MRI real-time triggering method and system based on SCG-PPG signal, terminal and medium

Abstract

The invention discloses an MRI real-time triggering method and system based on SCG-PPG signals, a terminal and a medium, and the method comprises the steps: capturing the periodic motion of a heart, obtaining an SCG signal, and detecting the dynamic change of blood flow, and obtaining a PPG signal; the SCG signals and the PPG signals are preprocessed, periodic changes of the SCG signals and the PPG signals are determined, and a trigger point of each heart cycle is calibrated; a mathematical model is established based on the trigger points of each heart cycle, and the optimal MRI trigger opportunity is adjusted in real time. The problems that in a traditional MRI triggering method, signal abnormity is prone to occurring, triggering precision is affected, triggering is not timely due to physiological delay, and imaging quality is affected are effectively solved, the reliability of data collection is greatly improved, high-quality heart image data are provided, and the method has important application value.

Description

Technical Field

[0001] The present invention relates to the field of magnetic resonance imaging technology, and in particular to a real-time triggering method, system, terminal and medium of MRI based on SCG-PPG signal. Background Art

[0002] Magnetic resonance imaging (MRI) is a high-resolution, non-invasive medical imaging technology that plays an important role in the diagnosis and evaluation of complex lesions such as cardiovascular diseases (CD). However, during MRI scanning, the patient's physiological movements (such as heart beats, blood flow, and respiratory movements) can cause motion artifacts. Especially in high-field magnetic resonance environments (such as 3T and above), these artifacts have a significant impact on image quality and diagnostic accuracy. In order to improve imaging quality, it is usually necessary to synchronously acquire data during the patient's specific physiological cycles (such as cardiac systole or diastole) to achieve precise timing trigger control, thereby effectively reducing artifacts and ensuring the reliability of diagnostic results. Therefore, the development of efficient MRI real-time triggering technology is of great significance to improving image quality.

[0003] At present, the traditional method widely used for MRI triggering is the triggering technology based on the electrocardiogram (ECG) signal. ECG synchronizes data acquisition by detecting the electrical activity of the heart and has a high degree of application maturity. However, this technology has obvious shortcomings in practical applications. On the one hand, pathological conditions such as arrhythmia and myocardial infarction may cause abnormal ECG signals and affect the triggering accuracy. In addition, in a high-field magnetic resonance environment, ECG signals are susceptible to electromagnetic interference, resulting in signal fluctuations or loss, further reducing the stability of the trigger. These problems limit the applicability of traditional ECG triggering technology in complex clinical environments. In response to the problems of ECG gating, photoplethysmography (PPG) signals measured by fingertips are often used as an alternative. However, due to the long distance between the PPG signal and the heart, there is a physiological delay of up to several hundred milliseconds between the generated trigger signal and the actual heart activity, which cannot reserve enough time for subsequent MR data acquisition, further limiting the imaging quality. It can be seen that traditional MRI triggering methods are prone to signal anomalies, which affect the triggering accuracy, and there are physiological delays that lead to untimely triggering, affecting the imaging quality. Summary of the invention

[0004] The present invention provides a real-time MRI triggering method, system, terminal and medium based on SCG-PPG signals. The technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a real-time triggering method for MRI based on SCG-PPG signals, wherein the method comprises: Capture the periodic movement of the heart to obtain SCG signals, and detect the dynamic changes in blood flow to obtain PPG signals; Preprocess the SCG signal and the PPG signal, determine the periodic changes of the SCG signal and the PPG signal, and calibrate the trigger point of each cardiac cycle; A mathematical model for real-time calculation of the optimal MRI triggering timing is established based on the triggering point of each cardiac cycle, and the optimal MRI triggering timing is adjusted in real time.

[0005] In one implementation, capturing the periodic motion of the heart to obtain an SCG signal includes: Position the defocus camera at the fourth rib in the lower left part of the chest cavity and irradiate with the laser system; Adjust the configuration of the defocus camera to collect the speckle image; The optical flow method is used to deeply analyze the collected speckle images, calculate the motion amplitude of the speckle in the X-axis and Y-axis directions, and obtain the motion amplitude sequence; Divide the motion amplitude sequence and determine the motion angle of each segment, which reflects the direction of heart vibration; Based on the motion angle of each segment, the SCG signal is obtained.

[0006] In one implementation, adjusting the configuration of the defocus camera includes: Adjust the defocused camera so that the distance between the focal plane and the chest plane is greater than the distance between the lens and the focal plane.

[0007] In one implementation, detecting dynamic changes in blood flow to obtain a PPG signal includes: The optical sensor covers the key parts of the face, emits a periodic square wave synchronization signal, and collects images; Extracting a portion containing skin pixels from the acquired image to obtain a skin area; Based on the skin area, a G channel image sequence is determined, and a PPG signal is extracted from the G channel image sequence.

[0008] In one implementation, preprocessing includes: filtering, detrending and normalization; wherein, filtering is used to remove high-frequency noise and / or low-frequency noise in SCG signals and PPG signals; detrending is used to remove linear trends and / or nonlinear trends in SCG signals and PPG signals; and normalization is used to map the amplitudes of SCG signals and PPG signals into a unified standard range.

[0009] In one implementation, determining the periodic changes of the SCG signal and the PPG signal includes: Perform real-time peak detection processing on SCG signals and PPG signals to capture periodic changes of SCG signals and PPG signals; The real-time peak detection processing includes: sliding time window processing and peak detection, wherein the sliding time window processing includes: dividing the long-term continuous SCG signal and PPG signal into multiple small segments, and gradually processing each fixed-length data subset in the SCG signal and the PPG signal; Peak detection includes: determining the local maximum values ​​in the SCG signal and the PPG signal respectively to obtain the potential peak positions of the SCG signal and the PPG signal; obtaining a preset height threshold, and using the height threshold to filter the local maximum values ​​to obtain the filtered local maximum values; determining the minimum peak spacing, and analyzing the filtered local maximum values ​​based on the minimum peak spacing to obtain the real-time peaks in the SCG signal and the PPG signal and determine the heart cycle.

[0010] In one implementation, adjusting the optimal MRI triggering timing in real time includes: The physiological status information is obtained, and the constructed mathematical model is combined with the physiological status information to adjust the optimal MRI triggering time in real time.

[0011] In a second aspect, an embodiment of the present invention further provides an MRI real-time triggering system based on SCG-PPG signals, wherein the system is used to implement the steps of the above-mentioned MRI real-time triggering method based on SCG-PPG signals, and the system includes: SCG-PPG signal acquisition module, used to capture the periodic movement of the heart to obtain SCG signals, and detect the dynamic changes of blood flow to obtain PPG signals; A trigger point calibration module is used to pre-process the SCG signal and the PPG signal, determine the periodic changes of the SCG signal and the PPG signal, and calibrate the trigger point of each cardiac cycle; The MRI trigger timing optimization module is used to establish a mathematical model for real-time calculation of the optimal MRI trigger timing based on the trigger point of each cardiac cycle, and to adjust the optimal MRI trigger timing in real time.

[0012] In a third aspect, an embodiment of the present invention further provides a terminal, wherein the terminal includes a memory, a processor, and an MRI real-time trigger program based on SCG-PPG signals stored in the memory and executable on the processor, and when the processor executes the MRI real-time trigger program based on SCG-PPG signals, the steps of the MRI real-time trigger method based on SCG-PPG signals of any one of the above-mentioned schemes are implemented.

[0013] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein an MRI real-time trigger program based on SCG-PPG signals is stored on the computer-readable storage medium, and when the MRI real-time trigger program based on SCG-PPG signals is executed by a processor, the steps of the MRI real-time trigger method based on SCG-PPG signals of any one of the above-mentioned schemes are implemented.

[0014] Beneficial effects: The present invention provides a real-time MRI triggering method based on SCG-PPG signals. The present invention first captures the periodic motion of the heart to obtain SCG signals, and detects the dynamic changes of blood flow to obtain PPG signals. Then, the SCG signals and PPG signals are preprocessed to determine the periodic changes of the SCG signals and PPG signals, and the trigger points of each cardiac cycle are calibrated. Finally, a mathematical model for real-time calculation of the optimal MRI triggering time is established based on the trigger points of each cardiac cycle, and the optimal MRI triggering time is adjusted in real time. The SCG signals collected in the present invention can accurately capture the tiny vibrations caused by the mechanical activity of the heart, and the PPG signals can accurately reflect the dynamic changes of blood flow, breaking through the limitations of traditional MRI triggering technology under pathological conditions, and effectively avoiding the problem of untimely triggering due to physiological delays, which affects the imaging quality. In addition, by fusing SCG signals and PPG signals, the present invention can provide more comprehensive and accurate physiological information, effectively avoiding the problem of signal abnormalities that are prone to occur in traditional MRI triggering technology and affect the triggering accuracy, greatly improving the reliability of data acquisition, and providing high-quality cardiac imaging data, which has important application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flowchart of a preferred embodiment of the MRI real-time triggering method based on SCG-PPG signals provided in an embodiment of the present invention.

[0016] Figure 2 A schematic diagram of MRI triggered imaging provided by an embodiment of the present invention.

[0017] Figure 3 A schematic diagram of collecting SCG-PPG signals provided in an embodiment of the present invention.

[0018] Figure 4A schematic diagram of the architecture of an MRI real-time triggering system based on SCG-PPG signals provided in an embodiment of the present invention.

[0019] Figure 5 A functional block diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations or steps, nor must they be executed in the order described. For example, some operations or steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0022] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms. It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish between identical or similar items with substantially identical functions and effects. For example, the first control information and the second control information are only used to distinguish different control information, and their order is not limited. Those skilled in the art can understand that the words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not necessarily limit the differences. It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0023] The MRI real-time triggering method based on SCG-PPG signal of this embodiment is applied to a terminal, which may be a computer, a smart TV, a mobile phone or other intelligent terminal products. Figure 1 As shown in , the MRI real-time triggering method based on SCG-PPG signal of this embodiment includes the following steps: Step S100: Capture the periodic movement of the heart to obtain an SCG signal, and detect the dynamic changes of blood flow to obtain a PPG signal.

[0024] In this embodiment, combined with Figure 2 As shown, it is necessary to obtain the seismocardiogram (SCG) signal and the photoplethysmography (PPG) signal in real time to provide high-quality raw data to support subsequent in-depth analysis and precise processing. To this end, this embodiment is equipped with a high-precision sensor to ensure the accuracy and real-time performance of signal acquisition, and its sampling frequency is usually set high enough to capture rapidly changing physiological signals. In this embodiment, the acquisition of SCG signals mainly relies on the optical model of the defocus camera and the laser system to significantly amplify the tiny vibrations caused by the activity of the heart machine, and these vibrations reflect detailed information about the mechanical cycle of the heart. The acquisition of PPG signals can detect the dynamic changes of blood flow with the help of optical sensors (such as cameras), thereby providing pulse wave characteristics and relevant information about hemodynamics. Specifically, the acquisition of SCG signals is based on the principle of optical interference. The beating of the heart will cause a tiny movement of the chest cavity, which will cause the interference field of the laser reflected on the chest surface to change. This change is manifested as a speckle displacement on the camera imaging plane. By capturing the changes in these speckle displacements with a high-speed camera, the SCG signal can be extracted. The acquisition of PPG signals is based on the principle of optical reflection. The light source is used to illuminate the skin surface and detect changes in reflected light to extract the pulse wave signal. When the light source is irradiated to the skin, part of the light is absorbed by the skin tissue, and part of the light is reflected back to the optical sensor. The changes in light absorption caused by blood flow will be detected by changes in the intensity of the reflected light. By extracting the alternating current (AC) signal of the reflected light, the characteristics of the pulse wave can be obtained. In this embodiment, the real-time acquisition of SCG signals and PPG signals provides a solid foundation for subsequent precise physiological data analysis triggered by MRI gating.

[0025] In practical applications, the process of SCG signal extraction first requires precise adjustment of the configuration of the defocus camera and the laser system. Figure 3As shown, camera 2 is precisely positioned at the fourth rib of the lower left side of the chest cavity, and cooperates with the laser system to irradiate the area. Camera 2 of this embodiment is a defocus camera. In order to improve the detection accuracy of the cardiac motion signal, the configuration of the defocus camera is adjusted, and the distance L1 between the focal plane and the chest plane of the defocus camera is adjusted to be greater than the distance L2 between the lens and the focal plane. This configuration of the defocus camera significantly amplifies the laser speckle image caused by cardiac vibration, so that tiny cardiac movements can be clearly presented. The ratio of L1 / L2 can be used to indicate the degree of camera defocus, and the higher the degree of defocus, the more significant the signal amplification effect. This configuration enables the defocus camera to continuously collect speckle images at a frame rate of at least 200fps and a resolution of 400×300, providing high-quality data for subsequent signal processing. After the speckle image is collected, this embodiment can use the optical flow method to conduct an in-depth analysis of the captured speckle image, accurately calculate the motion amplitude of the speckle in the X-axis and Y-axis directions, and obtain a motion amplitude sequence. Then, the motion amplitude sequence is reasonably divided by the sliding window method, and the motion angle of each segment is calculated. The movement angle It represents the direction of heart vibration and can be used to synthesize the final SCG signal. The SCG signal can accurately reflect the periodic motion of the heart and become the key to triggering the synchronous imaging of cardiac magnetic resonance imaging (CMR) equipment. Through this high-precision signal extraction and analysis method, the SCG signal can not only achieve accurate reconstruction of cardiac motion, but also provide stable and reliable data information for clinical use, and has broad application prospects.

[0026] In practical applications, the extraction of PPG signals first requires ensuring the precise adjustment of the position of the optical sensor (i.e., the camera) so as to clearly capture the facial skin area of ​​the subject. To this end, the present embodiment must accurately position the camera to ensure that it covers key parts of the face, such as the forehead, cheeks, or chin, which can effectively reflect changes in blood flow. The role of the synchronization signal is particularly critical. The present embodiment can ensure that the camera and other devices can achieve precise time synchronization during image acquisition by emitting a periodic square wave synchronization signal. The frame rate of this synchronization signal is generally set to not less than 200fps to ensure the efficiency and accuracy of image acquisition and avoid signal loss or distortion caused by low frame rate. After completing image acquisition, it is first necessary to extract the part containing skin pixels from the acquired image to obtain the skin area. The accuracy of this step directly affects the quality of subsequent signal extraction, so efficient image processing technology must be used. After successfully extracting the skin area, the present embodiment obtains a G channel image sequence with stronger pulsation, and extracts the final PPG signal by analyzing the G channel image sequence. The PPG signal reflects the changes in blood flow and is closely related to the activity of the heart. It is widely used to monitor physiological parameters such as heart rate and blood oxygen saturation. Through this series of precise image processing and signal extraction steps, the PPG signal provides reliable and stable data support for physiological monitoring. This technology has high accuracy and robustness, can meet a variety of medical monitoring needs, and provide important technical support in health management and disease early warning.

[0027] It can be seen that the present embodiment adopts a camera-based SCG signal and PPG signal acquisition method. The SCG signal is based on the principle of optical interference and can accurately capture the tiny vibrations caused by the mechanical activity of the heart, while the PPG signal relies on the principle of optical reflection to sensitively monitor the dynamic changes of blood flow. This acquisition method breaks through the limitations of traditional ECG triggering technology in pathological conditions such as arrhythmia, as well as the interference and patient discomfort that may be caused by contact monitoring in an MRI environment. By fusing SCG signals and PPG signals, the system can provide more comprehensive and accurate physiological information, provide solid data support for high-quality MRI imaging, and promote the development of cardiac imaging in clinical diagnosis and disease screening, which has important clinical significance and application prospects.

[0028] It should be noted that although this embodiment mainly focuses on the fusion of SCG signals and PPG signals, it is possible to consider further integrating other physiological signals to form a more comprehensive multimodal signal monitoring system to provide richer physiological information and enhance the accuracy and robustness of MRI trigger control. With the continuous advancement of sensor technology, cameras or other optical sensors with higher precision and higher frame rates can also be used. For example, physiological signal acquisition equipment based on electromagnetic induction, micro-electromechanical systems or other non-optical principles can be developed to replace the current SCG signal and PPG signal acquisition equipment to further improve the quality and real-time performance of signal acquisition, thereby improving the performance of the entire system.

[0029] In addition, although the principle of optical interference is the main method of SCG signal acquisition at present, other high-precision vibration detection technologies can also be used, such as contact vibration detection based on piezoelectric sensors (without affecting the patient's comfort), or the use of ultrasonic technology to detect tiny chest movements, as a supplement or alternative to SCG signal acquisition. In addition to the principle of optical reflection, PPG signal acquisition methods based on the principle of optical transmission can also be explored, especially for transmitted light detection in parts such as fingers or earlobes to obtain more stable pulse wave signals. In addition, combined with wearable optoelectronic devices, such as smart bracelets or finger rings, continuous monitoring and transmission of PPG signals can be achieved, providing a more flexible signal source for MRI triggering.

[0030] Step S200: pre-process the SCG signal and the PPG signal, determine the periodic changes of the SCG signal and the PPG signal, and calibrate the trigger point of each cardiac cycle.

[0031] Preprocessing is an important step in data analysis, especially when it comes to cardiac motion signals. The quality of preprocessing directly affects the accuracy and reliability of subsequent analysis. The main purpose of preprocessing is to remove unnecessary noise and trends and enhance the characteristics of effective signals by filtering, detrending and normalizing SCG and PPG signals, thereby providing clean and standardized data for subsequent analysis.

[0032] During the signal acquisition process, it is usually interfered by high-frequency and low-frequency noise. High-frequency noise may come from electrical interference of the device or rapid changes in the environment, while low-frequency noise may be caused by reasons such as device vibration or baseline drift. In order to improve the quality of the signal, this embodiment performs filtering on the collected SCG signal and PPG signal to remove high-frequency noise and / or low-frequency noise in the SCG signal and PPG signal. Bandpass filtering is a commonly used preprocessing method. The bandpass filter sets a frequency range , only retain the signal components within the frequency range and remove interference from other frequencies. Specifically, the input signal After Fourier transformation, it will be converted to the frequency domain and compared with the frequency response of the bandpass filter Multiply and filter out components other than the target frequency. The signal after inverse Fourier transform It is the signal that has been filtered, that is, the redundant frequency components have been removed: (1) This processing can effectively suppress the noise components in the signal and retain useful physiological signals.

[0033] In many physiological signals, especially those generated by physiological phenomena (such as cardiac movement), long-term linear and / or nonlinear trends may exist. These trends do not represent valid information of the target signal, but may be caused by equipment deviations, environmental factors or other external interference. In order to make the signal more stable and not affected by these trends, this embodiment performs detrending processing on the SCG signal and the PPG signal to remove the linear trend or nonlinear trend in the SCG signal and the PPG signal to achieve a purer signal. Specifically, this embodiment can represent the trend component by fitting a linear or nonlinear function, and subtract these trend components from the original signal. Assume that the signal obtained after the above filtering processing is Contains trending components (linear trend), then the detrended signal for: (2) represents the slope of a linear trend, that is, the rate at which the trend changes over time. is positive, indicating that the signal increases with time; if is negative, indicating that the signal decreases with time; if A value of zero indicates that the signal has no linear trend. It represents the intercept of the linear trend, that is, the value of the trend component when t=0. It represents the value of the trend component at the starting point of time. If the signal has a nonlinear trend, a high-order polynomial fit or a more complex model can be used to ensure the stability and accuracy of the signal. The purpose of detrending is to remove those irrelevant components and only retain the parts related to the target signal.

[0034] Normalization is to map the amplitude of SCG and PPG signals to a unified standard range, usually [0,1], to facilitate subsequent signal analysis. In particular, in tasks such as multimodal signal fusion and feature extraction, the amplitudes of different signals may vary greatly. Normalization helps to eliminate the amplitude differences between signals from different sources, so that the signals can be compared and processed under the same standard. Assume that the signal is the signal after filtering and detrending, and its minimum value is , the maximum value is , then the normalized signal It can be expressed as: (3) After preprocessing the SCG signal and the PPG signal, this embodiment further analyzes the periodic changes of the SCG signal and the PPG signal to calibrate the trigger point of each cardiac cycle, thereby analyzing the optimal triggering time of each cardiac cycle and realizing more intelligent and adaptive MRI trigger control.

[0035] In one implementation, this embodiment performs real-time peak detection on SCG signals and PPG signals to capture periodic changes in SCG signals and PPG signals. Real-time peak detection is a key technology used to analyze periodic fluctuations in continuous signals, especially when processing dynamic signals. In this process, sliding time window processing and peak detection work closely together to ensure that the system can accurately capture and feedback the periodic changes of signals in real time. In particular, in MR real-time triggering systems based on SCG signals and PPG signals, this method is crucial to improving diagnostic accuracy and image quality.

[0036] Specifically, the sliding time window is the first step in the process, and its main purpose is to simulate the display of real-time signals. By dividing the long-term continuous signal into multiple small segments, the sliding time window can gradually process each fixed-length data subset in the SCG signal and PPG signal. In each sliding process, the window processes the current time point t and its left boundary The signal segment between The sliding window is used to detect the signal in the window of the SCG signal and perform preprocessing, smoothing and peak detection on this section of the signal. In this way, the sliding window simulates the continuous display and real-time processing of the signal. In dynamic signal analysis, the length of the sliding time window is crucial to the real-time and accuracy of detection. If the window is too short, it may over-respond to the instantaneous fluctuations in the SCG signal and the PPG signal, while if the window is too long, it may cause delays in detection. Choosing an appropriate sliding window length can ensure that the periodic fluctuations of the SCG signal and the PPG signal can be captured and fed back in a timely and accurate manner.

[0037] Preferably, the present embodiment can also perform signal smoothing when performing real-time peak detection, the main purpose of which is to further remove high-frequency noise in the signal, improve the denoising accuracy, and retain the main trend and periodic fluctuations of the signal. In practical applications, the smoothing method of the present embodiment is local polynomial fitting, especially the Savitzky-Golay filter. The filter effectively eliminates high-frequency noise and sudden fluctuations by performing local polynomial fitting on the signal, while retaining the periodic characteristics of the signal. Specifically, the Savitzky-Golay filter achieves smoothing by performing polynomial fitting on the signal within a local window. Suppose we have an original signal The filter generates a smoothed signal by fitting a polynomial in each sliding window of the signal. , in order to replace the local fluctuations in the original signal. Smoothing filtering can be expressed by polynomial fitting as: (4) in, is the signal value after smoothing, is the value of the i+kth position in the original signal, is the polynomial coefficient obtained by least squares fitting, and M is the window size of the filter, that is, the neighborhood length used for fitting in signal processing. Through this local polynomial fitting, the Savitzky-Golay filter can effectively remove short-term sudden fluctuations and noise while maintaining the main trend of the signal. Especially when processing high-frequency noise and rapidly changing signals, the fitted polynomial can better maintain the smoothness of the signal.

[0038] The core purpose of peak detection is to accurately extract effective peaks from SCG and PPG signals to provide reliable data for further analysis and real-time feedback. Peaks are usually local maxima that reflect significant changes in the signal, and peak detection ensures that the identified peaks have practical significance by screening and judging the signal. The process usually includes three key steps: finding local maxima, setting height thresholds, and minimum peak spacing to ensure the accuracy of peak identification.

[0039] Specifically, firstly, the local maximum values ​​in the SCG signal and the PPG signal are determined to obtain the potential peak positions of the SCG signal and the PPG signal. The point n satisfies the following conditions: and (5) That is, the local maximum Greater than its neighbor and In this way, the core features of the peak can be identified by means of local maximum values, thus providing preliminary peak candidate points for subsequent analysis.

[0040] Next, set a height threshold and use it to filter the local maximum values ​​to obtain the filtered local maximum values. Relying solely on local maximum detection may cause some noise or small fluctuations to be mistakenly identified as peaks. Therefore, set a height threshold This height threshold is used to filter out the peaks with smaller amplitudes in the SCG and PPG signals, and only retain the peaks with practical significance. A peak is considered valid only when the following conditions are met: (6) For example, setting the height threshold to , only when the local maximum value is greater than 0.5, the point corresponding to the local maximum value will be identified as a peak. This process can effectively eliminate noise and small fluctuations and improve the reliability of peak detection.

[0041] Finally, the minimum peak spacing is determined, and the local maximum values ​​after screening are analyzed based on the minimum peak spacing to obtain the real-time peaks in the SCG signal and PPG signal and determine the heart cycle. In order to prevent the peaks from being too close and misjudged as the same peak, a minimum peak spacing needs to be introduced. This parameter specifies the minimum distance between adjacent peaks, thereby preventing multiple peaks from being misidentified as the same peak. Specifically, if the time positions of two peaks are and , then the spacing between them The following conditions must be met: (7) In order to improve the accuracy of peak detection, this embodiment can calculate the time interval between two adjacent peaks based on historical signals (such as the signal in the previous sliding time window) and dynamically adjust the minimum spacing between adjacent peaks. By calculating the interval between two peaks in the historical signal, a reasonable reference can be provided for the current peak detection, and then the minimum peak interval can be adaptively adjusted according to the individual's heart rate changes. Analyzing the local maximum values ​​after screening can not only avoid misjudging the peaks as the same peak, but also improve the detection sensitivity and ensure the accurate identification of valid peaks. This method not only effectively eliminates noise and invalid fluctuations, but also adapts to changes under different physiological states, providing reliable data support for real-time signal processing and feedback.

[0042] As the time window slides, the new signal segments of the SCG signal and the PPG signal will be smoothed and then peak detected to ensure that the signals in each time period can be analyzed in a timely and accurate manner, and the real-time peaks in the SCG-PPG signal are obtained, that is, the periodic changes of the SCG signal and the PPG signal are captured, so the heart cycle can be determined. Overall, the close cooperation of the sliding time window, signal smoothing, and peak detection can not only display and analyze the periodic fluctuations in the signal in real time, but also ensure that the system responds quickly and the analysis is accurate. In the MRI real-time trigger system based on SCG-PPG signals, this method effectively improves the image quality and the real-time nature of diagnosis, making signal analysis more efficient and reliable.

[0043] It should be noted that, in the peak detection link, in addition to the existing sliding time window, signal smoothing and local maximum judgment methods, in other implementations, this embodiment can also introduce wavelet transform technology to perform multi-scale analysis on the signal to more accurately identify the peak features in the signal. At the same time, combined with an adaptive filter, the filter parameters are dynamically adjusted according to the real-time changes of the signal to further improve the accuracy and noise resistance of peak detection.

[0044] In cardiac imaging, motion artifacts caused by cardiac motion can significantly affect the quality of MRI images, so accurate trigger control technology is crucial. After capturing the periodic changes of SCG signals and PPG signals, this embodiment can extract key physiological events of the cardiac cycle (such as systolic and diastolic phases and blood flow fluctuation characteristics) from SCG signals and PPG signals, and then use threshold settings to calibrate trigger points. For example, it can identify whether the signal value of the key physiological event exceeds the threshold. If it exceeds the threshold, the trigger point can be calibrated, which can provide an accurate start time for MRI scanning and reduce the interference of motion artifacts.

[0045] In other implementations, in addition to the trigger point calibration method based on peak detection of this embodiment, this embodiment can also explore a trigger point prediction model based on deep learning, use a large amount of physiological signal data to train the neural network, and directly predict the optimal trigger time for each cardiac cycle, so as to achieve more intelligent and adaptive MRI trigger control. In addition, in another implementation, this embodiment can also design a distributed signal processing architecture to disperse the signal processing tasks of this embodiment to multiple small processors or sensor nodes, each node is responsible for processing local signals, and then summarizes the results through wireless communication and other methods, so as to achieve distributed detection and decision-making of trigger points. In addition, this embodiment can also use a prediction algorithm based on the patient's physiological model and historical data to predict the phase changes of the cardiac cycle in advance, so as to determine the MRI triggering time. This method does not directly rely on the peak detection of real-time signals, but realizes trigger control in a model-driven manner.

[0046] Step S300: establishing a mathematical model for real-time calculation of the optimal MRI triggering timing based on the triggering point of each cardiac cycle, and adjusting the optimal MRI triggering timing in real time.

[0047] To further optimize the trigger timing, the system introduces dynamic modeling methods (such as Kalman filtering) to construct a mathematical model for real-time calculation of the optimal MRI trigger timing. This mathematical model uses the time series of real-time monitored SCG signals and PPG signals as input, combined with physiological state information (such as heart rate, body position, and breathing pattern, etc.) to dynamically adjust the optimal MRI trigger timing. Specifically, Kalman filtering can achieve time synchronization with MRI scans by updating and predicting heart activity status in real time, thereby effectively improving image quality and diagnostic accuracy. As a dynamic optimization algorithm, Kalman filtering achieves precise time synchronization through the following steps: ① Prior Estimate: Before receiving the measurement data of the current time step, the current heart state is predicted based on the previous heart activity state and control input. Specifically, the prior estimate Through the state transition model and the posterior estimate of the previous time step , we can calculate: (8) in, is the state transition matrix, which describes how the state is transferred from one time step to the next; is the posterior estimate of the previous time step; is the control input matrix; is the control input.

[0048] ② Covariance Prediction: At the same time, predict the covariance matrix of the prior estimate , reflecting the uncertainty of the prior estimate: (9) in, is the covariance matrix of the posterior estimate at the previous time step; is the process noise covariance matrix, which represents the uncertainty of the system model.

[0049] ③Measurement: Get the measurement value from the physiological signal at the current time step These measurements reflect key physiological events of the current cardiac cycle.

[0050] ④ Kalman Gain: Calculate Kalman Gain , which is used to weigh the reliability of prior estimates and measured data: (10) in, is the measurement matrix, which describes how states are mapped to measurement data; is the measurement noise covariance matrix, which represents the uncertainty of the measurement data.

[0051] ⑤ Current State Estimate: The Kalman gain is used to combine the prior estimate with the measured value to calculate the current state estimate. .

[0052] (11) ⑥Posterior Covariance Update: Update the covariance matrix of the posterior estimate , reflecting the uncertainty of the current state estimate: (12) in, is the identity matrix.

[0053] In cardiac imaging, Kalman filtering combined with MRI trigger control technology plays a key role, aiming to reduce motion artifacts by accurately calculating the trigger timing and ensure the high quality and stability of MRI images. In the face of the dynamic changes of the cardiac cycle and the interference of physiological factors such as breathing and body position changes, this embodiment uses the adaptive adjustment mechanism of Kalman filtering to optimize the trigger timing according to the patient's physiological state in real time, correct the trigger point, and maintain the high quality of image acquisition. Kalman filtering can recursively estimate the dynamic changes of physiological signals, compensate for system noise and uncertainty, accurately predict and adjust the trigger timing, and reduce image distortion. Therefore, this embodiment adopts dynamic modeling methods such as Kalman filtering to track and adjust the optimal MRI trigger timing in real time, consider the interaction of multiple factors such as respiratory cycle and body position changes, accurately respond to changes, and ensure that MRI scans are accurate and reliable. In this embodiment, the MRI trigger control and trigger point calibration steps work seamlessly, continuously monitor and optimize real-time physiological state data, ensure that images are acquired at specific phases of the cardiac cycle, and minimize the impact of motion artifacts. This technology not only improves the adaptability of the trigger control system and promotes the progress of cardiac imaging research and application, but also provides reliable cardiac imaging data for clinical practice and helps formulate personalized treatment plans.

[0054] In summary, the present embodiment gives priority to the camera-based SCG-PPG signal acquisition method. The SCG signal is based on the principle of optical interference and can accurately capture the tiny vibrations caused by the mechanical activity of the heart, while the PPG signal relies on the principle of optical reflection to sensitively monitor the dynamic changes of blood flow. This acquisition method breaks through the limitations of traditional ECG triggering technology in pathological conditions such as arrhythmia, as well as the interference and patient discomfort that may be caused by contact monitoring in an MRI environment. Of course, in other implementations, the present embodiment may also adopt a contact SCG-PPG signal acquisition method, which is not limited in the present embodiment. In addition, by fusing the SCG signal and the PPG signal, the system in the present embodiment can provide more comprehensive and accurate physiological information, provide solid data support for high-quality MRI imaging, and promote the development of cardiac imaging in clinical diagnosis and disease screening, which has important clinical significance and application prospects. In addition, the real-time peak detection technology in the present embodiment can accurately capture the periodic changes of the signal and accurately calibrate the trigger point of the cardiac cycle. This embodiment establishes a mathematical model based on these trigger points, integrates individual differences, and uses an optimization algorithm to adjust the MRI trigger timing in real time, flexibly adapting to the dynamic changes of the cardiac cycle and the interference of various physiological factors such as breathing and body position changes. This mechanism effectively reduces motion artifacts, improves the stability of image quality, provides more accurate and reliable cardiac images for clinical diagnosis, helps the scientific formulation of personalized treatment plans, optimizes the intelligence and precision level of medical decision-making, and significantly improves the clinical value and application effect of MRI imaging.

[0055] Based on the above embodiments, the present invention also provides an MRI real-time triggering system based on SCG-PPG signals, and the system is used to implement the steps of the above MRI real-time triggering method based on SCG-PPG signals, such as Figure 4 As shown in , the system includes: an SCG-PPG signal acquisition module 10, a trigger point calibration module 20 and an MRI trigger timing optimization module 30. Specifically, the SCG-PPG signal acquisition module 10 is used to capture the periodic movement of the heart to obtain an SCG signal, and to detect the dynamic changes in blood flow to obtain a PPG signal. The trigger point calibration module 20 is used to pre-process the SCG signal and the PPG signal, determine the periodic changes of the SCG signal and the PPG signal, and calibrate the trigger point of each cardiac cycle. The MRI trigger timing optimization module 30 is used to establish a mathematical model for real-time calculation of the optimal MRI trigger timing based on the trigger point of each cardiac cycle, and to adjust the optimal MRI trigger timing in real time.

[0056] The working principles of each module in the MRI real-time triggering system based on SCG signals and PPG signals in this embodiment are the same as the principles of each step in the above method embodiment, and will not be repeated here.

[0057] Each module in the above-mentioned MRI real-time triggering system based on SCG signals and PPG signals can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the terminal in the form of hardware, or can be stored in the memory in the terminal in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0058] Based on the above embodiment, the present invention further provides a terminal, the principle block diagram of the terminal can be as follows: Figure 5 The terminal may include one or more processors 100 ( Figure 5 Only one is shown in the figure), a memory 101 and a computer program 102 stored in the memory 101 and executable on one or more processors 100. For example, an MRI real-time trigger program based on SCG signals and PPG signals. When one or more processors 100 execute the computer program 102, the various steps in the embodiment of the MRI real-time trigger method based on SCG signals and PPG signals can be implemented. Alternatively, when one or more processors 100 execute the computer program 102, the functions of each module / unit in the embodiment of the MRI real-time trigger system based on SCG signals and PPG signals can be implemented, which is not limited here.

[0059] In one embodiment, the processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0060] In one embodiment, the memory 101 may be an internal storage unit of an electronic device, such as a hard disk or memory of the electronic device. The memory 101 may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Further, the memory 101 may also include both an internal storage unit of the electronic device and an external storage device. The memory 101 is used to store computer programs and other programs and data required by the terminal. The memory 101 may also be used to temporarily store data that has been output or is to be output.

[0061] Those skilled in the art will understand that Figure 5 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the terminal to which the scheme of the present invention is applied. The specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0062] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, operating database or other media used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double operational data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A real-time triggering method for MRI based on SCG-PPG signal, characterized in that: The method comprises: Capture the periodic movement of the heart to obtain SCG signals, and detect the dynamic changes in blood flow to obtain PPG signals; Preprocessing the SCG signal and the PPG signal, determining the periodic changes of the SCG signal and the PPG signal, and calibrating the trigger point of each cardiac cycle; A mathematical model for real-time calculation of the optimal MRI triggering timing is established based on the triggering point of each cardiac cycle, and the optimal MRI triggering timing is adjusted in real time.

2. The MRI real-time triggering method based on SCG-PPG signal according to claim 1, characterized in that: The capturing of the periodic motion of the heart to obtain the SCG signal includes: Position the defocus camera at the fourth rib in the lower left part of the chest cavity and irradiate with the laser system; Adjusting the configuration of the defocus camera to collect a speckle image; The optical flow method is used to deeply analyze the collected speckle images, calculate the motion amplitude of the speckle in the X-axis and Y-axis directions, and obtain the motion amplitude sequence; Dividing the motion amplitude sequence to determine the motion angle of each segment, wherein the motion angle reflects the direction of heart vibration; Based on the motion angle of each segment, the SCG signal is obtained.

3. The MRI real-time triggering method based on SCG-PPG signal according to claim 2, characterized in that: The adjusting the configuration of the defocus camera comprises: The defocused camera is adjusted so that the distance between the focal plane and the chest plane is greater than the distance between the lens and the focal plane.

4. The MRI real-time triggering method based on SCG-PPG signal according to claim 1, characterized in that: Detect dynamic changes in blood flow and obtain PPG signals, including: The optical sensor covers the key parts of the face, emits a periodic square wave synchronization signal, and collects images; Extracting a portion containing skin pixels from the acquired image to obtain a skin area; Based on the skin area, a G channel image sequence is determined, and the PPG signal is extracted from the G channel image sequence.

5. The MRI real-time triggering method based on SCG-PPG signal according to claim 1, characterized in that: The preprocessing includes: filtering processing, detrending processing and normalization processing; wherein the filtering processing is used to remove high-frequency noise and / or low-frequency noise in the SCG signal and the PPG signal; the detrending processing is used to remove linear trends and / or nonlinear trends in the SCG signal and the PPG signal; the normalization processing is used to map the amplitudes of the SCG signal and the PPG signal into a unified standard range.

6. The MRI real-time triggering method based on SCG-PPG signal according to claim 1, characterized in that: The determining of the periodic changes of the SCG signal and the PPG signal comprises: Performing real-time peak detection processing on the SCG signal and the PPG signal to capture periodic changes of the SCG signal and the PPG signal; The real-time peak detection process includes: sliding time window processing and peak detection, wherein the sliding time window processing includes: dividing the long-term continuous SCG signal and PPG signal into multiple small segments, and gradually processing each fixed-length data subset in the SCG signal and the PPG signal; The peak detection includes: respectively determining the local maximum values ​​in the SCG signal and the PPG signal to obtain the potential peak positions of the SCG signal and the PPG signal; obtaining a preset height threshold, and using the height threshold to filter the local maximum values ​​to obtain the filtered local maximum values; determining the minimum peak spacing, and analyzing the filtered local maximum values ​​based on the minimum peak spacing to obtain the real-time peaks in the SCG signal and the PPG signal and determine the cardiac cycle.

7. The MRI real-time triggering method based on SCG-PPG signal according to claim 1, characterized in that: Real-time adjustment of the optimal MRI triggering time, including: Physiological status information is obtained, and the constructed mathematical model is combined with the physiological status information to adjust the optimal MRI triggering timing in real time.

8. A real-time MRI triggering system based on SCG-PPG signals, characterized in that: The system is used to implement the steps of the MRI real-time triggering method based on SCG-PPG signals as described in any one of claims 1 to 7, and the system comprises: SCG-PPG signal acquisition module, used to capture the periodic movement of the heart to obtain SCG signals, and detect the dynamic changes of blood flow to obtain PPG signals; A trigger point calibration module, used for preprocessing the SCG signal and the PPG signal, determining the periodic changes of the SCG signal and the PPG signal, and calibrating the trigger point of each cardiac cycle; The MRI trigger timing optimization module is used to establish a mathematical model for real-time calculation of the optimal MRI trigger timing based on the trigger point of each cardiac cycle, and to adjust the optimal MRI trigger timing in real time.

9. A terminal, characterized in that: The terminal includes a memory, a processor, and an MRI real-time trigger program based on SCG-PPG signals stored in the memory and executable on the processor. When the processor executes the MRI real-time trigger program based on SCG-PPG signals, the steps of the MRI real-time trigger method based on SCG-PPG signals as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an MRI real-time trigger program based on SCG-PPG signals. When the MRI real-time trigger program based on SCG-PPG signals is executed by the processor, the steps of the MRI real-time trigger method based on SCG-PPG signals as described in any one of claims 1 to 7 are implemented.

Images