diagram, but its function is controlled by M200) provides continuous low-power standby power to M100 and M200. When M200 makes an imaging decision, it controls the power switch in M600 to connect the main operating power to M300 and M400. Diagnostic data packets processed by M400 are sent to the data storage and communication module M500 (which includes, for example, a Flash memory chip and a BLE RF transceiver) for storage and wireless transmission. This modular approach and event-based power gating achieve high energy efficiency and efficient collaborative operation.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0075] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware.

[0076] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A skin microcirculation imaging system based on functional ultrasound, characterized in that, include: An inertial measurement and motion sensing module is configured to monitor the motion state of the system in real time and output raw motion data; A state decision and power gating module is configured to receive the raw motion data, generate a dynamic motion intensity sequence in real time, compare the dynamic motion intensity sequence with a preset static threshold to determine whether the system has entered a continuous static state, output a wake-up signal when the system enters a continuous static state, and output a stop signal if the dynamic motion intensity exceeds a preset fuse threshold during subsequent data acquisition. An ultrasound imaging link includes an ultrasound transmitting and receiving array module and an ultrasound microvascular imaging signal processing and imaging module. The ultrasound imaging link is configured to be activated in response to the wake-up signal to acquire raw ultrasound echo data, and to enter a dormant state in response to the stop signal. The state decision and power gating module is also configured to control the power supply to the ultrasound imaging link.

2. The system according to claim 1, characterized in that, The state decision and power gating module is further configured to generate the dynamic motion intensity sequence in real time by performing the following operations: Acquire the raw triaxial acceleration data and raw triaxial angular velocity data output by the inertial measurement and motion sensing module; The vector magnitude of the raw triaxial acceleration data is calculated, and the standard gravitational acceleration component is removed from the vector magnitude to generate a dynamic acceleration intensity sequence. as well as The vector magnitude of the raw triaxial angular velocity data is calculated to generate a resultant angular velocity sequence; The dynamic motion intensity sequence includes the dynamic acceleration intensity sequence and the resultant angular velocity sequence.

3. The system according to claim 2, characterized in that, The state determination and power gating module is further configured to determine whether the system has entered a continuous static state by performing the following operations: At each sampling moment, the real-time generated dynamic acceleration intensity is compared with a preset acceleration rest threshold, and the real-time generated resultant angular velocity is compared with a preset angular velocity rest threshold. When the dynamic acceleration intensity is less than the acceleration rest threshold and the resultant angular velocity is less than the angular velocity rest threshold, the system is determined to be in an instantaneous rest state at that sampling moment; and When the duration of the instantaneous stillness exceeds a preset stillness duration threshold, the system is determined to enter the continuous stillness state.

4. The system according to claim 1, characterized in that, The state decision and power gating module responds to the wake-up signal by turning on the main power supply to the ultrasound imaging link.

5. The system according to claim 1, characterized in that, The state decision and power gating module responds to the abort signal by cutting off the main power supply to the ultrasound imaging link and clearing the original ultrasound echo data that was acquired and temporarily stored by the ultrasound imaging link before the abort.

6. The system according to claim 1, characterized in that, The ultrasound microvascular imaging signal processing and imaging module is further configured as follows: If the acquisition of the original ultrasound echo data is successfully completed without triggering an interruption, an ultra-microvascular imaging algorithm is executed based on the original ultrasound echo data to generate a two-dimensional blood flow perfusion matrix; and After generating the two-dimensional blood perfusion matrix, the state decision and power gating module is notified to put the ultrasound imaging link into the sleep state.

7. The system according to claim 6, characterized in that, The ultrasound microvascular imaging signal processing and imaging module is further configured to execute the ultramicrovascular imaging algorithm by performing the following operations: The original ultrasonic echo data is constructed into an ultrasonic spatiotemporal data tensor; Singular value decomposition spatiotemporal filtering is performed on the ultrasound spatiotemporal data tensor, the filtering being achieved by setting the first few singular value components corresponding to tissue clutter to zero; and Based on the filtered ultrasound spatiotemporal data tensor, the Doppler frequency shift power spectrum is calculated and integrated to generate the two-dimensional blood perfusion matrix.

8. The system according to claim 6 or 7, characterized in that, The ultrasound microvascular imaging signal processing and imaging module is further configured as follows: Based on the two-dimensional blood perfusion matrix, a global adaptive binarization algorithm is used to classify the pixels in the two-dimensional blood perfusion matrix into effective microvascular pixels and background pixels. as well as A microvessel density parameter is generated by quantifying the number of effective microvessel pixels and the total number of pixels in a preset region of interest.

9. The system according to claim 1, characterized in that, The circuit breaker threshold is configured as an acceleration circuit breaker threshold, and the value of the acceleration circuit breaker threshold is greater than the value of the acceleration stationary threshold.

10. A skin microcirculation imaging method based on functional ultrasound, characterized in that, include: The motion state of an imaging device is continuously monitored. The motion state is characterized by raw motion data acquired by the inertial measurement unit on the imaging device, and a dynamic motion intensity sequence is generated in real time based on the raw motion data. The determination of whether the imaging device has entered a continuous static state is based on a comparison of the dynamic motion intensity sequence with a preset static threshold. In response to a determination that the imaging device has entered the sustained static state, an ultrasound imaging link is activated, and the ultrasound imaging link is controlled to acquire raw ultrasound echo data; and If the dynamic motion intensity exceeds a preset fusing threshold during the acquisition of the raw ultrasound echo data, the acquisition of the raw ultrasound echo data will be immediately stopped, and the ultrasound imaging link will enter a dormant state.

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