Head and neck flexible magnetic resonance wireless coil multichannel signal collaborative acquisition system

Abstract

The invention relates to the technical field of electrical variable measurement, in particular to a head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition system, which comprises the following steps of: determining a deformation deviation abnormal index based on the bending degree of a head and neck contour corresponding to a target coil and a dynamic distribution relationship between the target coil and an adjacent coil; determining a dynamic deformation rate coefficient and a deformation instability feature representing deformation stability based on the real-time offset feature of the target coil, and obtaining a dynamic deformation feature vector in combination with a deformation deviation anomaly index; further determining a comprehensive risk coefficient of the target coil, and determining respective subitem weights of a coupling matrix and a noise covariance matrix between coil channels based on the comprehensive risk coefficient; and based on the subitem weight, constructing a coupled noise joint optimization objective function, and obtaining an acquisition optimization parameter to optimize a signal acquisition process. According to the technical scheme of the invention, the signal-to-noise ratio of the channel is kept balanced in real time, so that the overall imaging uniformity and spatial resolution of the head and neck are improved.

Description

Technical Field

[0001] This invention relates to the field of electrical variable measurement technology, specifically to a multi-channel signal collaborative acquisition system for a flexible magnetic resonance wireless coil in the head and neck region. Background Technology

[0002] Traditional magnetic resonance (MRI) coils are often designed with rigid molds, making them unsuitable for irregular human structures and curved joints, especially limiting their application in head and neck imaging. Newer flexible MRI wireless coils are better suited for human structures. For MRI coils, the radio frequency (RF) receiving coil is a key component determining the quality of MRI imaging. Signal-to-noise ratio (SNR) is a crucial metric for evaluating the quality of an RF coil. Improving the image SNR can be achieved by increasing the number of channels in the coil; the number of channels is closely related to SNR, scan time, and scan range. However, as the number of channels in a flexible coil increases, the synchronization, coordinated transmission, and data consistency management of multi-channel signals become more complex. Therefore, a collaborative acquisition scheme for multi-channel signals from flexible coils is needed.

[0003] In flexible magnetic resonance wireless coil systems for the head and neck region, channels typically operate in a high-density array close to the area under test. The RF matching state, resonant characteristics, and workload of a single channel can exert a significant electromagnetic coupling effect on neighboring channels. Although systems generally reduce inter-channel interactions through methods such as geometric decoupling, capacitor network decoupling, or preamplifier decoupling, in scenarios like the head and neck region with complex structural curvature, large surface undulations, and significant influence from positional changes, coil deformation can disrupt the preset decoupling conditions, resulting in unavoidable residual coupling. This residual coupling causes the resonant shift, load changes, or wireless transmission state of one channel to simultaneously interfere with the sensitivity and noise levels of neighboring channels, leading to an abnormally increased correlation between channel output signals and limiting the collaborative acquisition of multi-channel signals and subsequent parallel imaging performance. Summary of the Invention

[0004] To address the technical problem of reduced imaging performance in the head and neck region due to coupling interference during multi-channel signal acquisition by flexible magnetic resonance wireless coils, this invention aims to provide a multi-channel signal collaborative acquisition system for flexible magnetic resonance wireless coils in the head and neck region. The specific technical solution adopted is as follows: This invention provides a multi-channel signal collaborative acquisition system for flexible magnetic resonance wireless coils in the head and neck region, the system comprising: The deformation analysis module is used to determine the deformation deviation anomaly index, which characterizes the degree of deformation interference with the coil coupling, based on the curvature of the head and neck contour corresponding to the target coil and its dynamic distribution relationship with adjacent coils. The dynamic deformation rate coefficient and deformation instability characteristics characterizing deformation stability are determined based on the real-time offset characteristics of the target coil, and the dynamic deformation feature vector is obtained by combining the deformation deviation anomaly index. The comprehensive risk coefficient characterizing the deformation tolerance is determined based on the dynamic deformation eigenvector of the target coil, and the sub-item weights of the coupling matrix and noise covariance matrix between coil channels are determined based on the comprehensive risk coefficient. The optimization parameter-finding module is used to construct a joint optimization objective function based on the sub-item weights and obtain the acquisition optimization parameters, which are used to optimize the signal acquisition process.

[0005] Further, the degree of curvature of the target coil corresponding to the head and neck contour is determined, including: Import the target coil and the head and neck contour point cloud data into the same coordinate system to determine the local point cloud of the head and neck contour corresponding to the target coil. The mean Gaussian curvature of the local point cloud of the head and neck contour is used as the index for judging the abnormality of the head and neck surface contour, which characterizes the degree of curvature.

[0006] Furthermore, determining the dynamic distribution relationship between the target coil and its adjacent coils includes: Determine the initial and offset distances between the target coil and its adjacent coils at the initial position and the current offset feature position, respectively, in the same coordinate system; Based on the initial spacing and the spacing after offset, as well as their respective discreteness, a coil distribution anomaly determination index is determined to characterize the current distribution relationship between the target coil and its adjacent coils.

[0007] Furthermore, the determination of the deformation deviation anomaly index, which characterizes the degree of deformation-induced coupling interference with the coil, based on the curvature of the target coil corresponding to the head and neck contour and its dynamic distribution relationship with adjacent coils, includes: By combining the curvature of the head and neck contour corresponding to the target coil and the dynamic distribution relationship between the target coil and its adjacent coils, the strict judgment coefficient of coil anomaly, which characterizes the tolerance of coil displacement, is determined. By combining the current offset characteristics of the target coil and the strict determination coefficient of coil anomaly, a deformation deviation anomaly index is obtained, which characterizes the degree of coupling interference of deformation on the coil.

[0008] Furthermore, the dynamic deformation rate coefficient is determined based on the real-time offset characteristics of the target coil, including: Determine the angular deviation rate and stretching rate corresponding to the current deviation angle and stretching amount of the target coil, respectively. By combining the angular deviation rate and the stretching rate, the dynamic deformation rate coefficient of the target coil is obtained.

[0009] Furthermore, based on the real-time offset characteristics of the target coil, deformation instability characteristics characterizing deformation stability are determined, including: The deviation angle change curve and the stretching amount change curve are determined based on the real-time deviation angle and stretching amount of the target coil, respectively. Determine the average length of the first monotonic interval in the deviation angle change curve and the average length of the second monotonic interval in the stretching amount change curve at all times before the current time. The deformation instability characteristics that characterize deformation stability are obtained by utilizing the dispersion of the mean length of the first monotonic interval and the mean length of the second monotonic interval.

[0010] Furthermore, the determination of the comprehensive risk coefficient characterizing the deformation tolerance based on the dynamic deformation feature vector of the target coil includes: Determine the clinical need scores for multiple contour anatomical regions corresponding to the head and neck contour point cloud data and the contour anatomical regions corresponding to the target coil. By using the dynamic deformation feature vector of the target coil and the clinical need score, the comprehensive risk coefficient of the target coil's characterization to deformation tolerance is determined; Among them, the clinical needs score characterizes the degree of coupling interference between coil channels caused by signal attenuation due to coil deformation.

[0011] Furthermore, the determination of the sub-item weights of the coupling matrix and noise covariance matrix between coil channels based on the comprehensive risk coefficient includes: Determine the risk percentage of the overall risk coefficient of the target coil channel relative to the overall risk coefficient of all coil channels; The risk ratio is used to determine the coupling sub-item weight in the coupling matrix between coil channels that corresponds to the target coil channel; The noise sub-item weights corresponding to the target coil channel in the noise covariance matrix are obtained from the coupling sub-item weights.

[0012] Furthermore, the step of constructing a joint optimization objective function based on sub-item weights and obtaining the collected optimization parameters includes: The decoupled network parameters are used as optimization variables of the coupling matrix, and the coupling optimization sub-items of the coupling matrix are constructed by combining the initial coupling matrix and the current coupling matrix; The low-noise amplifier gain is used as the optimization variable of the noise covariance matrix, and the noise optimization sub-term of the noise covariance matrix is ​​constructed by combining the initial noise covariance matrix and the current noise covariance matrix. A joint optimization objective function for coupling and noise is constructed based on the weights of the coupling sub-items and the coupling optimization sub-items, as well as the weights of the noise sub-items and the noise optimization sub-items. The collected optimization parameters are obtained when the optimization objective function converges.

[0013] Furthermore, based on the weights of the coupling sub-items and the coupling optimization sub-items, as well as the weights of the noise sub-items and the noise optimization sub-items, a joint optimization objective function for coupling and noise is constructed, including: The prediction penalty factor is obtained by combining the dynamic deformation rate coefficient and deformation instability characteristics of the target coil. Based on the coupling sub-item weights and coupling optimization sub-items, as well as the noise sub-item weights and noise optimization sub-items, and combined with the prediction penalty factor, a joint optimization objective function of coupling and noise is constructed.

[0014] The present invention has the following beneficial effects: Based on the proposed head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition system, the following steps are taken: First, a baseline system for collaborative acquisition is constructed, and pre-acquisition deployment and basic calibration are performed. The dynamic characteristics of the current user are evaluated, and combined with the user's head and neck contour, dynamic input information required for optimized acquisition is extracted, and the dynamic deformation feature vector of each sub-coil is obtained. The relationship between coil deformation risk levels is analyzed, a joint optimization objective function for coupling noise is constructed, and optimized acquisition parameters are output. The signal acquisition process is optimized using these acquisition optimization parameters. This invention adapts to individualized head and neck anatomical differences, predicts coupling offset and noise degradation trends in real time based on an electromagnetic mutual inductance model, and automatically adjusts the decoupling matrix and channel gain through a joint optimization strategy. This ensures that the multi-channel array, susceptible to disturbances under high field strength, maintains a stable operating point, reduces signal crosstalk caused by dynamic coupling, suppresses noise power increases due to deformation, and maintains channel SNR (Signal-to-Noise Ratio) balance in real time, thereby improving the overall imaging uniformity and spatial resolution of the head and neck. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of a head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition system according to an embodiment of the present invention; Figure 2 A detailed flowchart of step S1 in a multi-channel signal collaborative acquisition system for flexible magnetic resonance wireless coils in the head and neck, provided as an embodiment of the present invention; Figure 3 A detailed flowchart of step S2 in a multi-channel signal collaborative acquisition system for flexible magnetic resonance wireless coils in the head and neck, provided as an embodiment of the present invention; Figure 4 A detailed flowchart of step S3 in a multi-channel signal collaborative acquisition system for flexible magnetic resonance wireless coils in the head and neck, provided as an embodiment of the present invention; Figure 5 A detailed flowchart of step S4 in a multi-channel signal collaborative acquisition system for flexible magnetic resonance wireless coils in the head and neck, provided as an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware operating environment of the head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition device involved in the embodiments of the present invention; Figure 7 This is a schematic diagram of the framework structure of the head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition system involved in the embodiments of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] It should be noted that, for ease of calculation, all indicator data involved in the calculation in this embodiment of the invention have undergone data preprocessing to eliminate the influence of dimensions. The specific methods for eliminating the influence of dimensions are well known to those skilled in the art and are not limited here.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of a multi-channel signal collaborative acquisition system for flexible magnetic resonance wireless coils in the head and neck region provided by this invention.

[0021] Example 1: For the head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition system provided by this invention, please refer to [link to relevant documentation]. Figure 7 , Figure 7 This is a schematic diagram of the framework structure of the head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition system involved in the embodiments of the present invention.

[0022] The head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition system (hereinafter referred to as the "multi-channel signal collaborative acquisition system") includes: The deformation analysis module A10 is used to determine the deformation deviation anomaly index, which characterizes the degree of deformation interference to the coil coupling, based on the curvature of the head and neck contour corresponding to the target coil and its dynamic distribution relationship with adjacent coils. The dynamic deformation rate coefficient and deformation instability characteristics characterizing deformation stability are determined based on the real-time offset characteristics of the target coil, and the dynamic deformation feature vector is obtained by combining the deformation deviation anomaly index. The comprehensive risk coefficient characterizing the deformation tolerance is determined based on the dynamic deformation eigenvector of the target coil, and the sub-item weights of the coupling matrix and noise covariance matrix between coil channels are determined based on the comprehensive risk coefficient. The optimization parameter calculation module A20 is used to construct the joint optimization objective function of coupled noise based on the sub-item weights and obtain the acquisition optimization parameters, which are used to optimize the signal acquisition process.

[0023] Please see Figure 1 , Figure 1 The flowchart of the steps corresponding to the head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition system provided in one embodiment of the present invention is shown.

[0024] The various method steps corresponding to the head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition system include: Step S1: Based on the curvature of the head and neck contour corresponding to the target coil and its dynamic distribution relationship with adjacent coils, determine the deformation deviation anomaly index, which characterizes the degree of deformation interference with coil coupling. Before proceeding with the following embodiments, in one embodiment, it is necessary to construct a benchmark system for collaborative data acquisition and perform pre-acquisition deployment and basic calibration: The head and neck contours exhibit complex features such as a prominent forehead, temporal depressions, and cervical curvature. The initial fit of the flexible coil directly determines the initial coupling level: if the gap is too large, it will result in higher signal attenuation under high field strength, while also increasing initial coupling differences; furthermore, the resonant frequency of the coil is easily affected by environmental factors under high field strength, necessitating initial calibration to lock in the reference parameters. In addition, inconsistent initial gains in multi-channel LNAs (Low Noise Amplifiers) will amplify subsequent noise differences, requiring pre-calibration to unify the reference.

[0025] First, the multi-channel flexible coils are arranged in a regionalized manner according to the anatomical characteristics of the head and neck, so that each sub-coil is fully in contact with the skin surface. Then, through simple posture adjustment, the coil plane is aligned with the direction of the main magnetic field, thereby reducing the initial coupling deviation caused by the degree of contact and coil deformation. Establish a wireless coupling link between the host and the coil, start the energy transmission and data communication modules, and initialize the operating frequency band and power supply status to ensure that the radio frequency front end of each (coil) channel enters the acquisition preparation state under stable power consumption conditions. The host sends a reference radio frequency calibration signal, which is received synchronously by each channel and used to estimate and obtain the initial coupling matrix, characterizing the static electromagnetic interaction between the channels. Then, the background noise was collected without external excitation, the initial noise covariance matrix was constructed, and the initial gain of each low-noise amplifier was recorded to establish a unified benchmark for subsequent dynamic adjustment of decoupling and noise reduction. Several miniature, non-magnetically-interference structured light sensors (the number of which is sufficient to cover the entire head and neck region) are integrated at the edge of the flexible coil array to collect the current user's head and neck contour data. The initial coupling matrix, initial noise covariance matrix, low-noise amplifier reference gain, and head and neck contour data are stored in the system database and used as input for subsequent multi-channel collaborative optimization algorithms to support individualized real-time decoupling and noise suppression.

[0026] The above data format is explained as follows: (1) The initial multi-channel coupling matrix is ​​an N×N matrix, where N represents the number of coil channels, and the element in the i-th row and j-th column represents the coupling coefficient of channel i to channel j; (2) The noise covariance matrix is ​​also an N×N matrix, where the element in the i-th row and j-th column represents the noise covariance between channel i and channel j, and the element in the i-th row and i-th column represents the noise variance of channel i; (3) The amplifier reference gain is a 1-dimensional vector of length N, and each element is the initial gain of the corresponding channel; (4) The head and neck contour data is point cloud data, i.e., head and neck contour point cloud data. The head and neck contour point cloud data is acquired by an FBG grating sensor or a depth camera, and the corresponding acquisition device is also configured in the overall system.

[0027] Based on the above embodiments, in this embodiment, it is necessary to evaluate the dynamic characteristics of the current user and analyze them in conjunction with the user's head and neck contour to extract the dynamic input information required for optimized acquisition.

[0028] During head and neck examinations, user swallowing or slight head movements can affect the skin in the throat and neck areas, causing dynamic changes in the spacing and bending angle between flexible sub-coils. Especially under high field strength, the coupling coefficient gradually increases as the sub-coil spacing decreases. Simultaneously, coil deformation deteriorates the RF matching state, increasing the LNA's input noise figure (NF), creating a chain reaction of deformation, enhanced coupling, and noise amplification. If these changes cannot be captured in real time, optimized parameters will become disconnected from the actual situation, leading to the failure of decoupling and noise reduction.

[0029] First, it is necessary to extract the abnormal regions of the sub-coil during data acquisition by analyzing the user's motion characteristics at different times. These abnormal regions are those where the spatial state of the sub-coil deviates significantly from the head and neck contour features and normal physiological deformation patterns of the anatomical region after deformation. In actual multi-channel coil acquisition, the coupling noise interaction patterns differ in different regions. Directly applying a general optimization strategy cannot provide targeted adjustments for coils in different regions. Therefore, further analysis of the regional abnormal noise interaction characteristics of different coils is required.

[0030] For areas with large curvature in the head and neck region, the coil needs to be precisely fitted to maintain stable coupling. Even slight deformation will disrupt the fit, so the judgment of abnormal characteristics is strict. In contrast, areas with small curvature in the head and neck region have a gentle contour and a large tolerance for coil deformation, so the judgment of abnormalities can be relatively lenient. Meanwhile, in high-density arrays, in areas where the initial spacing between adjacent coils is small, the deformation of one coil can be rapidly propagated to surrounding coils, causing a chain reaction coupling problem, requiring strict judgment; in areas where the coil spacing is large, the impact range of a single coil deformation is limited, requiring lenient judgment. In summary, this allows for the analysis of the deformation deviation anomaly index of sub-coils at various time points.

[0031] Specifically, in one embodiment, determining the degree of curvature of the target coil corresponding to the head and neck contour includes: Import the target coil and the head and neck contour point cloud data into the same coordinate system to determine the local point cloud of the head and neck contour corresponding to the target coil. The mean Gaussian curvature of the local point cloud of the head and neck contour is used as the index for judging the abnormality of the head and neck surface contour, which characterizes the degree of curvature.

[0032] In this embodiment, a coordinate system is established with the tip of the user's nose as the origin, and the position of the coil and the point cloud data of the user's head and neck are imported into this coordinate system; In the initial state (i.e., the initial state when the coil has not deformed or displaced), the geometric center point of each sub-coil in the same coordinate system is taken as the initial position feature point. The skin contour point (head and neck) corresponding to the initial position feature point is found by the point cloud nearest neighbor matching algorithm, and its initial coordinates are recorded. Compared to the initial state, this is combined with the deviation angle of each sub-coil at time t (as the current time). (As the direction of deformation, it can be normalized to facilitate subsequent data processing, such as normalization of maximum and minimum values) and tensile amount. (As a deformation offset, it can be normalized for easier subsequent data processing, such as maximum and minimum value normalization.) Calculate the position feature point of the sub-coil at time t, which can be recorded as the current position feature point; for the deviation angle... and stretching amount For details on how to obtain it, please refer to the relevant descriptions in the following embodiments.

[0033] Calculate the mean Gaussian curvature (absolute value is acceptable) of each sub-coil, such as the target coil (representing any coil), at its initial position corresponding to each point in the local point cloud of the head and neck contour. After normalization (e.g., normalization to maximum and minimum values), this value is used as the anomaly detection index for the head and neck surface contour, denoted as [index missing]. ;Should The larger the value, the greater the curvature of the head and neck contour corresponding to the initial position of the target coil, and the more stringent the abnormal judgment of its deformation deviation should be. It should be noted that the head and neck surface profile anomaly judgment index is a parameter that represents the sensitivity of the target coil's position to anomalies caused by coil movement. Therefore, it is sufficient to calculate the surface profile anomaly judgment index only in the initial state.

[0034] Specifically, please refer to Figure 2 In one embodiment, determining the dynamic distribution relationship between the target coil and its adjacent coils includes: Step S11: Determine the initial distance and offset distance between the target coil and its adjacent coils in the same coordinate system at the initial position and the current offset feature position, respectively. Step S12: Based on the initial spacing and the spacing after offset, as well as their respective discreteness, determine the coil distribution anomaly judgment index, which characterizes the current distribution relationship between the target coil and its adjacent coils.

[0035] In this embodiment, the offset characteristics of the coil will be described in detail first: An FBG (Fiber Bragg Grating) fiber strain array is placed in the coil substrate, and the deviation angle of each sub-coil at time t is collected through this strain array. and stretching amount Both are used as offset features of the coil; Obtain the initial geometric centerline of each sub-coil along the long side of the coil (the coil shape can be rectangular or similar to a rectangle) in the initial state. The direction of this geometric center axis should ensure that it is parallel to the contour tangent of the corresponding area of ​​the head and neck during the initial fit; The angle between the current geometric centerline and the initial geometric centerline at time t during the acquisition process is taken as the deviation angle at time t, denoted as . This angle, which is the normal curvature of the head and neck surface, is one of the parameters characterizing the change in the fit between the sub-coil and the head and neck. The larger the value, the larger the gap between the coil and the skin, and the more significant the fluctuation of the coupling coefficient.

[0036] Define the direction of each sub-coil along the tangent of the head and neck contour as the bonding direction, and obtain the reference length of the sub-coil in the bonding direction without stretching or compression in the initial bonding state; Calculate the difference between the actual length of the sub-coil along the bonding direction at time t and the reference length, and use this value as the stretching amount at time t, denoted as . A positive stretch indicates that the coil is stretched, while a negative stretch indicates that it is compressed, directly reflecting the change in the distance between the sub-coil and the adjacent coil.

[0037] After obtaining the offset characteristics of different sub-coils at different times, the abnormal characteristics of different sub-coils are analyzed.

[0038] For each sub-coil, such as the target coil, find its neighboring coils in the initial point cloud and calculate the initial spacing between the sub-coil and other neighboring coils at the initial position. (i represents the i-th adjacent sub-coil, the same below) and the standard deviation of the spacing ; Obtain the coordinates of the position feature points of the target coil and its adjacent coils at time t, and obtain the distance between the target coil and the position feature point coordinates (current offset feature position) of its adjacent coils at time t. , which is recorded as the offset spacing; Calculate the absolute value of the difference between the offset distance between the target coil and each adjacent coil at time t and the initial distance. The larger the absolute value of this spacing difference, the greater the difference in position change between the i-th adjacent coil and the target coil at time t, and the easier it is to generate coupling interference. Calculate the distributed interference characteristics of the coil : ; In the above formula, n represents the number of adjacent coils of the target coil, which is obtained through a normalized exponential function. Will As a weight, the positional features of coils that show significant positional changes at time t are amplified; the closer the initial positions of adjacent coils are to the corresponding target coil ( The smaller the value, the more regular the positional distribution ( The smaller the value, the more pronounced the coupling interference caused by coil misalignment. The larger the value, the greater the normalization; norm indicates normalization, such as normalization of maximum and minimum values.

[0039] calculate The standard deviation, after normalization, is denoted as . The larger the normalized standard deviation value, the more significant the coupling interference between the coils is, indicating that the displacement of the target coil at time t is different from the displacement of other coils during the measurement process. In other words, the more chaotic the relative position changes, the more obvious the coupling interference between the coils. Will and The product of these products, normalized (e.g., by maximum / minimum normalization), is used as the coil distribution anomaly determination index for the target coil at time t, denoted as... It can be used to characterize the current distribution relationship between the target coil and its adjacent coils.

[0040] Specifically, in one embodiment, step S1 includes: By combining the curvature of the head and neck contour corresponding to the target coil and the dynamic distribution relationship between the target coil and its adjacent coils, the strict judgment coefficient of coil anomaly, which characterizes the tolerance of coil displacement, is determined. By combining the current offset characteristics of the target coil and the strict determination coefficient of coil anomaly, a deformation deviation anomaly index is obtained, which characterizes the degree of coupling interference of deformation on the coil.

[0041] Based on the above embodiments, in this embodiment, the abnormality determination index of the head and neck surface contour of the target coil is calculated. Coil Distribution Anomaly Judgment Index The product of these factors is used as the strict determination coefficient for coil anomalies of the target coil at time t. The larger this value, the more stringent the criteria should be for determining abnormal displacement of the current coil at time t. Combined with strict judgment coefficient for coil abnormalities Calculate the deformation deviation anomaly index of the target coil at time t. In the above formula, The hyperbolic tangent function, tanh, is positive, and its purpose is to place the calculation result between (0,1); β is a preset gain coefficient (e.g., 10), used to stretch the value to the tanh sensitive range. The larger the value, the more obvious the abnormal interference that the current coil's offset and deformation (which can be collectively referred to as deformation) may cause to the signal at time t.

[0042] Step S2: Determine the dynamic deformation rate coefficient and deformation instability characteristics that characterize deformation stability based on the real-time offset characteristics of the target coil, and obtain the dynamic deformation feature vector by combining the deformation deviation anomaly index. In this embodiment, it is necessary to further expand the extraction of dynamic deformation features of each coil to provide dynamic data support for optimization and obtain the dynamic deformation feature vector of each sub-coil.

[0043] Furthermore, the variation in coupling noise of the head and neck coils is a dynamic result of deformation, manifested through static deformation deviation from the anomaly index. While features can represent the magnitude of current risk, they cannot explain the risk's development trend. Therefore, dynamic features are needed to shift the subsequent optimization process from passive response to active adaptation.

[0044] Specifically, in one embodiment, step S2, determining the dynamic deformation rate coefficient based on the real-time offset characteristics of the target coil, includes: Determine the angular deviation rate and stretching rate corresponding to the current deviation angle and stretching amount of the target coil, respectively. By combining the angular deviation rate and the stretching rate, the dynamic deformation rate coefficient of the target coil is obtained.

[0045] In this embodiment, for the target coil, calculation , The first derivatives of are denoted as . , , which represents the angular deviation rate and stretching rate of the coil at time t, and a dynamic deformation rate coefficient is constructed. The larger this value, the faster the angle deviation and stretching rate of the sub-coil change at time t.

[0046] It should be noted that if within a certain period of time If the deformation rate is less than the corresponding deformation rate threshold, it is considered to be in a static state. In the static state, the deformation instability characteristic is directly set to 0, and the monotonic interval calculation is skipped. The deformation rate threshold can be, for example, 0.2, and can be set according to the actual situation; there is no restriction on this.

[0047] Specifically, please refer to Figure 3 In one embodiment, step S2, determining deformation instability characteristics characterizing deformation stability based on the real-time offset characteristics of the target coil, includes: Step S21: Determine the deviation angle change curve and the stretching amount change curve based on the real-time deviation angle and stretching amount of the target coil, respectively. Step S22: Determine the average length of the first monotonic interval in the deviation angle change curve and the average length of the second monotonic interval in the stretching amount change curve at all times before the current time. Step S23: Use the dispersion corresponding to the mean length of the first monotonic interval and the mean length of the second monotonic interval to obtain the deformation instability characteristics that characterize the deformation stability.

[0048] In this embodiment, construct , After obtaining the monotonic intervals of the two curves, calculate the mean length of the monotonic intervals of each curve at each time point (sampling time). (mean length of the first monotonic interval) and (Mean length of the second monotonic interval), obtain the values ​​before time t respectively. and The length data is used to calculate its variance, which is then used as the angular deviation deformation fluctuation. and stretching deformation fluctuation Furthermore, its deformation instability characteristics were constructed. . This indicates the normalization process for maximum and minimum values.

[0049] By selecting the average length of the monotonic interval to match the natural rhythm of head and neck deformation, distortion caused by the fixed window is avoided, further improving the adaptability to deformation modes in different regions and enhancing the anti-interference capability of stability calculation; the stronger the deformation stability, the more stable the dynamic deformation characteristics of the target coil. At this point, the dynamic deformation feature vector of each sub-coil is constructed. .

[0050] Step S3: Determine the comprehensive risk coefficient representing the deformation tolerance based on the dynamic deformation feature vector of the target coil, and determine the sub-item weights of the coupling matrix and noise covariance matrix between coil channels based on the comprehensive risk coefficient. Specifically, in one embodiment, step S3, determining a comprehensive risk coefficient characterizing the deformation tolerance based on the dynamic deformation feature vector of the target coil, includes: Determine the clinical need scores for multiple contour anatomical regions corresponding to the head and neck contour point cloud data and the contour anatomical regions corresponding to the target coil. By using the dynamic deformation feature vector of the target coil and the clinical need score, the comprehensive risk coefficient of the target coil's characterization to deformation tolerance is determined; Among them, the clinical needs score characterizes the degree of coupling interference between coil channels caused by signal attenuation due to coil deformation.

[0051] In this embodiment, the clinical need for coil deformation risk varies in different regions of the head and neck, requiring a combination of dynamic deformation characteristics and anatomical location to classify the risk level. The anatomical location and clinical need characteristics of the head and neck contour point cloud data can be analyzed by training a deep learning model.

[0052] We collected a large amount of three-dimensional contour point cloud data from head and neck MRI (Magnetic Resonance Imaging) users and manually annotated it. We then scored each anatomical sub-region with a clinical need rating ranging from [0,1]. The higher the score, the more important the signal attenuation caused by coil deformation in the current region is for clinical diagnosis. Design a multi-task model: First, anatomically partition the input point cloud (classification task, with the loss function being the cross-entropy function), and then output a clinical need score for each partition (regression task, with the loss function being the MSE function, i.e., the mean squared error function). PointNet++ is used as the backbone network, and its SA (Set Abstraction) module is divided into two levels: the first level captures the global structure and the second level captures local fine features; the probability distribution of different anatomical regions and the corresponding clinical needs scores are output. The data was divided into training and validation sets in a 7:3 ratio. The training set data was input into the network for training, using the AdamW (Adaptive Moment Estimation with Decoupled Weight Decay) optimizer. Early stopping was used to prevent overfitting, and IoU (Intersection over Union) was applied to the partitioned task. The scoring task uses MAE (Mean Absolute Error) as the evaluation metric, and the model is trained after validation on the validation set. After inputting the current user's head and neck contour point cloud data into the trained model, the anatomical partitioning results of the point cloud data and the corresponding clinical needs score for each anatomical partition are obtained. ; Calculate the dynamic deformation eigenvector of the target coil at the current moment. Data in various dimensions ( , , The weighted mean after normalization (such as maximum-minimum normalization) is denoted as... ,in The weight of this dimension is the same as the sum of the weights of the other two dimensions, and the weights of the other two dimensions are the same, for example... The weight is 0.5. , The weights are all 0.25.

[0053] It should be noted that each of the above-mentioned anatomical zones can correspond to multiple sub-coils. For a target coil, its comprehensive risk coefficient (risk level) is calculated based on its corresponding anatomical zone. The larger this value, the lower the tolerance of the current area to deformation, and the more stringent the judgment of its anomalies.

[0054] Specifically, please refer to Figure 4 In one embodiment, step S3, determining the sub-item weights of the coupling matrix and noise covariance matrix between coil channels based on the comprehensive risk coefficient, includes: Step S31: Determine the risk percentage of the target coil channel's overall risk coefficient relative to the overall overall risk coefficient of all coil channels; Step S32: Use the risk ratio to determine the coupling sub-item weight of the coupling sub-item corresponding to the target coil channel in the coupling matrix between coil channels; Step S33: Obtain the noise sub-item weights of the noise sub-items corresponding to the target coil channel in the noise covariance matrix from the coupling sub-item weights of the coupling sub-items.

[0055] In this embodiment, the essence of coupled noise optimization is to adapt the electromagnetic characteristics of the coil system to the dynamic deformation of the head and neck under hardware physical constraints. Therefore, the optimization objective function must simultaneously reflect: (1) the electromagnetic law of coupling between coils (the relationship between coupling coefficient and spacing or angle); (2) the signal processing principle of noise power (the relationship between noise and gain or load); and (3) the individualized differences in deformation risk (comprehensive risk coefficient). Quantified physiological needs and dynamic risks.

[0056] According to the principle of mutual inductance, the coupling coefficient between coils should be inversely proportional to the spacing and positively correlated with the angle. Changes in spacing or angle caused by deformation will cause the coupling coefficient between coils to deviate from the target value. Therefore, the coupling optimization sub-item in the objective function needs to quantify the deviation between the actual coupling coefficient and the target value. The weight of this deviation is determined by the comprehensive risk coefficient. Coils with higher risk require stricter coupling control. Furthermore, the noise power of the coil is related to the LNA gain and the coil load resistance; high-risk coils require lower noise power. The noise optimization sub-item needs to quantify the deviation between the actual noise power and the target value, with its weight negatively correlated with the overall risk coefficient: high-risk coils emphasize coupling, while low-risk coils can have their noise levels moderately relaxed to improve signal strength. Calculate the coupling sub-item weight of the target coil channel j (in this embodiment, the coil and coil channel can have a one-to-one correspondence) in the coupling matrix. Where N represents the number of coil channels, j specifically refers to the j-th coil channel, and k represents the k-th coil channel, which can be used as an index for the coil channels. This represents the overall comprehensive risk coefficient for all coil channels. This represents the overall risk coefficient of target coil channel j. The larger the value, the greater the proportion of risk and the more tightly coupled the relationship; it should be noted that... This represents the parameter tuning factor, a safety value set to prevent the denominator from being 0. Its value can be, for example, 0.01, and its dimensions are the same as... The same applies, and no restrictions are imposed on this.

[0057] Then the noise term weights of the corresponding noise terms in the noise covariance matrix. It should be noted that noise power is proportional to gain and load impedance. In high-risk areas, it is necessary to maintain stable coupling rather than blindly increasing the signal-to-noise ratio.

[0058] In other embodiments of the present invention, it is possible to directly target... Perform maximum and minimum value normalization processing and use it as the weight of the coupled sub-item.

[0059] Step S4: Construct an optimization objective function for coupled noise based on the sub-item weights and obtain the acquisition optimization parameters. The acquisition optimization parameters are used to optimize the signal acquisition process.

[0060] Specifically, please refer to Figure 5 In one embodiment, step S4, which involves constructing a joint optimization objective function for coupled noise based on the sub-item weights and obtaining the collected optimization parameters, includes: Step S41: Use the decoupled network parameters as optimization variables of the coupling matrix, and construct the coupling optimization sub-item of the coupling matrix by combining the initial coupling matrix and the current coupling matrix; Step S42: Use the low-noise amplifier gain as the optimization variable of the noise covariance matrix, and construct the noise optimization sub-item of the noise covariance matrix by combining the initial noise covariance matrix and the current noise covariance matrix; Step S43: Construct a joint optimization objective function for coupling and noise based on the weights of the coupling sub-items and the coupling optimization sub-items, as well as the weights of the noise sub-items and the noise optimization sub-items. When the optimization objective function converges, the collected optimization parameters are obtained.

[0061] More specifically, step S43, which constructs a joint optimization objective function for coupling and noise based on the weights of the coupling sub-items and the coupling optimization sub-items, and the weights of the noise sub-items and the noise optimization sub-items, includes: The prediction penalty factor is obtained by combining the dynamic deformation rate coefficient and deformation instability characteristics of the target coil. Based on the coupling sub-item weights and coupling optimization sub-items, as well as the noise sub-item weights and noise optimization sub-items, and combined with the prediction penalty factor, a joint optimization objective function of coupling and noise is constructed.

[0062] In this embodiment, based on the aforementioned initial coupling matrix, initial noise covariance matrix, and current coupling matrix and current noise covariance matrix at time t, the example of target coil channel j will be used for illustration: Coupling optimization sub-item It can be represented as ,in Represents the measured coupling coefficient at time t (from the current coupling matrix); Represents the target coupling coefficient (from the initial coupling matrix); This represents the adjustable parameters of the decoupling network, which is one of the optimization variables. Here, i still represents the i-th coil channel in the coupling matrix. It should be noted that, Decoupling network parameters (each (General term) regulation, This indicates that, given the decoupling network parameters Under the condition, the actual coupling coefficient after compensation by the decoupling network; This represents the difference between the compensated and expected coupling coefficients; Noise optimization sub-item It can be represented as ,in The noise power of channel j is represented by the diagonal of the current noise covariance matrix at time t. The target noise power is represented by the diagonal of the initial noise covariance matrix; This represents the elements in the noise covariance matrix (i.e., the noise correlation before gain). Let LNA gain of the j-th channel be another optimization variable; It should be noted that, This indicates the noise power after gain adjustment. This indicates the difference between the current noise power after gain adjustment and the expected noise power; This indicates the noise correlation of channels i and j under their respective gains. Excessive noise correlation will destroy the condition number of the parallel imaging solution matrix. In addition, a prediction penalty term based on dynamic deformation needs to be introduced: the deformation rate coefficient. The larger the value, the greater the deformation instability characteristics. The higher the value, the faster the coupling and noise deteriorate. Therefore, it is necessary to add a prediction multiplication to the objective function to avoid the optimization parameters lagging behind the deformation changes.

[0063] Calculate the prediction penalty factor It should be noted that when rapid movement and unstable adhesion occur, the coupling change rate increases sharply, so a greater degree of optimization modulation needs to be applied in advance. It should be noted that, since the value ranges of the coupling optimization sub-item and the noise optimization sub-item are different, in order to conduct a unified analysis, the coupling optimization sub-item and the noise optimization sub-item are standardized respectively. This standardization process can calculate the ratio of the sub-item value to the sub-item's preset benchmark value or theoretical maximum value, thereby ensuring that the two values ​​are on the same order of magnitude and have consistent dimensions, which facilitates the subsequent construction of the coupling and noise joint optimization objective function.

[0064] In summary, the complete coupled-noise joint optimization objective function is... ; Continuously and randomly iterate to decouple network parameters and LNA gain of each channel The process continues until the objective function converges. The convergence condition can be set as needed (convergence of the objective function is an existing technique; a preset number of iterations can be set, such as 200, or the continuous change can be set to be less than a preset threshold, such as 0.01, to achieve the convergence process). The collected and optimized parameters at this point are then output, which are the corresponding decoupling network parameters. and LNA gain .

[0065] After acquiring the optimized acquisition parameters, the parameters are transmitted to the hardware to adjust the decoupling network parameters and LNA gain, further improving the signal acquisition quality and facilitating subsequent medical decision-making.

[0066] After obtaining the optimization parameters, the signal acquisition process is optimized: An electromagnetic coupling link is established between the host and the coil end to synchronously complete wireless power supply and communication initialization. Based on risk level priority, decoupling parameters are achieved through the TDMA (Time Division Multiple Access) protocol. LNA gain Wirelessly distributed to each channel, with high-risk channels occupying priority time slots, and additional checksums added to prevent packet loss; After receiving parameters at the coil end, the wireless drive decoupling network is adjusted. Compensate the coupling coefficient to the target value; synchronously adjust the LNA gain. To balance noise power and correlation, and adapt to the needs of coupled noise collaborative optimization; The MRI (Magnetic Resonance Imaging) host emits radio frequency pulses to excite hydrogen nuclei resonance. Each channel synchronously captures the echo signals, which are then pre-processed and uploaded to the host via a wireless link in a time-division manner to avoid signal conflicts between multiple channels. The host verifies the coupling noise index of the acquired signal in real time and dynamically corrects it by transmitting adjustment commands back via wireless link. and This ensures low-coupling and low-noise collaborative acquisition throughout the entire process.

[0067] This invention adapts to individual differences in head and neck anatomy, predicts coupling offset and noise degradation trends in real time based on an electromagnetic mutual inductance model, and automatically adjusts the decoupling matrix and channel gain through a joint optimization strategy. This enables the multi-channel array, which is susceptible to disturbances under high field strength, to maintain a stable operating point, reduce signal crosstalk caused by dynamic coupling, suppress noise power increase caused by deformation, and maintain channel signal-to-noise ratio balance in real time, thereby improving the overall imaging uniformity and spatial resolution of the head and neck.

[0068] Example 2: This invention also proposes a multi-channel signal collaborative acquisition device for flexible magnetic resonance wireless coils in the head and neck region. The device can be a magnetic resonance imaging device, a head and neck coil device, a computer, a server, or a combination of multiple devices.

[0069] like Figure 6 As shown, Figure 6 This is a schematic diagram of the hardware operating environment of the head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition device involved in the embodiments of the present invention.

[0070] like Figure 6 As shown, the head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to realize communication between these components. The user interface 1003 may include a display or an input unit such as a control panel; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WIFI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk storage device. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001. The memory 1005, as a computer storage medium, may include a head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition program, referred to as the "multi-channel signal collaborative acquisition program".

[0071] Those skilled in the art will understand that Figure 6 The hardware structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0072] Continue to refer to Figure 6 , Figure 6 The memory 1005, which is a computer-readable storage medium, may include an operating device, a user interface module, a network communication module, and a multi-channel signal collaborative acquisition program for flexible magnetic resonance wireless coils in the head and neck region.

[0073] exist Figure 6 In this embodiment, the network communication module is mainly used to connect to the server and can communicate with the server for data; while the processor 1001 can call the head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition program stored in the memory 1005 and execute the steps in the above embodiments.

[0074] Based on the hardware structure of the above-mentioned head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition device, various embodiments of the head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition system of the present invention are implemented.

[0075] Furthermore, the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a multi-channel signal collaborative acquisition program for a flexible magnetic resonance wireless coil in the head and neck region. When executed by a processor, the multi-channel signal collaborative acquisition program for a flexible magnetic resonance wireless coil in the head and neck region implements the steps of the method corresponding to the multi-channel signal collaborative acquisition system for a flexible magnetic resonance wireless coil in the head and neck region as described above.

[0076] The method implemented when the head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition program is executed can be referred to in various embodiments of the head and neck flexible magnetic resonance wireless coil multi-channel signal collaborative acquisition system of the present invention, and will not be repeated here.

[0077] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0079] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] The above description is only a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. All equivalent structural / method transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the scope of protection of the present invention.

Claims

1. A flexible head and neck magnetic resonance wireless coil multi-channel signal coordinated acquisition system, characterized in that, The system comprises: a deformation analysis module, configured to determine a deformation deviation anomaly index representing a degree of coupling interference of deformation on a coil based on a bending degree of a head and neck contour corresponding to the coil and a dynamic distribution relationship with adjacent coils thereof; determine a dynamic deformation rate coefficient and a deformation instability feature representing deformation stability based on real-time offset features of the target coil, and obtain a dynamic deformation feature vector in combination with the deformation deviation anomaly index; determine a comprehensive risk coefficient representing deformation tolerance based on the dynamic deformation feature vector of the target coil, and determine sub-item weights of a coupling matrix and a noise covariance matrix between coil channels based on the comprehensive risk coefficient; an optimization parameter solving module, configured to construct an optimization objective function of coupling noise combination based on the sub-item weights and obtain acquisition optimization parameters, the acquisition optimization parameters being used for optimizing a signal acquisition process.

2. The flexible head and neck magnetic resonance wireless coil multi-channel signal synergic acquisition system of claim 1, wherein, determining the bending degree of the head and neck contour corresponding to the target coil comprises: importing the target coil and the head and neck contour point cloud data into the same coordinate system to determine the head and neck contour local point cloud corresponding to the target coil; taking the mean value of the Gaussian curvature of the head and neck contour local point cloud as a head and neck surface contour anomaly judgment index representing the bending degree.

3. The flexible head and neck magnetic resonance wireless coil multi-channel signal synergic acquisition system of claim 1, wherein, determining the dynamic distribution relationship between the target coil and the adjacent coils thereof comprises: determining the initial distance and the distance after offset between the target coil and the adjacent coils thereof in the initial position and the current offset feature position respectively in the same coordinate system; based on the initial distance and the distance after offset and the respective dispersion degrees, determining a coil distribution anomaly judgment index representing the current distribution relationship between the target coil and the adjacent coils thereof.

4. The flexible head and neck magnetic resonance wireless coil multi-channel signal synergic acquisition system of claim 1, wherein, determining the deformation deviation anomaly index representing the degree of coupling interference of deformation on the coil based on the bending degree of the head and neck contour corresponding to the target coil and the dynamic distribution relationship with the adjacent coils thereof comprises: determining a coil anomaly strict judgment coefficient representing the coil displacement tolerance in combination with the bending degree of the head and neck contour corresponding to the target coil and the dynamic distribution relationship between the target coil and the adjacent coils thereof; obtaining the deformation deviation anomaly index representing the degree of coupling interference of deformation on the coil in combination with the current offset features of the target coil and the coil anomaly strict judgment coefficient.

5. The flexible head and neck magnetic resonance wireless coil multi-channel signal synergic acquisition system of claim 1, wherein, determining the dynamic deformation rate coefficient based on the real-time offset features of the target coil comprises: determining the angle deviation rate and the stretching rate corresponding to the current deviation angle and the stretching amount of the target coil respectively; obtaining the dynamic deformation rate coefficient of the target coil in combination with the angle deviation rate and the stretching rate.

6. The flexible head and neck magnetic resonance wireless coil multi-channel signal synergic acquisition system of claim 1, wherein, determining the deformation instability feature representing deformation stability based on the real-time offset features of the target coil comprises: determining the deviation angle change curve and the stretching amount change curve based on the real-time deviation angle and the stretching amount of the target coil respectively; determining the first monotonic interval length mean value in the deviation angle change curve and the second monotonic interval length mean value in the stretching amount change curve at each time point before the current time point; obtaining the deformation instability feature representing deformation stability by using the dispersion degrees corresponding to the first monotonic interval length mean value and the second monotonic interval length mean value respectively.

7. The flexible head and neck magnetic resonance wireless coil multi-channel signal synergic acquisition system of claim 1, wherein, determining the comprehensive risk coefficient representing deformation tolerance based on the dynamic deformation feature vector of the target coil comprises: Determine the clinical demand score of the contour anatomic partition corresponding to the head and neck contour point cloud data and the contour anatomic partition corresponding to the target coil; Determine the comprehensive risk coefficient of the target coil representing the deformation tolerance by using the dynamic deformation feature vector of the target coil and the clinical demand score. The clinical demand score represents the degree of coupling interference between coil channels caused by signal attenuation due to coil deformation.

8. The flexible head and neck magnetic resonance wireless coil multi-channel signal synergic acquisition system of claim 1, wherein, The method comprises the following steps: Determine the risk proportion of the comprehensive risk coefficient of the target coil channel compared with the overall comprehensive risk coefficient of all coil channels. Determine the coupling sub-item weight of the coupling sub-item corresponding to the target coil channel in the coupling matrix between coil channels by using the risk proportion. Determine the noise sub-item weight of the noise sub-item corresponding to the target coil channel in the noise covariance matrix from the coupling sub-item weight of the coupling sub-item.

9. The flexible head and neck magnetic resonance wireless coil multi-channel signal synergic acquisition system of claim 1, wherein, The method comprises the following steps: Take the decoupling network parameters as the optimization variables of the coupling matrix, and combine the initial coupling matrix and the current coupling matrix to construct the coupling optimization sub-item of the coupling matrix. Take the low-noise amplifier gain as the optimization variable of the noise covariance matrix, and combine the initial noise covariance matrix and the current noise covariance matrix to construct the noise optimization sub-item of the noise covariance matrix. Based on the coupling sub-item weight, the coupling optimization sub-item, the noise sub-item weight, and the noise optimization sub-item, construct the optimization objective function of the coupling noise joint, and obtain the acquisition optimization parameter when the optimization objective function converges.

10. The flexible head and neck magnetic resonance wireless coil multi-channel signal synergic acquisition system of claim 9, wherein, Based on the coupling sub-item weight, the coupling optimization sub-item, the noise sub-item weight, and the noise optimization sub-item, construct the optimization objective function of the coupling noise joint, including: Combine the dynamic deformation rate coefficient of the target coil and the deformation instability feature to obtain a prediction penalty factor, and based on the coupling sub-item weight, the coupling optimization sub-item, the noise sub-item weight, and the noise optimization sub-item, and combining the prediction penalty factor, construct the optimization objective function of the coupling noise joint.

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