WO2023226116A1 - 一种fMRI中射频接收线圈本征时域稳定性评价方法 - Google Patents

一种fMRI中射频接收线圈本征时域稳定性评价方法 Download PDF

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WO2023226116A1
WO2023226116A1 PCT/CN2022/099612 CN2022099612W WO2023226116A1 WO 2023226116 A1 WO2023226116 A1 WO 2023226116A1 CN 2022099612 W CN2022099612 W CN 2022099612W WO 2023226116 A1 WO2023226116 A1 WO 2023226116A1
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time domain
intrinsic
stability
noise
fmri
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French (fr)
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高阳
张孝通
全枝艳
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浙江大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/28—Details of apparatus provided for in groups G01R33/44 - G01R33/64
    • G01R33/32—Excitation or detection systems, e.g. using radio frequency signals
    • G01R33/36—Electrical details, e.g. matching or coupling of the coil to the receiver
    • G01R33/3671—Electrical details, e.g. matching or coupling of the coil to the receiver involving modulation of the quality factor of the RF coil
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution

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  • the invention belongs to the technical field of nuclear magnetic resonance, and specifically relates to a method for evaluating the intrinsic time domain stability of radio frequency receiving coils in fMRI.
  • ultra-high field magnetic resonance imaging due to the increase in magnetic field strength, the image signal-to-noise ratio and the contrast when detecting neuronal activity in functional magnetic resonance imaging (fMRI, functional Magnetic Resonance Imaging) It has also improved accordingly, so ultra-high field magnetic resonance is widely used in sub-millimeter functional imaging.
  • fMRI functional Magnetic Resonance Imaging
  • ultra-high field magnetic resonance imaging as the magnetic field intensity increases, the time-domain noise utilized by the subject's motion also increases, thus greatly weakening the imaging potential that can be achieved under ultra-high fields.
  • functional magnetic resonance imaging has mainly focused on improving data acquisition methods, such as image post-processing and motion correction algorithms, in mitigating temporal noise, but few can fundamentally solve the problem of magnetic field strength-related temporal noise.
  • the time-domain noise related to the magnetic field strength can be attributed to the radio frequency operating frequency that increases with the field strength, which causes a more complex interaction between the electromagnetic field and the subject.
  • the electrodynamic coupling between the imaging subject and the radio frequency coil acts as a dielectric load.
  • the electrodynamic coupling will be disturbed; when the coil and the imaged subject The electrodynamic coupling will reach a steady state only when the distance remains constant.
  • MRI Magnetic Resonance Imaging, Magnetic Resonance Imaging
  • image post-processing algorithms often assume a constant coupling level between the radio frequency receiving coil and the subject, but this is obviously not applicable in fMRI.
  • the reason is that changes within the human brain inevitably occur during functional magnetic resonance scanning. Even if the subject's head is stationary, there will be non-rigid movement of brain tissue, as well as the flow of blood and cerebrospinal fluid, which will interfere with the imaging. The interaction between the test and the RF receiving coil.
  • the purpose of the present invention is to provide a method for evaluating the intrinsic time domain stability of radio frequency receiving coils in fMRI, which is used to solve the problem that the existing technology cannot know the impact of the performance of the radio frequency receiving coil itself on functional magnetic resonance imaging at the sub-millimeter spatial scale. This in turn leads to technical issues where imaging performance remains poor.
  • the present invention provides a method for evaluating the intrinsic time domain stability of radio frequency receiving coils in fMRI, including:
  • fMRI functional magnetic resonance imaging
  • the intrinsic time domain signal-to-noise ratio calculation model is used to calculate the intrinsic time domain stability parameters.
  • the intrinsic time domain stability parameters at least include the intrinsic time domain signal-to-noise ratio, the intrinsic time domain signal-to-noise ratio, and the intrinsic time domain signal-to-noise ratio.
  • the performance of the radiofrequency receiving coil in fMRI applications is evaluated based on the intrinsic time domain stability parameters.
  • the intrinsic time domain imaging data includes sample thermal noise data, coil sensitivity data and Gaussian thermal noise data at each moment during the fMRI time domain imaging process.
  • the intrinsic time domain signal-to-noise ratio calculation model is used to calculate the intrinsic time domain stability parameters, including:
  • the intrinsic time domain noise variance is calculated as follows:
  • the intrinsic time domain signal-to-noise ratio calculation model uses the intrinsic time domain signal-to-noise ratio calculation model to calculate the intrinsic time domain signal-to-noise ratio tSNR * .
  • the calculation formula is as follows:
  • ⁇ t * ′ represents the intrinsic time domain noise standard deviation ⁇ t * ′.
  • the coil sensitivity time domain variance The Gaussian thermal noise variance as well as the The time domain variance of the sample thermal noise is calculated by the following formula:
  • Gaussian thermal noise variance representing coil losses, Represents the transient variance of sample loss thermal noise at any time
  • the thermal noise transient variance of the sample loss at any time is as follows:
  • E represents the electric field
  • ⁇ represents the conductivity
  • r represents the spatial coordinate inside the test sample
  • both E and ⁇ are functions of r.
  • the sensitivity time domain average It is calculated by the average value of the sensitivity S * at any time.
  • the calculation formula of the sensitivity S * at any time is as follows:
  • H x and H y both represent the transverse magnetic field component of the coil at any time
  • i represents the imaginary part of H y
  • ⁇ 0 represents the magnetic permeability
  • the performance of the radiofrequency receiving coil in fMRI applications is evaluated based on intrinsic time domain stability parameters, including:
  • the intrinsic time domain stability of the radio frequency receiving coil in fMRI applications is evaluated according to the intrinsic thermal noise time domain stability ⁇ * and the intrinsic sensitivity time domain stability ⁇ * .
  • the intrinsic time domain stability of the radio frequency receiving coil in fMRI applications is evaluated according to the intrinsic thermal noise time domain stability ⁇ * and the intrinsic sensitivity time domain stability ⁇ * ,include:
  • the ratio of the intrinsic time domain signal-to-noise ratio tSNR * to the intrinsic signal-to-noise ratio SNR * is determined , and determine the conversion efficiency of the two according to the ratio;
  • the present invention provides a device for evaluating intrinsic time domain stability of radio frequency receiving coils in fMRI, including:
  • the parameter acquisition module is used to acquire the intrinsic time domain imaging data generated by the relative displacement of the radio frequency receiving coil and the subject sample in functional magnetic resonance imaging fMRI;
  • a calculation module configured to calculate intrinsic time domain stability parameters using an intrinsic time domain signal-to-noise ratio calculation model based on the intrinsic time domain imaging data.
  • the intrinsic time domain stability parameters at least include intrinsic time domain signal-to-noise ratio calculation models. Noise ratio, intrinsic time domain sensitivity stability and intrinsic time domain thermal noise stability;
  • An evaluation module is used to evaluate the performance of the radio frequency receiving coil in fMRI applications based on the intrinsic time domain stability parameters.
  • the present invention provides a computer device, including a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read Take the computer program and execute the intrinsic time domain stability evaluation method of the radio frequency receiving coil in fMRI as described in any possible design of the first aspect.
  • the present invention provides a computer-readable storage medium. Instructions are stored on the computer-readable storage medium. When the instructions are run on a computer, the invention performs any fMRI radio frequency receiving coil as in the first aspect. Intrinsic time domain stability evaluation method.
  • the present invention provides a computer program product containing instructions.
  • the instructions When the instructions are run on a computer, the computer is caused to execute the radio frequency receiving coil in fMRI as described in any possible design of the first aspect.
  • Intrinsic time domain stability evaluation method When the instructions are run on a computer, the computer is caused to execute the radio frequency receiving coil in fMRI as described in any possible design of the first aspect.
  • Intrinsic time domain stability evaluation method When the instructions are run on a computer, the computer is caused to execute the radio frequency receiving coil in fMRI as described in any possible design of the first aspect.
  • the present invention obtains intrinsic time domain imaging data generated by the relative displacement of the radio frequency receiving coil and the subject sample in functional magnetic resonance imaging fMRI; based on the intrinsic time domain imaging data, the intrinsic time domain signal-to-noise ratio is used
  • the calculation model calculates intrinsic time domain stability parameters; and evaluates the performance of the radio frequency receiving coil in fMRI applications based on the intrinsic time domain stability parameters. That is, the present invention only considers the intrinsic time domain noise caused by the relative motion of the radio frequency receiving coil and the subject, eliminates the physiological noise of the subject sample, and proposes the intrinsic time domain signal noise that is only related to the performance of the radio frequency receiving coil itself.
  • the calculation results of this model can be used to evaluate the performance of the radio frequency receiving coil in fMRI applications, which can be used to guide the design of special radio frequency receiving coils in functional magnetic resonance imaging, improve the quality of the coil, and then improve the functional magnetic resonance imaging. Image quality of resonance imaging.
  • Figure 1 is a flow chart of the intrinsic time domain stability evaluation method of the radio frequency receiving coil in fMRI in this embodiment
  • Figure 2 is a schematic diagram of the conversion curve between the intrinsic signal-to-noise ratio and the intrinsic time-domain signal-to-noise ratio in this embodiment.
  • the concept of intrinsic signal-to-noise ratio SNR * has been proposed.
  • the intrinsic signal-to-noise ratio SNR * it can be based on numerical electromagnetic simulation calculation to determine the upper limit of the image signal-to-noise ratio during actual magnetic resonance scanning; however, the intrinsic signal-to-noise ratio cannot evaluate the dynamic noise caused by the time domain signal, and there is currently no method that can effectively reveal the electromagnetic characteristics of the radio frequency receiving coil and functional magnetic resonance imaging. Based on this method, this embodiment believes that the intrinsic time domain performance should be quantified and can be used to evaluate the contribution of the radio frequency receiving coil to the time domain noise.
  • the intrinsic sensitivity of the magnetic resonance signal and the intrinsic thermal noise level determined by the electrodynamics of the radio frequency receiving coil fluctuate during MRI scanning, this embodiment defines it as intrinsic time domain noise. Due to the shorter radio frequency wavelength of UHF (Ultra High Frequency), the resulting intrinsic time-domain noise may be the main component of the time-domain noise related to the magnetic field strength, thereby deteriorating the imaging performance at the sub-millimeter spatial scale, so , the determination of intrinsic time domain noise is of great significance for improving imaging performance.
  • UHF Ultra High Frequency
  • this embodiment proposes a method for evaluating the intrinsic time domain stability of the radio frequency receiving coil in fMRI, and uses this method to quantitatively analyze the intrinsic time domain noise that is only related to the radio frequency receiving coil itself, so that it can be based on the analysis
  • the results promote improvements in coil structure, thereby improving image imaging performance.
  • the method is illustrated below through examples, specifically as follows:
  • this embodiment provides a method for evaluating the intrinsic time domain stability of radio frequency receiving coils in fMRI, including but not limited to steps S101 to S103:
  • Step S101 In functional magnetic resonance imaging fMRI, obtain the intrinsic time domain imaging data generated by the relative displacement of the radio frequency receiving coil and the subject sample;
  • the subject sample in this embodiment mainly refers to the human head.
  • the brain tissue and other structures inside the human head will inevitably interact with the radio frequency receiving coil. Relative displacement brings dynamic time domain noise.
  • this embodiment quantitatively evaluates the intrinsic time domain stability of the radio frequency receiving coil by acquiring intrinsic time domain imaging data.
  • this embodiment can directly evaluate the intrinsic time domain imaging data of the above-mentioned radio frequency receiving coil in the electromagnetic field simulation design stage, or can also measure the intrinsic time domain imaging data of the radio frequency receiving coil on the magnetic resonance imaging equipment. There are no limitations here.
  • the intrinsic time domain imaging data includes but is not limited to sample thermal noise data, coil sensitivity data and Gaussian thermal noise data.
  • the main component of the intrinsic time domain noise is the fluctuation of the radio frequency receiving coil time domain signal caused by the displacement of the subject sample.
  • the main component of the intrinsic time domain noise begins to become the fluctuation of the thermal noise level of the radio frequency receiving coil caused by the displacement of the subject sample.
  • the sensitivity of the radio frequency receiving coil is determined by the magnetic field distribution of the coil.
  • the fluctuation of the time domain signal of the radio frequency receiving coil is the change of the coil sensitivity, that is, the sensitivity time domain variance described later; and the thermal noise sources of the radio frequency receiving coil include two Part: One part is the thermal noise caused by the conduction current (i.e., electric field distribution) inside the RF receiving coil, and the other part is the thermal noise caused by the conduction current generated by the electromagnetic field of the coil inside the imaging object (i.e., the sample thermal noise determined by the electric field distribution), where, The second part is that the level of the displacement current is determined by the relative position of the radio frequency coil and the subject sample, and is also affected by the displacement of the subject sample during the imaging process.
  • the thermal noise sources of the radio frequency receiving coil include two Part: One part is the thermal noise caused by the conduction current (i.e., electric field distribution) inside the RF receiving coil, and the other part is the thermal noise caused by the conduction current generated by the electromagnetic field of the coil inside the imaging object (i.e., the sample thermal noise determined by the electric field distribution
  • Step S102 According to the intrinsic time domain imaging data, use the intrinsic time domain signal-to-noise ratio calculation model to calculate the intrinsic time domain stability parameters.
  • the intrinsic time domain stability parameters at least include the intrinsic time domain signal to noise ratio. , intrinsic time domain sensitivity stability and intrinsic time domain thermal noise stability;
  • the intrinsic time domain signal-to-noise ratio refers to only considering the relative motion of the radio frequency receiving coil and the subject sample, and is determined by factors such as the sensitivity of the radio frequency receiving coil, thermal noise, and the time domain fluctuations of the two.
  • the time domain signal-to-noise ratio performance parameter is used to evaluate whether a specific radio frequency receiving coil is suitable for time domain acquisition applications such as functional magnetic resonance imaging, that is, the time domain noise caused by the relative motion of the radio frequency receiving coil and the subject sample is also considered. , as well as the sensitivity and thermal noise of the RF receiving coil.
  • step S102 based on the intrinsic time domain imaging data, the intrinsic time domain signal-to-noise ratio calculation model is used to calculate the intrinsic time domain stability parameters, including:
  • Step S1021. Calculate the intrinsic time domain noise variance based on the coil sensitivity data, Gaussian thermal noise data and sample thermal noise data at each moment during the fMRI time domain imaging process. Calculated as follows:
  • the source of Gaussian thermal noise is the loss of the coil itself, the loss of the sample due to the coil, and the change in electric field distribution caused by the displacement of the sample relative to the coil.
  • the coil sensitivity time domain variance The Gaussian thermal noise variance as well as the The time domain variance of the sample thermal noise is calculated by the following formula:
  • Gaussian thermal noise variance representing coil losses, Represents the transient variance of sample loss thermal noise at any time
  • the time domain variance of the Gaussian thermal noise of the coil loss is It can be characterized by equivalent resistance, in which case this parameter is a constant.
  • E represents the electric field
  • ⁇ represents the conductivity
  • r represents the spatial coordinate inside the test sample
  • both E and ⁇ are functions of r.
  • Step S1022. According to the intrinsic time domain noise variance Use the intrinsic time domain signal-to-noise ratio calculation model to calculate the intrinsic time domain signal-to-noise ratio tSNR * .
  • the calculation formula is as follows:
  • ⁇ t * ′ represents the intrinsic time domain noise standard deviation ⁇ t * ′.
  • the sensitivity time domain average value It is calculated by the following formula:
  • H x and H y both represent the transverse magnetic field component of the coil at any time
  • i represents the imaginary part of H y
  • ⁇ 0 represents the magnetic permeability
  • Step S103 Evaluate the performance of the radio frequency receiving coil in fMRI applications based on the intrinsic time domain stability parameters.
  • step S103 the performance of the radio frequency receiving coil in fMRI applications is evaluated based on the intrinsic time domain stability parameters, including:
  • Step S1031. Put formula (1) into formula (2), and obtain the intrinsic time domain signal-to-noise ratio tSNR * through transformation, which is a function of the intrinsic signal-to-noise ratio SNR * .
  • the transformed formula is as follows:
  • Step S1032. Define the intrinsic thermal noise standard deviation as the intrinsic thermal noise time domain stability ⁇ * , and define the intrinsic sensitivity variance as the intrinsic sensitivity time domain stability ⁇ * , then formula (6) It can be converted into the following formula:
  • Step S1033. Evaluate the intrinsic time domain stability of the radio frequency receiving coil in fMRI applications based on the intrinsic thermal noise time domain stability ⁇ * and the intrinsic sensitivity time domain stability ⁇ * .
  • step S1033 the intrinsic time domain stability of the radio frequency receiving coil in the fMRI application is evaluated according to the intrinsic thermal noise time domain stability ⁇ * and the intrinsic sensitivity time domain stability ⁇ * , including:
  • the ratio of the intrinsic time domain signal-to-noise ratio tSNR * to the intrinsic signal-to-noise ratio SNR * is determined , and determine the conversion efficiency of the two according to the ratio;
  • the conversion curve model between the intrinsic signal-to-noise ratio SNR * and the intrinsic time-domain signal-to-noise ratio tSNR * is shown. It can be seen that when the intrinsic thermal noise time-domain stability ⁇ * and / Or when the value of the intrinsic sensitivity time domain stability ⁇ * is zero, it is considered that no intrinsic time domain noise is generated, and the radio frequency receiving coil and the test sample are relatively stationary, as shown in the figure , the slope of the curve is 1 at this time; when the intrinsic thermal noise time domain stability ⁇ * and/or the intrinsic sensitivity time domain stability ⁇ * are different from zero, the intrinsic thermal noise
  • the intrinsic time-domain noise of the radio frequency receiving coil is The larger the intrinsic sensitivity time domain stability ⁇ * is, the smaller the upper limit of the high signal-to-noise ratio area of the image is. At this time, the intrinsic time domain noise of the radio frequency receiving coil is larger.
  • this embodiment obtains intrinsic time domain imaging data generated by the relative displacement of the radio frequency receiving coil and the subject sample in functional magnetic resonance imaging fMRI; based on the intrinsic time domain imaging data, using The intrinsic time domain signal-to-noise ratio calculation model calculates the intrinsic time domain stability parameters; based on the intrinsic time domain stability parameters, the performance of the radio frequency receiving coil in fMRI applications is evaluated. That is, the present invention only considers the intrinsic time domain noise caused by the relative motion of the radio frequency receiving coil and the subject, eliminates the physiological noise of the subject sample, and proposes the intrinsic time domain signal noise that is only related to the performance of the radio frequency receiving coil itself.
  • the calculation results of this model can be used to evaluate the performance of the radio frequency receiving coil in fMRI applications, which can be used to guide the design of special radio frequency receiving coils in functional magnetic resonance imaging, improve the quality of the coil, and then improve the functional magnetic resonance imaging.
  • Image quality of resonance imaging can be used by manufacturers to design new radio frequency receiving coils as one of the evaluation indicators of coil performance.
  • this parameter can also be used by customers to evaluate purchased radio frequency receiving coil products. Third-party testing for daily coil quality assessment.
  • this embodiment provides a device for evaluating intrinsic time domain stability of radio frequency receiving coils in fMRI, including:
  • the parameter acquisition module is used to acquire the intrinsic time domain imaging data generated by the relative displacement of the radio frequency receiving coil and the subject sample in functional magnetic resonance imaging fMRI;
  • a calculation module configured to calculate intrinsic time domain stability parameters using an intrinsic time domain signal-to-noise ratio calculation model based on the intrinsic time domain imaging data.
  • the intrinsic time domain stability parameters at least include intrinsic time domain signal-to-noise ratio calculation models. Noise ratio, intrinsic time domain sensitivity stability and intrinsic time domain thermal noise stability;
  • An evaluation module is used to evaluate the performance of the radio frequency receiving coil in fMRI applications based on the intrinsic time domain stability parameters.
  • the third aspect of this embodiment provides a computer device, including a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read Take the computer program and execute the intrinsic time domain stability evaluation method of the radio frequency receiving coil in fMRI as described in any possible design of the first aspect.
  • the memory may include, but is not limited to, random access memory (Random-Access Memory, RAM), read-only memory (Read-Only Memory, ROM), flash memory (Flash Memory), first-in first-out memory (First Input First Output, FIFO) and/or First Input Last Output, FILO, etc.;
  • the processor may not be limited to microprocessors of the STM32F105 series;
  • the transceiver may be, but is not limited to, WiFi (Wireless Fidelity) wireless transceiver, Bluetooth wireless transceiver, GPRS (General Packet Radio Service, General Packet Radio Service Technology) wireless transceiver and/or ZigBee (Zigbee protocol, a low-power LAN protocol based on the IEEE802.15.4 standard ) wireless transceiver, etc.
  • the computer device may also include, but is not limited to, a power module, a display screen and other necessary components.
  • a fourth aspect of this embodiment provides a computer-readable storage medium. Instructions are stored on the computer-readable storage medium. When the instructions are run on a computer, the instructions are executed as in any possible design of the first aspect. The described method for evaluating the intrinsic time domain stability of radiofrequency receiving coils in fMRI.
  • the computer-readable storage medium refers to a carrier for storing data, which may, but is not limited to, include floppy disks, optical disks, hard disks, flash memory, USB flash drives and/or memory sticks, etc.
  • the computer may be a general-purpose computer, a special-purpose computer, etc. Computer, computer network, or other programmable device.
  • the fifth aspect of this embodiment provides a computer program product containing instructions.
  • the instructions When the instructions are run on a computer, the computer is caused to execute the radio frequency receiving coil in fMRI as described in any possible design of the first aspect.
  • Intrinsic time domain stability evaluation method When the instructions are run on a computer, the computer is caused to execute the radio frequency receiving coil in fMRI as described in any possible design of the first aspect.
  • Intrinsic time domain stability evaluation method is provided.

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Abstract

一种fMRI中射频接收线圈本征时域稳定性评价方法和设备，该评价方法包括：获取射频接收线圈与被试样本的相对位移产生的本征时域成像数据(S101)；根据本征时域成像数据，利用本征时域信噪比计算模型计算本征时域稳定性参数(S102)；基于本征时域稳定性参数对射频接收线圈在fMRI应用中的性能进行评价(S103)。该方法仅考虑由于射频接收线圈与被试样本相对运动带来的本征时域噪声，排除了被试样本的生理噪声，从而得到仅与射频接收线圈本身性能有关的本征时域信噪比计算模型，再通过该模型的计算结果能够对射频接收线圈的时域稳定性进行评价，从而指导功能磁共振成像中的专用射频接收线圈的设计，提高线圈质量，进而提高功能磁共振成像的图像质量。

Description

一种fMRI中射频接收线圈本征时域稳定性评价方法 技术领域
本发明属于核磁共振技术领域，具体涉及fMRI中射频接收线圈本征时域稳定性评价方法。
背景技术
在7T(tesla，特斯拉)超高场磁共振成像系统中，由于磁场强度的提高，图像信噪比以及功能磁共振成像(fMRI，functional Magnetic Resonance Imaging))中检测神经元活动时的对比度也随之提高，因此超高场磁共振被广泛应用于亚毫米功能成像中。然而，在超高场磁共振成像中，随着磁场强度的提高，被试运动利用的时域噪声也随之增强，从而极大削弱了超高场下可能达到的成像潜能。迄今为止，功能磁共振成像在缓解时域噪声方面主要致力于改进数据获取方法，如图像后处理和运动校正算法，但很少能从根本上解决磁场强度相关的时域噪声问题。
其中，与磁场强度相关的时域噪声可以归因于随着场强提升的射频工作频率，该射频工作频率使得电磁场与被试之间产生更复杂的相互作用。具体的，成像被试与射频线圈之间由于电动力学耦合而作为介质负载，当射频接收线圈的空间位置相对于成像被试发生变化时，电动力学耦合会受到干扰；当线圈与被成像被试的距离保持不变时，电动力学耦合才会达到稳态。
现有技术中，MRI(Magnetic Resonance Imaging，磁共振成像)获取序列和图像后处理算法中常假设射频接收线圈和被试之间具有恒定的耦合水平，但这在fMRI中显然不适用。原因在于：功能磁共振扫描中人体大脑内部不可避免地发生变化，即使受试者的头部是静止的，也会有脑组织的非刚性运动，以 及血液和脑脊液等的流动，从而干扰成像被试与射频接收线圈之间的相互作用。尽管有学者研究了射频接收线圈参与影响功能磁共振成像的时域信噪比(tSNR，time SIGNAL NOISE RATIO)，但这些研究将生理噪声和射频接收线圈相关的时域噪声叠加在一起进行考虑，没有单独考虑由于线圈-被试相对运动带来的相关时域噪声，从而无法获知射频接收线圈本身性能对亚毫米空间尺度下的功能磁共振成像的影响，进而导致成像性能仍然不佳。
发明内容
本发明的目的是提供一种fMRI中射频接收线圈本征时域稳定性评价方法，用于解决现有技术中无法获知射频接收线圈本身性能对亚毫米空间尺度下的功能磁共振成像的影响，进而导致成像性能仍然不佳的技术问题。
为了实现上述目的，本发明采用以下技术方案：
第一方面，本发明提供一种fMRI中射频接收线圈本征时域稳定性评价方法，包括：
在功能磁共振成像fMRI中，获取射频接收线圈与被试样本的相对位移产生的本征时域成像数据；
根据所述本征时域成像数据，利用本征时域信噪比计算模型计算本征时域稳定性参数，所述本征时域稳定性参数至少包括本征时域信噪比、本征时域灵敏度稳定性和本征时域热噪声稳定性；
基于所述本征时域稳定性参数对射频接收线圈在fMRI应用中的性能进行评价。
在一种可能的设计中，所述本征时域成像数据包括fMRI时域成像过程中各个时刻的样本热噪声数据、线圈灵敏度数据以及高斯热噪声数据。
在一种可能的设计中，根据所述本征时域成像数据，利用本征时域信噪比计算模型计算本征时域稳定性参数，包括:
根据所述fMRI时域成像过程中各个时刻的线圈灵敏度数据、高斯热噪声数据和样本热噪声数据，计算本征时域噪声方差
Figure PCTCN2022099612-appb-000001
计算公式如下：
Figure PCTCN2022099612-appb-000002
其中，
Figure PCTCN2022099612-appb-000003
表示线圈灵敏度时域方差，
Figure PCTCN2022099612-appb-000004
表示高斯热噪声方差，
Figure PCTCN2022099612-appb-000005
表示样本热噪声时域方差；
根据所述本征时域噪声方差
Figure PCTCN2022099612-appb-000006
利用本征时域信噪比计算模型计算本征时域信噪比tSNR *，计算公式如下：
Figure PCTCN2022099612-appb-000007
其中，
Figure PCTCN2022099612-appb-000008
表示灵敏度时域平均值，σ t *′表示本征时域噪声标准差σ t *′。
在一种可能的设计中，所述线圈灵敏度时域方差
Figure PCTCN2022099612-appb-000009
所述高斯热噪声方差
Figure PCTCN2022099612-appb-000010
以及所述
Figure PCTCN2022099612-appb-000011
样本热噪声时域方差分别通过以下公式计算得到：
Figure PCTCN2022099612-appb-000012
其中，
Figure PCTCN2022099612-appb-000013
表示fMRI时域成像过程中各个时间点的线圈灵敏度；
Figure PCTCN2022099612-appb-000014
其中，
Figure PCTCN2022099612-appb-000015
表示线圈损耗的高斯热噪声方差，
Figure PCTCN2022099612-appb-000016
表示任意时刻样本损耗热噪声瞬态方差；
Figure PCTCN2022099612-appb-000017
其中，
Figure PCTCN2022099612-appb-000018
表示各时刻j的样本损耗热噪声瞬态方差，j表示任意离散时间点，N表示总的时域采样个数。
在一种可能的设计中，所述任意时刻样本损耗热噪声瞬态方差
Figure PCTCN2022099612-appb-000019
的计算公式如下：
Figure PCTCN2022099612-appb-000020
其中，E表示电场，σ表示电导率，r表示被试样本内部的空间坐标，E和σ均为关于r的函数。
在一种可能的设计中，所述灵敏度时域平均值
Figure PCTCN2022099612-appb-000021
通过任意时刻灵敏度S *的均值计算得到，所述任意时刻灵敏度S *的计算公式如下：
Figure PCTCN2022099612-appb-000022
其中，H x和H y均表示任意时刻的线圈横向磁场分量，i表示H y的虚部，μ 0表示磁导率。
在一种可能的设计中，基于本征时域稳定性参数对射频接收线圈在fMRI应用中的性能进行评价，包括：
将公式(1)带入公式(2)，通过变换得到所述本征时域信噪比tSNR *是关于本征信噪比SNR *的函数，变换后的公式如下：
Figure PCTCN2022099612-appb-000023
其中，
Figure PCTCN2022099612-appb-000024
表示本征热噪声标准差，
Figure PCTCN2022099612-appb-000025
表示本征灵敏度方差；
将所述本征热噪声标准差定义为本征热噪声时域稳定性α *，并将所述本征 灵敏度方差定义为本征灵敏度时域稳定性λ *，则公式(6)可转化为如下公式：
Figure PCTCN2022099612-appb-000026
根据所述本征热噪声时域稳定性α *和所述本征灵敏度时域稳定性λ *对射频接收线圈在fMRI应用中的本征时域稳定性进行评价。
在一种可能的设计中，根据所述本征热噪声时域稳定性α *和所述本征灵敏度时域稳定性λ *对射频接收线圈在fMRI应用中的本征时域稳定性进行评价，包括：
根据所述本征热噪声时域稳定性α *和所述本征灵敏度时域稳定性λ *，确定所述本征时域信噪比tSNR *与所述本征信噪比SNR *的比值，并根据比值确定二者的转化效率；其中，
所述本征热噪声时域稳定性α *和/或所述本征灵敏度时域稳定性λ *的值越接近于零，本征信噪比SNR *越容易转化为本征时域信噪比tSNR *，则表明射频接收线圈本征时域噪声越小。
第二方面，本发明提供一种fMRI中射频接收线圈本征时域稳定性评价装置，包括：
参数获取模块，用于在功能磁共振成像fMRI中，获取射频接收线圈与被试样本的相对位移产生的本征时域成像数据；
计算模块，用于根据所述本征时域成像数据，利用本征时域信噪比计算模型计算本征时域稳定性参数，所述本征时域稳定性参数至少包括本征时域信噪比、本征时域灵敏度稳定性和本征时域热噪声稳定性；
评价模块，用于基于所述本征时域稳定性参数对射频接收线圈在fMRI应用 中的性能进行评价。
第三方面，本发明提供一种计算机设备，包括依次通信相连的存储器、处理器和收发器，其中，所述存储器用于存储计算机程序，所述收发器用于收发消息，所述处理器用于读取所述计算机程序，执行如第一方面任意一种可能的设计中所述的fMRI中射频接收线圈本征时域稳定性评价方法。
第四方面，本发明提供一种计算机可读存储介质，所述计算机可读存储介质上存储有指令，当所述指令在计算机上运行时，执行如第一方面任意一种fMRI中射频接收线圈本征时域稳定性评价方法。
第五方面，本发明提供一种包含指令的计算机程序产品，当所述指令在计算机上运行时，使所述计算机执行如第一方面任意一种可能的设计中所述的fMRI中射频接收线圈本征时域稳定性评价方法。
有益效果：
本发明通过在功能磁共振成像fMRI中，获取射频接收线圈与被试样本的相对位移产生的本征时域成像数据；根据所述本征时域成像数据，利用本征时域信噪比计算模型计算本征时域稳定性参数；基于所述本征时域稳定性参数对射频接收线圈在fMRI应用中的性能进行评价。即本发明仅考虑由于射频接收线圈与被试相对运动带来的本征时域噪声，排出了被试样本的生理噪声，提出了仅与射频接收线圈本身性能有关的本征时域信噪比计算模型，通过该模型的计算结果能够对射频接收线圈在fMRI应用中的性能进行评价，从而能够用于指导功能磁共振成像中的专用射频接收线圈的设计，提高线圈质量，进而提高功能磁共振成像的图像质量。
附图说明
图1为本实施例中的fMRI中射频接收线圈本征时域稳定性评价方法的流程图；
图2为本实施例中的本征信噪比和本征时域信噪比之间的转化曲线示意图。
具体实施方式
为使本说明书实施例的目的、技术方案和优点更加清楚，下面将结合本说明书实施例中的附图，对本说明书实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本说明书一部分实施例，而不是全部的实施例。基于本说明书中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
由于射频接收线圈的电磁场决定了磁共振信号本征灵敏度和本征热噪声水平，基于此已有本征信噪比SNR *的概念被提出，通过本征信噪比SNR *可以根据数值电磁仿真计算来确定实际磁共振扫描时图像信噪比的上限；然而，本征信噪比无法评价时域信号带来的动态噪声，且目前尚无能够有效揭示射频接收线圈电磁特性与功能磁共振成像获取中的时域噪声的关系的方法，基于此，本实施例认为本征时域性能应当被量化，并且可以用来评估射频接收线圈对时域噪声的贡献。
此外，由于在MRI扫描中由射频接收线圈的电动力学所决定的磁共振信号本征灵敏度与本征热噪声水平会有波动，本实施例将其定义为本征时域噪声。由于UHF(特高频)的射频波长较短，由此产生的本征时域噪声可能是与磁场强度相关的时域噪声的主要组成部分，从而恶化了亚毫米空间尺度下的成像性能，因此，本征时域噪声的确定对于提高成像性能具有重要意义。
此外，现代MRI系统使用射频接收线圈的相控阵列设计，以实现在更大的视野范围上获得比较高的图像信噪比，同时可实现并行成像技术以提高图像编码效率。但线圈不同单元之间与被试载荷之间存在复杂的电动力学耦合，不可避免地会影响并行成像的灵敏度、热噪声和噪声放大因子(g因子)。这种电动力学耦合的扰动会导致相控阵列单元间相互耦合发生变化，从而使本征时域噪声更加复杂。基于此，本实施例提出了一种fMRI中射频接收线圈本征时域稳定性评价方法，并通过该方法对仅与射频接收线圈本身相关的本征时域噪声进行定量分析，从而可以根据分析结果促进线圈结构改进，进而提高图像成像性能。以下通过实施例对该方法进行说明，具体如下：
实施例
如图1所示，第一方面，本实施例提供一种fMRI中射频接收线圈本征时域稳定性评价方法，包括但不限于由步骤S101～S103实现：
步骤S101.在功能磁共振成像fMRI中，获取射频接收线圈与被试样本的相对位移产生的本征时域成像数据；
其中，需要说明的是，本实施例中的被试样本主要是指人体头部，在功能磁共振成像中，人体头部内部的脑组织等结构将不可避免地与射频接收线圈之间产生相对位移，从而带来动态的时域噪声，基于此，本实施例通过获取本征时域成像数据来对射频接收线圈的本征时域稳定性进行定量评价。
其中，需要说明的是，本实施例可以直接在电磁场仿真设计阶段评估上述射频接收线圈的本征时域成像数据，也可以在磁共振成像设备上测量射频接收线圈的本征时域成像数据，此处不做限定。
优选的，所述本征时域成像数据包括但不限于样本热噪声数据、线圈灵敏 度数据以及高斯热噪声数据。
其中，需要说明的是，对于本征时域噪声，当图像信噪比较高的时候，构成本征时域噪声的主要成分是射频接收线圈时域信号受被试样本位移引起的波动，当图像信噪比较低的时候，构成本征时域噪声的主要成分开始变为射频接收线圈热噪声水平受被试样本位移引起的波动。其中，射频接收线圈灵敏度由线圈的磁场分布决定，射频接收线圈时域信号的波动即为所述线圈灵敏度的变化，即后述的灵敏度时域方差；而射频接收线圈的热噪声来源包括两个部分：一部分是射频接收线圈内部的传导电流(即电场分布)引起的热噪声，另一部分是线圈电磁场在成像物体内部产生传导电流引起的热噪声(即电场分布决定的样本热噪声)，其中，第二部分由于位移电流的水平受到射频线圈和被试样本相对位置所决定，因为也会受到被试样本在成像过程中位移的影响。
步骤S102.根据所述本征时域成像数据，利用本征时域信噪比计算模型计算本征时域稳定性参数，所述本征时域稳定性参数至少包括本征时域信噪比、本征时域灵敏度稳定性和本征时域热噪声稳定性；
其中，需要说明的是，所述本征时域信噪比是指仅考虑射频接收线圈和被试样本相对运动，由射频接收线圈灵敏度、热噪声以及二者的时域波动等因素所决定的时域信噪比性能参数，用于评价特定射频接收线圈是否适用于功能磁共振成像这种时域获取应用中，即同时考虑射频接收线圈和被试样本相对运动带来的时域噪声，以及射频接收线圈的灵敏度和热噪声。
在步骤S102中，根据所述本征时域成像数据，利用本征时域信噪比计算模型计算本征时域稳定性参数，包括:
步骤S1021.根据所述fMRI时域成像过程中各个时刻的线圈灵敏度数据、 高斯热噪声数据和样本热噪声数据，计算本征时域噪声方差
Figure PCTCN2022099612-appb-000027
计算公式如下：
Figure PCTCN2022099612-appb-000028
其中，
Figure PCTCN2022099612-appb-000029
表示线圈灵敏度时域方差，用于衡量由于被试样本和线圈相对位移导致的灵敏度变化，
Figure PCTCN2022099612-appb-000030
表示表示高斯热噪声方差，产生高斯热噪声的根源是线圈本身损耗、样本因线圈导致的损耗以及样本相对线圈位移导致的电场分布变化，
Figure PCTCN2022099612-appb-000031
表示样本热噪声时域方差，根源是由于样本相对线圈位移导致的电场分布变化；
其中，所述线圈灵敏度时域方差
Figure PCTCN2022099612-appb-000032
所述高斯热噪声方差
Figure PCTCN2022099612-appb-000033
以及所述
Figure PCTCN2022099612-appb-000034
样本热噪声时域方差分别通过以下公式计算得到：
Figure PCTCN2022099612-appb-000035
其中，
Figure PCTCN2022099612-appb-000036
表示....；
Figure PCTCN2022099612-appb-000037
其中，
Figure PCTCN2022099612-appb-000038
表示线圈损耗的高斯热噪声方差，
Figure PCTCN2022099612-appb-000039
表示任意时刻样本损耗热噪声瞬态方差；
优选的，对于特定的射频接收线圈来说，线圈损耗的高斯热噪声时域方差
Figure PCTCN2022099612-appb-000040
可以用等效电阻来表征，此时该参数为一常量。
Figure PCTCN2022099612-appb-000041
其中，
Figure PCTCN2022099612-appb-000042
表示各时刻j的样本损耗热噪声瞬态方差，j表示任意离散时间点，N表示总的时域采样个数。
其中，所述任意时刻样本损耗热噪声瞬态方差
Figure PCTCN2022099612-appb-000043
的计算公式如下：
Figure PCTCN2022099612-appb-000044
其中，E表示电场，σ表示电导率，r表示被试样本内部的空间坐标，E和σ均为关于r的函数。
步骤S1022.根据所述本征时域噪声方差
Figure PCTCN2022099612-appb-000045
利用本征时域信噪比计算模型计算本征时域信噪比tSNR *，计算公式如下：
Figure PCTCN2022099612-appb-000046
其中，
Figure PCTCN2022099612-appb-000047
表示灵敏度时域平均值，σ t *′表示本征时域噪声标准偏差σ t *′。
其中，所述灵敏度时域平均值
Figure PCTCN2022099612-appb-000048
通过以下公式计算得到：
Figure PCTCN2022099612-appb-000049
其中，H x和H y均表示任意时刻的线圈横向磁场分量，i表示H y的虚部，μ 0表示磁导率、
步骤S103.基于所述本征时域稳定性参数对射频接收线圈在fMRI应用中的性能进行评价。
在步骤S103中，基于所述本征时域稳定性参数对射频接收线圈在fMRI应用中的性能进行评价，包括：
步骤S1031.将公式(1)带入公式(2)，通过变换得到所述本征时域信噪比tSNR *是关于本征信噪比SNR *的函数，变换后的公式如下：
Figure PCTCN2022099612-appb-000050
其中，
Figure PCTCN2022099612-appb-000051
表示本征热噪声标准差，
Figure PCTCN2022099612-appb-000052
表示本征灵敏度方差；
步骤S1032.将所述本征热噪声标准差定义为本征热噪声时域稳定性α *，并将所述本征灵敏度方差定义为本征灵敏度时域稳定性λ *，则公式(6)可转化为如下公式：
Figure PCTCN2022099612-appb-000053
步骤S1033.根据所述本征热噪声时域稳定性α *和所述本征灵敏度时域稳定性λ *对射频接收线圈在fMRI应用中的本征时域稳定性进行评价。
在步骤S1033中，根据所述本征热噪声时域稳定性α *和所述本征灵敏度时域稳定性λ *对射频接收线圈在fMRI应用中的本征时域稳定性进行评价，包括：
根据所述本征热噪声时域稳定性α *和所述本征灵敏度时域稳定性λ *，确定所述本征时域信噪比tSNR *与所述本征信噪比SNR *的比值，并根据比值确定二者的转化效率；其中，
所述本征热噪声时域稳定性α *和/或所述本征灵敏度时域稳定性λ *的值越接近于零，本征信噪比SNR *越容易转化为本征时域信噪比tSNR *，表明射频接收线圈本征时域噪声越小。
如图2所示，示出了本征信噪比SNR *和本征时域信噪比tSNR *之间的转化曲线模型，可见，当所述本征热噪声时域稳定性α *和/或所述本征灵敏度时域稳定性λ *的值越为零时，则认为未产生任何本征时域噪声，所述射频接收线圈与所述被试样本相对静止，如图中所示，此时曲线的斜率为1；当所述本征热噪声时域稳定性α *和/或所述本征灵敏度时域稳定性λ *的值越不为零时，所述本征热
噪声时域稳定性α *越大，曲线的斜率越小，则本征信噪比SNR *向本征时域信噪比tSNR *转化的效率越低，此时射频接收线圈本征时域噪声越大；当所述本征灵敏度时域稳定性λ *越大，图像高信噪比区域的上限越小，此时射频接收线圈本征时域噪声越大。
基于上述公开的内容，本实施例通过在功能磁共振成像fMRI中，获取射频接收线圈与被试样本的相对位移产生的本征时域成像数据；根据所述本征时域成像数据，利用本征时域信噪比计算模型计算本征时域稳定性参数；基于所述本征时域稳定性参数对射频接收线圈在fMRI应用中的性能进行评价。即本发明仅考虑由于射频接收线圈与被试相对运动带来的本征时域噪声，排出了被试样本的生理噪声，提出了仅与射频接收线圈本身性能有关的本征时域信噪比计算模型，通过该模型的计算结果能够对射频接收线圈在fMRI应用中的性能进行评价，从而能够用于指导功能磁共振成像中的专用射频接收线圈的设计，提高线圈质量，进而提高功能磁共振成像的图像质量。此外，本实施例中的本征时域信噪比可用于厂商设计新的射频接收线圈，作为线圈性能的测评指标之一，同时，该参数还可以用于客户对购买的射频接收线圈产品进行三方检测，用于日常线圈质量评估。
第二方面，本实施例提供一种fMRI中射频接收线圈本征时域稳定性评价装置，包括：
参数获取模块，用于在功能磁共振成像fMRI中，获取射频接收线圈与被试样本的相对位移产生的本征时域成像数据；
计算模块，用于根据所述本征时域成像数据，利用本征时域信噪比计算模型计算本征时域稳定性参数，所述本征时域稳定性参数至少包括本征时域信噪 比、本征时域灵敏度稳定性和本征时域热噪声稳定性；
评价模块，用于基于所述本征时域稳定性参数对射频接收线圈在fMRI应用中的性能进行评价。
本实施例第二方面提供的前述装置的工作过程、工作细节和技术效果，可以参见如上第一方面或第一方面中任意一种可能设计所述的方法，于此不再赘述。
本实施例第三方面提供一种计算机设备，包括依次通信相连的存储器、处理器和收发器，其中，所述存储器用于存储计算机程序，所述收发器用于收发消息，所述处理器用于读取所述计算机程序，执行如第一方面任意一种可能的设计中所述的fMRI中射频接收线圈本征时域稳定性评价方法。
具体举例的，所述存储器可以但不限于包括随机存取存储器(Random-Access Memory，RAM)、只读存储器(Read-Only Memory，ROM)、闪存(Flash Memory)、先进先出存储器(First Input First Output，FIFO)和/或先进后出存储器(First Input Last Output，FILO)等等；所述处理器可以不限于采用型号为STM32F105系列的微处理器；所述收发器可以但不限于为WiFi(无线保真)无线收发器、蓝牙无线收发器、GPRS(General Packet Radio Service，通用分组无线服务技术)无线收发器和/或ZigBee(紫蜂协议，基于IEEE802.15.4标准的低功耗局域网协议)无线收发器等。此外，所述计算机设备还可以但不限于包括有电源模块、显示屏和其它必要的部件。
本实施例第三方面提供的前述计算机设备的工作过程、工作细节和技术效果，可以参见如上第一方面或第一方面中任意一种可能设计所述的方法，于此不再赘述。
本实施例第四方面提供一种计算机可读存储介质，所述计算机可读存储介质上存储有指令，当所述指令在计算机上运行时，执行如第一方面任意一种可能的设计中所述的fMRI中射频接收线圈本征时域稳定性评价方法。
其中，所述计算机可读存储介质是指存储数据的载体，可以但不限于包括软盘、光盘、硬盘、闪存、优盘和/或记忆棒(Memory Stick)等，所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。
本实施例第四方面提供的前述计算机设备的工作过程、工作细节和技术效果，可以参见如上第一方面或第一方面中任意一种可能设计所述的方法，于此不再赘述。
本实施例第五方面提供一种包含指令的计算机程序产品，当所述指令在计算机上运行时，使所述计算机执行如第一方面任意一种可能的设计中所述的fMRI中射频接收线圈本征时域稳定性评价方法。
本实施例第五方面提供的前述计算机可读存储介质的工作过程、工作细节和技术效果，可以参见如上第一方面或第一方面中任意一种可能设计所述的方法，于此不再赘述。
最后应说明的是：以上所述仅为本发明的优选实施例而已，并不用于限制本发明的保护范围。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (8)

  1. 一种fMRI中射频接收线圈本征时域稳定性评价方法，其特征在于，包括：
    在功能磁共振成像fMRI中，获取射频接收线圈与被试样本的相对位移产生的本征时域成像数据；
    根据所述本征时域成像数据，利用本征时域信噪比计算模型计算本征时域稳定性参数，所述本征时域稳定性参数至少包括本征时域信噪比、本征时域灵敏度稳定性和本征时域热噪声稳定性；
    基于所述本征时域稳定性参数对射频接收线圈在fMRI应用中的性能进行评价。
  2. 根据权利要求1所述的fMRI中射频接收线圈本征时域稳定性评价方法，其特征在于，所述本征时域成像数据包括fMRI时域成像过程中各个时刻的线圈灵敏度数据以及热噪声数据。
  3. 根据权利要求2所述的fMRI中射频接收线圈本征时域稳定性评价方法，其特征在于，根据所述本征时域成像数据，利用本征时域信噪比计算模型计算本征时域稳定性参数，包括:
    根据所述fMRI时域成像过程中各个时刻的线圈灵敏度数据、热噪声数据，计算本征时域噪声方差
    Figure PCTCN2022099612-appb-100001
    计算公式如下：
    Figure PCTCN2022099612-appb-100002
    其中，
    Figure PCTCN2022099612-appb-100003
    表示线圈灵敏度时域方差，
    Figure PCTCN2022099612-appb-100004
    表示不会受射频接收线圈与被试样本的相对位移影响的高斯热噪声方差，
    Figure PCTCN2022099612-appb-100005
    表示会受射频接收线圈与被试样本的相对位移影响的样本热噪声时域方差；
    根据所述本征时域噪声方差
    Figure PCTCN2022099612-appb-100006
    利用本征时域信噪比计算模型计算本征时域信噪比tSNR *，计算公式如下：
    Figure PCTCN2022099612-appb-100007
    其中，
    Figure PCTCN2022099612-appb-100008
    表示灵敏度时域平均值，
    Figure PCTCN2022099612-appb-100009
    表示本征时域噪声标准差。
  4. 根据权利要求3所述的fMRI中射频接收线圈本征时域稳定性评价方法，其特征在于，所述线圈灵敏度时域方差
    Figure PCTCN2022099612-appb-100010
    所述高斯热噪声方差
    Figure PCTCN2022099612-appb-100011
    以及所述
    Figure PCTCN2022099612-appb-100012
    样本热噪声时域方差分别通过以下公式计算得到：
    Figure PCTCN2022099612-appb-100013
    其中，
    Figure PCTCN2022099612-appb-100014
    表示fMRI时域成像过程中各个时间点的线圈灵敏度；
    Figure PCTCN2022099612-appb-100015
    其中，
    Figure PCTCN2022099612-appb-100016
    表示线圈损耗的高斯热噪声方差，
    Figure PCTCN2022099612-appb-100017
    表示任意时刻样本损耗热噪声方差；
    Figure PCTCN2022099612-appb-100018
    其中，
    Figure PCTCN2022099612-appb-100019
    表示各时刻j的样本损耗热噪声瞬态方差，j表示任意离散时间点，N表示总的时域采样个数。
  5. 根据权利要求4所述的fMRI中射频接收线圈本征时域稳定性评价方法，其特征在于，所述任意时刻样本损耗热噪声瞬态方差
    Figure PCTCN2022099612-appb-100020
    的计算公式如下：
    Figure PCTCN2022099612-appb-100021
    其中，E表示电场，σ表示电导率，r表示被试样本内部的空间坐标，E和σ均为关于r的函数。
  6. 根据权利要求3所述的fMRI中射频接收线圈本征时域稳定性评价方法，其特征在于，所述灵敏度时域平均值
    Figure PCTCN2022099612-appb-100022
    通过任意时刻灵敏度S *的均值计算得到，所述任意时刻灵敏度S *的计算公式如下：
    Figure PCTCN2022099612-appb-100023
    其中，H x和H y均表示任意时刻的线圈横向磁场分量，i表示H y的虚部，μ 0表示磁导率。
  7. 根据权利要求3所述的fMRI中射频接收线圈本征时域稳定性评价方法，其特征在于，基于本征时域稳定性参数对射频接收线圈在fMRI应用中的性能进行评价，包括：
    将公式(1)带入公式(2)，通过变换得到所述本征时域信噪比tSNR *是关于本征信噪比SNR *的函数，变换后的公式如下：
    Figure PCTCN2022099612-appb-100024
    其中，
    Figure PCTCN2022099612-appb-100025
    表示本征热噪声标准差，
    Figure PCTCN2022099612-appb-100026
    表示本征灵敏度方差；
    将所述本征热噪声标准差定义为本征热噪声时域稳定性α *，并将所述本征灵敏度方差定义为本征灵敏度时域稳定性λ *，则公式(6)可转化为如下公式：
    Figure PCTCN2022099612-appb-100027
    根据所述本征热噪声时域稳定性α *和所述本征灵敏度时域稳定性λ *对射频接收线圈在fMRI应用中的本征时域稳定性进行评价。
  8. 根据权利要求7所述的fMRI中射频接收线圈本征时域稳定性评价方法， 其特征在于，根据所述本征热噪声时域稳定性α *和所述本征灵敏度时域稳定性λ *对射频接收线圈在fMRI应用中的本征时域稳定性进行评价，包括：
    根据所述本征热噪声时域稳定性α *和所述本征灵敏度时域稳定性λ *，确定所述本征时域信噪比tSNR *与所述本征信噪比SNR *的比值，并根据比值确定二者的转化效率；其中，
    所述本征热噪声时域稳定性α *和/或所述本征灵敏度时域稳定性λ *的值越接近于零，本征信噪比SNR *越容易转化为本征时域信噪比tSNR *，则表明射频接收线圈本征时域噪声越小。
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