KR20180114829A - An MRI approach of multipletimes to repeat for detection of neuronal oscillations - Google Patents

Abstract

Disclosed are a method for detecting a neuron resonance signal and an MRI signal processing apparatus which can process MRI neuron resonance data by measuring a size of a neuron resonance signal using a plurality of repetition times (or repetition periods). The method for detecting a neuron resonance signal includes the steps of: acquiring a plurality of different digital sequences respectively corresponding to a plurality of different repetition periods by sampling a magnetic resonance signal of a neuron resonance signal according to each of the plurality of different repetition periods; and calculating a correlation between the plurality of different digital sequences in a frequency band based on the plurality of different digital sequences.

Description

[0001] The present invention relates to a magnetic resonance imaging (MRI)

More particularly, the present invention relates to a technique for processing MRI data of a neuron resonance magnetic resonance image by measuring the magnitude of a neuron resonance signal by using two or more repetition times of the neuron resonance magnetic resonance image will be.

In recent system biology research, a hypothesis has been suggested that communication between brain regions is selectively made from a brain signal as a whole through a selective frequency band filter. The technique of measuring the resonance frequency of neurons for communication between brain regions based on this hypothesis can reveal the mechanism of communication between brain regions which is not well known at present, It can have an effect.

The brain is made up of small areas that hold many functions and these brain areas are structurally connected to each other. Most brain functions, such as behavior, cognition, and perception, are accomplished by reassembling the brain's network quickly and flexibly. Recent studies have shown that the vibrations and synchronization of neurons occurring in specific frequency bands control the flow of information between regions of the brain that are structurally connected and enable flexible and selective communication between brain regions. However, in the dormant and active / stimulated states, it is not clear what mechanism is causing the selective and flexible communication of the brain to a specific frequency band. Moreover, many clinical data suggest that patients with brain diseases such as autism, schizophrenia, epilepsy, dementia and Parkinson's disease have altered the synchronization characteristics of neurons over a broad frequency band and that such abnormal neuronal synchronization is associated with symptoms of the disease , Movement, etc.).

Therefore, by understanding the mechanism of selective inter-brain communication through synchronization, a very important clue can be found in the treatment of biopathology and brain diseases. Furthermore, the development of a new imaging technique that can map the resonance characteristics of neurons to the high resolution for the broadband frequencies used for communication between brain regions requires a tremendous ripple effect on academia, industry, and medicine related to biomedical engineering Lt; / RTI >

On the other hand, functional magnetic resonance imaging (fMRI) indirectly measures the activity of the brain by the interaction of neurons and bloodstream rather than directly measuring the current through neurons. Unlike conventional MRI, fMRI is a technology that acquires images repeatedly during and after external stimuli and maps brain regions correlated with temporal patterns of external stimuli through statistical processing. fMRI is a special imaging technique that can non-invasively map the activity of neurons, but rather indirectly measures blood flow changes by neuron activity rather than directly measuring electrical signals of neurons.

On the other hand, research has shown that fMRI can be performed without external stimulation. This new fMRI technology is called Resting-state fMRI. The basic assumption of resting-state fMRI is that if there is a functional association between the two regions of the brain, temporal MRI signal changes will also correlate. Resting-state fMRI measures functional connectivity between brain regions.

However, existing fMRI techniques, including Resting-state fMRI, indirectly measure neuronal activity through hemodynamic responses in the local area. The hemodynamic response is slow and there is a delay of about 4 seconds. Resting-state fMRIs show functional connectivity between brain regions, but they do not show which mechanisms selectively communicate between brain regions. This is a fundamental limitation of the existing fMRI technique and explains why "resonance of frequency-selective neurons" can not be confirmed by conventional methods.

The method of directly measuring the current of a neuron by MRI has been continuously tried. However, many researchers claim that the MRI signal due to the change in magnetic field generated by the current in the neuron is too small to be consistently localized by MRI in vivo.

There are two major MRI imaging techniques for directly sensing the electrical signals of neurons. One technique is to give a periodic stimulus with a constant time interval and to measure the electrical signal of the neuron by acquiring an MRI signal immediately after each stimulus (ie, before the hemodynamic response occurs). Another technique is to increase the temporal resolution of the MRI image acquisition (≤ 100 ms) and find a component that is synchronized with the frequency of the external stimulus through Fourier transform. Both methods depend on the frequency or frequency of the external stimulus and do not take into account the natural frequency of the neuron. Whether the neurons sense current signals is still controversial for all existing MRI imaging techniques, including the two methods described above.

As a new technique for solving the aforementioned problems, a neuron resonance magnetic resonance imaging method of Korean Patent Registration No. 10-1683217 is disclosed. According to this technique, an oblique magnetic field pattern oscillating in accordance with the resonance frequency of a neuron can be applied to the MRI imaging technique, so that the signal of the neuron can be maximized. In addition, this technique repeatedly performs multiphase image acquisition, extracts components corresponding to the resonance frequency of the neurons through the Fourier analysis method, and has a problem that the MRI signal due to the current of the neuron is very small, And the phase is randomly generated. The above-described technique not only extracts signals of a neuron in a frequency-selective manner by using an iterative multiphase image acquisition method and a Fourier analysis method, but also uses a time-averaging effect to obtain a signal-to-noise ratio, S / N ratio) can be greatly improved. In addition, it is possible to provide a basic technology for completing a communication channel map for each frequency band between brain regions which has not been possible in the past, and for identifying specific frequencies and brain regions related to brain function and brain diseases. However, in order to detect a magnetic field vibration signal, this technique needs to repeat the process of obtaining MRI data by an MRI imaging technique using an oblique magnetic field pattern a plurality of times, and it is necessary to change the relative phase between the oblique magnetic field pattern and the magnetic field vibration signal There are technical limitations in terms of. In addition, this technique has the disadvantage that the neuron resonance signal can be extracted when the neuron resonance signal can be predicted.

The present invention provides a technique for revealing frequency selective inter-brain communication mechanisms based on systematic biological studies on frequency selective inter-brain communication mechanisms. We also provide useful techniques to complete the proof-of-concept test and the frequency-selective brain-to-brain communication map.

The present invention provides a technique for processing data of a neuron resonance magnetic resonance image by measuring the magnitude of a neuron resonance signal using a plurality of repetition times (or a repetition period).

The present invention also provides a method of identifying a frequency component of a neuron resonance signal based on sampling data of a magnetic resonance signal of a neuron resonance signal according to a plurality of repetition times (or a repetition period) even in a state where a neuron resonance signal can not be predicted Technology.

According to an aspect of the present invention, there is provided a method of detecting a neuron resonance signal, comprising: sampling a magnetic resonance signal of the neuron resonance signal according to a plurality of different repetition periods, Obtaining a plurality of different digital sequences corresponding to each repetition period, and calculating a correlation between the plurality of different digital sequences in a frequency band based on the different plurality of digital sequences.

The method for analyzing the neuron resonance signal may further include identifying a frequency component in which the magnitude of the calculated correlation degree is greater than or equal to a predetermined magnitude, as a frequency component of the neuron resonance signal.

In this case, the calculating step may convert each of the plurality of different digital sequences into a frequency band, and may calculate the degree of correlation by superimposing a plurality of different digital sequences in the converted frequency band.

In the calculating, the plurality of different digital sequences may be convoluted, and the convoluted digital sequence may be converted into a frequency band to calculate the degree of correlation.

Further, the calculating step may include setting an arbitrary resonance frequency of the neuron resonance signal, and calculating a phase difference between the predetermined arbitrary resonance frequency, the different repetition period, and the phase difference of each of the plurality of different digital sequences obtained The degree of correlation can be calculated based on the data.

Meanwhile, the method of detecting the neuron resonance signal may further include padding dummy data to have a predetermined sampling period in each of the obtained plurality of different digital sequences.

In this case, the dummy data may be a value obtained by interpolating data included in the digital sequence with respect to 0, a preset constant, or a plurality of different digital sequences.

The plurality of digital sequences may include a first digital sequence generated from a first readout sequence indicating the magnitude of the neuron resonance signal obtained at each lead-out point of the magnetic resonance signal according to a first repetition period, And a second digital sequence generated from a second readout sequence indicating the magnitude of the neuron resonance signal obtained at each readout time point of the magnetic resonance signal according to a second repetition period different from the repetition period.

Also, the first digital sequence is a sequence generated by padding the first dummy data to the first readout sequence so that the sampling period is a predetermined period, and the second digital sequence is set so that the sampling period is the predetermined period And a sequence generated by padding the second dummy data to the second readout sequence.

Wherein the calculating step superimposes the first frequency spectrum and the second frequency spectrum based on the first frequency spectrum of the first digital sequence and the second frequency spectrum of the second digital sequence to calculate the correlation degree can do.

The calculating step may calculate the correlation from the frequency spectrum of the convoluted digital sequence by convoluting the first digital sequence and the second digital sequence and transforming the convoluted digital sequence to a frequency band .

At this time, the predetermined period may be equal to or less than a difference between the first repetition period and the second repetition period.

According to an aspect of the present invention, there is provided an MRI signal processing apparatus including an input unit for receiving a magnetic resonance signal of a neuron resonance signal and a magnetic resonance signal generating unit for generating a magnetic resonance signal of the neuron resonance signal, And a control unit for sampling a signal to obtain a plurality of different digital sequences corresponding to each of the plurality of different repetition periods, wherein the control unit is configured to sample the plurality of different digital sequences in the frequency band based on the different plurality of digital sequences And calculates a correlation between digital sequences.

According to an aspect of the present invention, there is provided a non-transitory computer readable medium on which a program for executing a method of detecting a neuron resonance signal is recorded, Sampling a magnetic resonance signal of the neuron resonance signal in accordance with each of the periods to obtain a plurality of different digital sequences corresponding to each of the different plurality of repetition periods, And a step of calculating a degree of correlation between the plurality of different digital sequences is recorded.

As described above, according to various embodiments of the present invention, the present invention provides a technique of processing data of a neuron resonance magnetic resonance image by measuring the magnitude of a neuron resonance signal using a plurality of repetition times (or a repetition period) .

The present invention also provides a technique for identifying a frequency component of a neuron resonance signal based on sampling data of a magnetic resonance signal of a neuron resonance signal according to a plurality of repetition times (or a repetition period) even in a state where the neuron resonance signal can not be predicted .

1 is a diagram illustrating communication between frequency selective brain regions in a neuron resonance magnetic resonance image according to an embodiment of the present invention.
2 is a diagram illustrating a process of acquiring a first digital sequence according to an embodiment of the present invention.
3 is a diagram illustrating a process of acquiring a second digital sequence according to an embodiment of the present invention.
4 is a flowchart of a method of detecting a neuron resonance signal according to an embodiment of the present invention.
5 is a diagram illustrating a method of detecting a frequency component of a neuron resonance signal using a first digital sequence and a second digital sequence according to an embodiment of the present invention.
6 is a diagram illustrating a method of detecting a frequency component of a neuron resonance signal using a first digital sequence and a second digital sequence according to another embodiment of the present invention.
7 is a flowchart illustrating a frequency spectrum correlation calculation process provided in accordance with an embodiment of the present invention.
8 is a flowchart illustrating a method of calculating a frequency domain correlation between a first frequency spectrum and a second frequency spectrum according to an embodiment of the present invention.
9 and 10 are views showing a first frequency spectrum and a second frequency spectrum according to an embodiment of the present invention.
11 is a flowchart illustrating a method of identifying a resonance frequency of a neuron according to an embodiment of the present invention.
12 is a view for explaining a method of identifying a resonance frequency of a neuron according to an embodiment of the present invention.
Figures 13-15 are flow diagrams illustrating a method for identifying resonant frequencies of neurons according to various embodiments.
16 is a block diagram of an MRI signal processing apparatus according to an embodiment of the present invention.
17 to 18 are diagrams showing simulation results for identifying the resonance frequency of a neuron according to an embodiment of the present invention.

Various embodiments will now be described in detail with reference to the accompanying drawings. The embodiments described herein can be variously modified. Specific embodiments are described in the drawings and may be described in detail in the detailed description. It should be understood, however, that the specific embodiments disclosed in the accompanying drawings are intended only to facilitate understanding of various embodiments. Accordingly, it is to be understood that the technical idea is not limited by the specific embodiments disclosed in the accompanying drawings, but includes all equivalents or alternatives falling within the spirit and scope of the invention.

 Terms including ordinals, such as first, second, etc., may be used to describe various elements, but such elements are not limited to the above terms. The above terms are used only for the purpose of distinguishing one component from another.

In this specification, the terms "comprises" or "having ", and the like, are intended to specify the presence of stated features, integers, steps, operations, elements, parts, or combinations thereof, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof. It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between.

In the meantime, "module" or "part" for components used in the present specification performs at least one function or operation. Also, "module" or "part" may perform functions or operations by hardware, software, or a combination of hardware and software. Also, a plurality of "modules" or a plurality of "parts ", other than a" module "or" part " which is to be performed in a specific hardware or performed in at least one control section, may be integrated into at least one module. The singular expressions include plural expressions unless the context clearly dictates otherwise.

In addition, in the description of the present invention, when it is judged that the detailed description of known functions or constructions related thereto may unnecessarily obscure the gist of the present invention, the detailed description thereof will be abbreviated or omitted. On the other hand, each embodiment may be independently implemented or operated, but each embodiment may be implemented or operated in combination.

1 is a diagram illustrating communication between frequency selective brain regions in a neuron resonance magnetic resonance image according to an embodiment of the present invention.

The mammalian brain exhibits resonance in the population of neurons by interactions between neurons. As shown in Fig. 1, one neuron group NG1 selectively communicates with another neuron group NG2 that resonates at a specific frequency and resonates at the same frequency.

The present invention provides a neuron resonance magnetic resonance imaging (NR-MRI) capable of measuring frequency-selective inter-brain communication signals. The present invention provides a method of extracting a resonance frequency component inherent to a neuron to provide a neuron resonance magnetic resonance image.

The resonance frequency component inherent to the neuron can be sampled by using a magnetic resonance signal sampled and a magnetic resonance signal of the neuron resonance signal. That is, the magnetic resonance signal may be the same signal as the neuron resonance signal. The frequency of the neuron resonance signal is higher than the frequency at which the present technology can sample the neuron resonance signal. For example, the frequency at which the neuron resonance signal can be sampled is 5 Hz or less, but the frequency of the neuron resonance signal is about several tens Hz. According to Nyquist Theorem, data must be sampled based on a frequency that is at least twice the highest frequency of the input signal to restore the original analog signal without loss. However, as described above, since the frequency that can be sampled is lower than the frequency of the neuron resonance signal, the resonance frequency component unique to the neuron can not be identified only by the general sampling method. Accordingly, Korean Patent Registration No. 10-1683217 discloses a method of extracting a resonance frequency of a neuron by assuming a resonance frequency of a neuron to be a specific value. However, since the resonance frequency of a neuron differs depending on a brain region, the above method can extract a resonance frequency of a neuron in a specific region, but there is a limit to extract a resonance frequency of a neuron included in all brain regions. Thus, the present invention provides a general method for extracting the resonance frequency of neurons for all brain regions.

2 is a diagram illustrating a process of acquiring a first digital sequence according to an embodiment of the present invention.

3 is a diagram illustrating a process of acquiring a second digital sequence according to an embodiment of the present invention.

Hereinafter, a method of acquiring a frequency component of a neuron resonance signal according to an embodiment of the present invention will be described with reference to FIGS. 2 and 3. FIG.

Referring to FIG. 2, an MRI controller according to an exemplary embodiment of the present invention may control the MRI scanner to generate a first MRI sequence 10 having a first TR 11. TR (Time to Repetition) is a repetition time or repetition period for sampling a magnetic resonance signal of a neuron resonance signal at regular intervals. The first MRI sequence 10 may comprise a plurality of periodically generated RF excitation signals e11, e12, e13, e14, .... The MRI control unit may be a computing device integrated into the MRI scanner or a computing device provided separately from the MRI scanner.

The MRI control unit then observes the time points t11, t12, t13, and t14, ... that have elapsed from the generation time of each of the RF excitation signals e11, e12, e13, and e14, The first observation values a11, a12, a13, a14, ... concerning the magnitude of the neuron resonance signal 1 of the target neuron can be obtained.

The time points (t11, t12, t13, and t14, ...) that have elapsed by a predetermined value may be referred to as a lead-out time point in the specification. At this time, the predetermined value may be a value corresponding to TE (Time Echo).

The discrete data formed by sequentially arranging the first observation values a11, a12, a13, a14, ... obtained with respect to the magnitude of the neuron resonance signal 1 will be referred to as a first readout sequence (d11). For example, the first readout sequence d11 may be given by d11 = [a11, a12, a13, a14, ...]. The process of obtaining the first readout sequence d11 may be referred to herein as a first process (P1). The first digital sequence may be generated by padding dummy data to the acquired first readout sequence d11, and if the dummy data is not padded, the acquired first readout sequence may be the first digital sequence.

Referring to FIG. 3, the MRI controller according to an embodiment of the present invention may control the MRI scanner to generate a second MRI sequence 20 having a second TR 21. The second MRI sequence 20 may include a plurality of RF excitation signals e21, e22, e23, e24, ..., which are periodically generated.

Here, the second TR 21 may be a different value from the first TR 11. For example, if the first TR 11 is a time repeated at 20 Hz, then the second TR 21 may be a time repeated at 21 Hz. However, the above-described concrete numerical values are one embodiment, and TR can be set to various values.

The MRI control unit then observes the time points t21, t22, t23, and t24, ... that have elapsed from the generation time of each of the RF excitation signals e21, e22, e23, and e24, The second observation values a21, a22, a23, a24, ... concerning the magnitude of the neuron resonance signal 1 of the object neuron can be obtained.

The discrete data formed by sequentially arranging the second observation values a21, a22, a23, a24, ... obtained with respect to the magnitude of the neuron resonance signal 1 will be referred to as a second readout sequence (d21). For example, the second readout sequence d21 may be given by d21 = [a21, a22, a23, a24, ...]. The process of obtaining the second readout sequence d21 may be referred to hereafter as the second process P2. The second digital sequence may be generated by padding dummy data to the obtained second lead-out sequence d21, and if the dummy data is not padded, the obtained second lead-out sequence may be the second digital sequence.

In FIG. 2 and FIG. 3, the neuron resonance signal 1 may have a period T. FIG. However, the period T is a previously unknown value, and the present invention relates to a technique for identifying period T rules. The period T may be identified by embodiments of the invention described below.

On the other hand, FIGS. 2 and 3 illustrate a process of sampling a magnetic resonance signal of a neuron resonance signal based on two TRs (repetition period or repetition time). However, the magnetic resonance signal of the neuron resonance signal may be sampled based on two or more TRs to generate two or more digital sequences.

4 is a flowchart of a method of analyzing a neuron resonance signal according to an embodiment of the present invention.

Referring to FIG. 4, the MRI signal processing apparatus samples a magnetic resonance signal of a neuron resonance signal according to each of a plurality of different repetition periods to acquire a plurality of different digital sequences corresponding to different repetition periods ( S410). As described above, the plurality of different repetition periods includes the first TR and the second TR. In some cases, the plurality of different repetition periods may include the third to n-th TRs. Then, the MRI signal processing apparatus can acquire the first digital sequence to the n < th > digital sequence corresponding to each TR.

The MRI signal processing apparatus calculates the degree of correlation between a plurality of different digital sequences in a frequency band based on a plurality of different digital sequences (S420). For example, the MRI signal processing apparatus can convert each of a plurality of different digital sequences into frequency bands, and calculate a correlation by superimposing a plurality of different digital sequences of the converted frequency bands. Alternatively, the MRI signal processing apparatus may convolve a plurality of different digital sequences and convert the convoluted digital sequence into a frequency band to calculate a correlation.

The MRI signal processing apparatus sets an arbitrary resonance frequency of the neuron resonance signal, and based on the phase difference between the set arbitrary resonance frequency and each of the different repetition periods and the data of each of the plurality of different digital sequences obtained, Can be calculated. In one embodiment, when the resonance frequency fNO of the neuron resonance signal is assumed to be 20 Hz, the period TNO becomes 50 ms. If the first TR is 60 ms, the data sampled consecutively according to the first TR is the data of TNO + 10 ms, TNO + 20 ms, TNO + 30 ms, ..., TNO + (10 × n) ms. If the second TR is 50 ms, data sampled consecutively according to the second TR is data of TNO-10 ms, TNO-20 ms, TNO-30 ms, ..., TNO- (10 × n) ms. If the resonance frequency of the hypothesized neuron resonance signal coincides with the resonance frequency of the actual neuron resonance signal, the energy in the frequency band of the data sampled according to the first TR and the second TR can be calculated to be larger than the energy in the other band have. A concrete procedure for calculating the correlation between the plurality of digital sequences in the MRI signal processing apparatus will be described later.

On the other hand, the MRI signal processing apparatus can identify a frequency component whose calculated correlation magnitude is greater than a predetermined magnitude as a frequency component of a neuron resonance signal.

5 is a diagram illustrating a method of analyzing a frequency component of a neuron resonance signal using a first digital sequence and a second digital sequence according to an embodiment of the present invention. As described above, the lead-out sequence may be referred to as a digital sequence, and the analysis of the frequency component of the neuron resonance signal may use a plurality of digital sequences as the first to n-th digital sequences.

5, it is assumed that the signal of the neuron to be observed resonates at a specific supposed resonance frequency. Then, the obtained first readout sequence d11 and second readout sequence d21 are compared with each other, (PC) that calculates the degree of correlation between the input image and the output image.

The correlation computation process PC may be performed a plurality of times and may change the value of the assumed resonance frequency each time it is performed. If the value of the assumed resonance frequency is changed, the degree of correlation between the first readout sequence d11 and the second readout sequence d21 may also vary. The assumed resonance frequency values to be changed here can be expressed, for example, as f1, f2, f3, f4, .... The assumed resonance frequency value may mean a plurality of different TRs (repetition time or repetition period).

The present invention is provided for a situation in which the actual resonant frequency fr of the signal of the neuron is not known in advance. Therefore, the fact that the actual resonance frequency fr is equal to or closest to any one of f1, f2, f3, f4, ... can be detected by the embodiments of the present invention described below.

Referring to FIG. 5, a method of analyzing a frequency component of a neuron resonance signal according to an embodiment of the present invention may include the following steps.

In step S510, it is assumed that the resonance frequency of the neuron to be observed is a specific assumed resonance frequency fk (k = 1, 2, 3, ..., or x).

Under the assumption of step S510, step S520 acquires the correlation degree CVk between the first readout sequence d11 and the second readout sequence d21 described above.

The above-described steps S510 and S520 may be repeatedly performed while changing the specific assumed resonance frequency fk to different values f1, f2, f3, ..., fx. As a result, a set of correlation {CV1, CV2, CV3, ... CVx} can be calculated.

In step S530, the largest value among the set of correlation maps {CV1, CV2, CV3, ... CVx} can be selected.

Step S540 may select an assumed resonance frequency corresponding to a selected one of the hypothesized resonance frequencies {f1, f2, f3, ..., fx}.

In operation S550, it may be determined that the selected resonance frequency is included in the resonance frequency of the neuron.

6 is a diagram illustrating a method of analyzing a frequency component of a neuron resonance signal using a first digital sequence and a second digital sequence according to another embodiment of the present invention.

Referring to FIG. 6, a method of analyzing a frequency component of a neuron resonance signal according to another embodiment of the present invention may include the following steps. Steps S610, S620, S640, and S650 may be the same as steps S510, S520, S540, and S550, respectively. Only differences from the method shown in Fig. 5 will be described below.

In step S530, values larger than a predetermined value among the set of correlation maps {CV1, CV2, CV3, ... CVx} can be selected.

The first readout sequence and the second readout sequence may be obtained a plurality of times by performing the first process and the second process described above each plural times. In this case, the SNR can be increased by using a plurality of first readout sequences and a plurality of second readout sequences in the process of acquiring the correlation degree (CVk).

In FIGS. 5 and 6, it is assumed that the correlation between the first readout sequence d11 and the second readout sequence d21 can be calculated. Hereinafter, a specific method of calculating the degree of correlation will be described. According to an embodiment of the present invention, the degree of correlation can calculate the degree of correlation in the frequency domain.

According to an embodiment of the present invention, a method of calculating the correlation between the first readout sequence d11 and the second readout sequence d21 may use the frequency spectrum correlation calculation process P3 described later .

7 is a flowchart illustrating a frequency spectrum correlation calculation process provided in accordance with an embodiment of the present invention.

The frequency spectral correlation calculation process P3 can be performed assuming that the neuron to be observed resonates at a certain assumed resonant frequency.

The frequency spectrum correlation calculation process (P3)

S710: converting the first readout sequence into a frequency domain to generate a first frequency spectrum f11,

S720: converting the second lead-out sequence to the frequency domain to generate a second frequency spectrum f21, and

S730 may include calculating a frequency domain-correlation between the first frequency spectrum and the second frequency spectrum in correlation between the first readout sequence and the second readout sequence.

At this time, the method of calculating the correlation between the first readout sequence d11 and the second readout sequence d21 is performed by performing the frequency spectrum correlation calculation process P3 one or more times while changing the specific hypothetical resonance frequency Step < / RTI > For example, a frequency spectrum correlation calculation process P3 may be performed for each frequency included in a preset set of assumed resonance frequencies f1, f2, f3, ..., fx, - Correlation can be calculated.

The frequency component analysis method for analyzing the frequency component of the neuron resonance signal shown in FIG. 5 and FIG. 6 may include calculating a frequency component of the neuron resonance signal based on one or more frequency domain-correlation degrees obtained for one or more specific hypothetical resonance frequencies May be detected.

At this time, the frequency domain-correlation between the first frequency spectrum and the second frequency spectrum included in step S730 can be calculated using, for example, steps S810 to S830 shown in the flowchart of FIG. At this time, it is assumed that the neuron to be observed resonates at a certain assumed resonance frequency.

8 is a flowchart illustrating a method of calculating a frequency domain correlation between a first frequency spectrum and a second frequency spectrum according to an embodiment of the present invention.

The step S810 may calculate a value relating to the first energy of the specific hypothetical resonance frequency in the first frequency spectrum.

The step S820 may calculate a value related to the second energy of the specific hypothetical resonance frequency in the second frequency spectrum.

In step S830, a value obtained by superposing (superimposing) the first energy and the second energy may be calculated as a frequency domain-correlation between the first frequency spectrum and the second frequency spectrum.

In this case, the 'superposition' suggested in step S830 may be a process of strengthening the first energy using the second energy, and the meaning of strengthening the first energy using the second energy is that the first energy and the second energy May be added or multiplied to each other to combine the first energy and the second energy.

At this time, the first readout sequence may be obtained by sampling the neuron resonance signal at the first TR interval. The neuron resonant signal is assumed to resonate at a certain assumed resonant frequency. The second readout sequence may be obtained by sampling the neuron resonance signal at the second TR interval. The neuron resonant signal is assumed to resonate at a certain assumed resonant frequency.

It will be appreciated by those skilled in the art that the first maximum frequency and the second maximum frequency represented by the first frequency spectrum and the second maximum frequency, respectively, when it is assumed that the neuron to be observed resonates at a certain hypothetical resonance frequency, It can be understood that it can be varied depending on the value of the specific hypothetical assumed resonant frequency and the data sampled based on the first TR or the second TR.

For example, when it is assumed that the neuron resonance signal resonates at 50 Hz, the period of the neuron resonance signal becomes 20 ms.

In this case, when the first TR is 90 ms, the phase of the neuron resonance signal observed at each lead-out time point is shifted by 10 ms. In this case, the first maximum frequency of the first frequency spectrum is 1000/10 ms = 100 Hz.

When the second TR is 91 ms, the phase of the neuron resonance signal observed at each lead-out time point is shifted by 9 ms, and the second maximum frequency of the second frequency spectrum is 1000/9 ms = ~ 111 Hz.

When the first maximum frequency and the second maximum frequency are determined as described above, a value relating to the first energy corresponding to a specific assumed resonance frequency 50 Hz from the first frequency spectrum is found as shown in steps S810 and S820 And find a value for a second energy corresponding to a particular hypothesized resonant frequency of 50 Hz from the second frequency spectrum.

Next, as shown in step S830, a value obtained by superposing the first energy and the second energy may be calculated as a frequency domain-correlation between the first frequency spectrum and the second frequency spectrum.

On the other hand, it can be divided into a first case where the assumed hypothetical resonance frequency is the same as the actual resonance frequency at which the observed neuron resonates, and a second case which is not the same. For example, when the actual resonance frequency of the neuron is 50 Hz, it can be divided into a first case in which a specific assumed resonance frequency is assumed to be 50 Hz and a second case in which a specific assumed resonance frequency is assumed to be 55 Hz.

At this time, the frequency domain-correlation calculated in the first case may be larger than the frequency domain-correlation calculated in the second case. Thus, it can be determined that the assumed hypothetical resonant frequency 50 Hz in the first case, which exhibits a greater correlation, is the actual resonant frequency of the neuron.

9 and 10 are views showing a first frequency spectrum and a second frequency spectrum according to an embodiment of the present invention.

FIG. 9 shows a first frequency spectrum and a second frequency spectrum according to an embodiment of the present invention. FIG. 9 shows a frequency spectrum when a resonance frequency of an unknown neuron is assumed to be a first value.

9 is a diagram for explaining the case where the actual resonance frequency of the neuron is equal to the assumed resonance frequency in the case of the first case wherein the maximum frequency fmax of the first frequency spectrum f11 is 100 Hz and the second frequency spectrum the maximum frequency fmax of the frequency f21 is 111 Hz. The MRI signal processing apparatus detects a first energy a1 corresponding to a specific assumed resonance frequency 50 Hz in the first frequency spectrum and a second energy a2 corresponding to an assumed frequency 50 hz in the second frequency spectrum And a first superposition value a1 * a2 can be calculated by superposing the first energy and the second energy.

FIG. 10 shows a first frequency spectrum and a second frequency spectrum derived in accordance with an embodiment of the present invention. In FIG. 10, when a resonance frequency of an unknown neuron is assumed to be a second value different from the first value, Lt; / RTI > The frequency spectrums shown in FIG. 10 are the same as the frequency spectrums shown in FIG. 9, but the scales on the frequency axis are different from each other.

10, that is, when the actual resonance frequency of the neuron is different from the assumed resonance frequency, for example, the actual resonance frequency of the neuron is 50 Hz, the first TR is 90 ms, the second TR is 91 ms, The assumed resonance frequency can be set to be 53 Hz. At this time, the phase of the neuron resonance signal observed at each lead-out time point of the first TR is shifted by 4.34 ms at each observation, and the first maximum frequency of the first frequency spectrum is 1000 / 4.34 ms = 230 Hz. The phase of the neuron resonance signal observed at the lead-out time point of the second TR is shifted by 3.34 ms in each observation, and the second maximum frequency of the second frequency spectrum is 1000 / 3.34 ms = ~ 299 Hz.

The MRI signal processing apparatus detects a first energy a3 corresponding to a specific assumed resonance frequency 55 Hz in the first frequency spectrum and a second energy a3 corresponding to an assumed frequency 55 hz in the second frequency spectrum, ), And calculate the second superposition value a3 * a4 by superposing the first energy and the second energy.

9 and 10, it can be seen that the first superposition value is larger than the second superposition value. That is, when the assumed resonance frequencies coincide with each other, the energy value calculated in the frequency band is reinforced to produce a larger value. If the assumed resonance frequencies do not match, the energy value calculated in the frequency band is canceled, . Therefore, when the first superposition value having a larger value is calculated, the assumed 50 Hz is the actual resonance frequency of the neuron, so that it is possible to determine the specific assumed resonance frequency 50 Hz as the actual resonance frequency of the neuron. That is, the MRI signal processing apparatus can identify a frequency component whose calculated correlation magnitude is greater than a predetermined magnitude, as a frequency component of a neuron resonance signal.

A method of determining the resonance frequency of a neuron according to another embodiment of the present invention will be described with reference to FIGS. 11 and 12. FIG.

11 is a flowchart illustrating a method of identifying a resonance frequency of a neuron according to an embodiment of the present invention.

The step S1110 may obtain the first readout sequence d11 = [a11, a12, a13, a14, ...] using the first process P1 described above.

The step S1120 can obtain the second readout sequence d21 = [a21, a22, a23, a24, ...] using the second process P2 described above.

The step S1130 may generate a first digital sequence with a sampling interval? T from the first readout sequence d11 = [a11, a12, a13, a14, ...]. On the other hand, the step S1130 may include the following steps.

In step S1131, the first observation values a11, a12, a13, a14, ..., which are the respective elements of the first readout sequence d11 = [a11, a12, a13, a14, can do. At this time, the time interval between the first observation values adjacent to each other may be the same as the first TR (11).

The step S1132 may insert the first dummy data between the first observation values adjacent to each other to generate the first digital sequence. In this case, the first dummy data may be '0' or a constant, or may be a value generated by interpolating first observation values arranged on the time axis. At this time, among the elements of the generated first digital sequence, the time interval of two elements adjacent to each other on the time axis can be set to? T. DELTA T can be understood as a sampling interval.

The step S1140 may generate the second digital sequence with the sampling interval? T from the second readout sequence d21 = [a21, a22, a23, a24, ...]. On the other hand, the step S1140 may include the following steps.

In step S1141, the second observation values a21, a22, a23, a24, ..., which are the respective elements of the second readout sequence d21 = [a21, a22, a23, a24, Can be arranged. At this time, the time interval between the second observation values adjacent to each other may be the same as the second TR 21.

The step S1142 may insert the second dummy data between the neighboring second observation values to generate the second digital sequence. Here, the second dummy data may be '0' or a constant, or may be a value generated by interpolating second observation values arranged on the time axis. At this time, among the elements of the generated second digital sequence, the time interval of two adjacent elements on the time axis can be set to? T. DELTA T can be understood as a sampling interval.

At this time, the maximum value of? T may be abs (first TR (11) - second TR (21)).

The step S1150 may convert the first digital sequence and the second digital sequence into frequency bands, respectively, to generate the first frequency spectrum and the second frequency spectrum, respectively. For this purpose, a Fourier transform can be used. Wherein the number of the first digital sequence used for the transformation may be equal to the number of the second digital sequence used for the transformation.

In operation S1160, the first frequency spectrum may be multiplied by the second frequency spectrum to generate a final frequency spectrum. Multiplying may mean multiplying two spectral values corresponding to the same frequency magnitude in the first frequency spectrum and the second frequency spectrum.

In operation S1170, one or more frequency values having a magnitude equal to or greater than a threshold value may be selected from the final frequency spectrum, and one or more of the selected frequency values may be determined as a resonance frequency of the neuron.

12 is a view for explaining a method of identifying a resonance frequency of a neuron according to an embodiment of the present invention.

The first readout sequence d11 sampled at the sampling rate TR1 on the time axis extending along the left and right direction of Fig. 12 can be converted into data of the first digital sequence having the new sampling interval? T. Similarly, the second readout sequence d21 sampled at the sampling rate TR2 can be converted into data of the second digital sequence having the sampling interval? T.

At this time, the un sampled space can be filled with 0 or filled with interpolation.

For example, in the case of TR1 = 90 ms and DELTA T = 1 ms, 89 pieces of dummy data '0' can be filled between signal values obtained in the first readout sequence d11 sampled at the sampling rate of TR1. The first frequency spectrum obtained by performing the FFT on the first digital sequence thus obtained may represent a frequency range of 0 to 500 Hz.

For example, in the case of TR2 = 91 ms and DELTA T = 1 ms, 90 dummy data '0' can be filled between signal values obtained in the second readout sequence d21 sampled at the sampling rate TR2. The second frequency spectrum obtained by performing the FFT on the second digital sequence thus obtained may indicate a frequency range of 0 to 500 Hz.

Since the first digital sequence and the second digital sequence have more dummy data than the actually measured data, aliased signals may appear in the calculated first frequency spectrum and the second frequency spectrum. To suppress this aliased signal, the first frequency spectrum and the second frequency spectrum may be multiplied together to produce a final frequency spectrum. This will eventually identify the frequency of the neuron.

In the method of filling dummy data '0' between data, the maximum value of DELTA T may be an absolute value of TR1 - TR2. That is, if TR1 = 90 ms and TR2 = 100 ms,? T can be a maximum of 10 ms. In this case, dummy data '0' is filled between the signals in the TR1 data and dummy data '0' Can be filled in nine. Thereafter, the range of the first frequency spectrum and the second frequency spectrum obtained after the Fourier transform may be 0 to 50 Hz. Of course, in this case,? T may be 1 ms. At this time, it is preferable that DELTA T is a divisor of 10 ms.

A method for analyzing the frequency component of a neuron resonant signal provided in accordance with various embodiments of the present invention may include the following steps.

This will be described below with reference to Figs. 2, 3, and 13. Fig.

13 is a flowchart illustrating a method of identifying a frequency component of a neuron resonance signal provided according to an embodiment of the present invention.

Step S1310 is a step of determining the size (a101) of the neuron resonance signal 1 to be observed which is obtained at each of the lead-out time points t11, t12, t13, ... of the first MR sequence 10 having the first TR 11 , a102, a03, a104, ...) from the first readout sequence d11.

Step S1320 is a step of generating a neuron resonance signal (t21, t22, t23, ...) obtained at each of the readout time points t21, t22, t23, ... of the second MR sequence 20 having the second TR 21 different from the first TR The second digital sequence can be generated from the second readout sequence d21 indicating the size (a201, a202, a203, a204, ...) of the first read sequence d21.

The step S1330 may include determining a frequency component of the neuron resonance signal from the enhanced frequency spectrum using the first frequency spectrum of the first digital sequence using the second frequency spectrum of the second digital sequence, Can be used to identify the frequency component of the neuron resonant signal from the frequency spectrum of the enhanced digital sequence.

At this time, step S1310 may include a step S1311 of generating a first digital sequence by padding the first dummy data to the first readout sequence so that the sampling period of the first digital sequence becomes a predetermined? T.

In operation S1320, the second digital sequence may be generated by padding the second dummy data to the second readout sequence so that the sampling period of the second digital sequence is DELTA T (S1321).

In addition, step S1330 may identify the frequency component of the neuron resonance signal from the enhanced frequency spectrum using the first frequency spectrum of the first digital sequence and the second frequency spectrum of the second digital sequence. Reinforcement can be an addition or a multiplication operation.

Alternatively, step S1330 may identify the frequency component of the neuron resonance signal from the frequency spectrum of the enhanced digital sequence using the first digital sequence and the second digital sequence. At this time, the enhancement may be a convolution operation.

The predetermined sampling period? T may be less than or equal to the absolute value of the difference between the first TR and the second TR. The first dummy data may be a value '0', a constant or an interpolated value from the first readout sequence, and the second dummy data may be a value '0', a constant or an interpolated value from a second readout sequence.

The method of analyzing the frequency component of the neuron resonance signal provided according to another embodiment of the present invention may include the following steps.

This will be described below with reference to Figs. 2, 3, 14, and 15. Fig.

14 is a flowchart illustrating a method of analyzing a frequency component of a neuron resonance signal provided according to another embodiment of the present invention.

Step S1410 is a step of determining the size (a101) of the neuron resonance signal 1 to be observed which is obtained at each of the lead-out time points t11, t12, t13, ... of the first MR sequence 10 having the first TR 11 , a102, a03, a104, ...) from the first readout sequence d11.

 Step S1420 is a step of acquiring the neuron resonance signal 1 (t) obtained at each of the lead-out time points t21, t22, t23, ... of the second MR sequence 20 having the second TR 21 different from the first TR 11 The second digital sequence can be generated from the second readout sequence d21 indicating the size (a201, a202, a203, a204, ...)

Step S1430 may identify the frequency component of the neuron resonant signal from the enhanced frequency spectrum using the first frequency spectrum of the first digital sequence and the second frequency spectrum of the second digital sequence.

At this time, the first digital sequence may be the first readout sequence and the second digital sequence may be the second readout sequence. Step S1430 may identify the frequency component of the neuron resonant signal from the enhanced frequency spectrum using the first frequency spectrum of the first digital sequence and the second frequency spectrum of the second digital sequence. At this time, the enhancement may be a addition or a multiplication operation.

Step S1430 may further include the following steps.

In step S1431, when it is assumed that the neurons to be observed resonate at one or more specific resonance frequencies, a correlation between the first readout sequence and the second readout sequence is obtained for each specific hypothetical resonance frequency .

Step S1432 may detect the frequency component of the neuron resonance signal based on one or more degrees of correlation obtained for one or more specific hypothesized resonance frequencies. In operation S1432, a correlation value having a value greater than or equal to a predetermined value among the one or more correlation values is selected (S1432-1), and a frequency corresponding to the selected correlation degree among the one or more hypothesized specific resonance frequencies is referred to as a neuron resonance signal (S1432-2) identifying that the frequency is included in the frequency band.

At this time, the MRI signal processing apparatus obtains the first readout sequence a plurality of times by performing the step S1410 a plurality of times, performs the step S1420 a plurality of times to obtain the second readout sequence a plurality of times, The correlation can be calculated using the sequence and a plurality of acquired second readout sequences.

At this time, step S1431 may calculate the frequency spectrum correlation including the following steps. In step S1431-1, when it is assumed that the neuron to be observed resonates at a specific resonance frequency, the first readout sequence may be converted into the frequency domain to generate the first frequency spectrum. In step S1431-2, when it is assumed that the neuron to be observed resonates at a specific resonance frequency, the second readout sequence may be converted into the frequency domain to generate the second frequency spectrum. In step S1431-3, the frequency domain-correlation between the first frequency spectrum and the second frequency spectrum may be calculated as a degree of correlation between the first readout sequence and the second readout sequence. Step S1431 may perform the frequency spectrum correlation calculation process more than once while changing the assumed resonance frequency.

15 is a diagram for explaining a method of calculating a frequency domain-correlation between a first frequency spectrum and a second frequency spectrum as an upper diagram between a first readout sequence and a second readout sequence according to an embodiment of the present invention; to be.

Step S1510 may include the following steps S1511, S1512, and S1513. Step S1511 may calculate a value related to the first energy of the specific resonance frequency in the first frequency spectrum. The step S1512 may calculate a value related to the second energy of the specific resonance frequency in the second frequency spectrum. In step S1513, a value obtained by enhancing the first energy using the second energy may be calculated as a frequency domain-correlation between the first frequency spectrum and the second frequency spectrum. Where enforcing may mean addition or multiplication operations.

Referring again to FIG. 14, step S1432 may include detecting a frequency component of the neuron resonance signal based on one or more frequency domain-correlation degrees obtained for one or more specific resonance frequencies.

Various embodiments of the detection method of the neuron resonance signal have been described so far. In the following, an MRI signal processing apparatus for detecting a neuron resonance signal will be described.

16 is a block diagram of an MRI signal processing apparatus according to an embodiment of the present invention.

Referring to FIG. 16, the MRI signal processing apparatus 100 includes an input unit 110 and a control unit 120. The input unit 110 receives a magnetic resonance signal of a neuron resonance signal.

The control unit 120 samples the magnetic resonance signals of the neuron resonance signals according to the plurality of different repetition periods to acquire a plurality of different digital sequences corresponding to the plurality of different repetition periods. The repetition period is a period for sampling a magnetic resonance signal of a neuron resonance signal at regular intervals, and may be expressed as a time to repetition (TR). The repetition period may include a first TR, a second TR, and in some cases, a third TR, ..., a nth TR.

Then, the control unit 120 calculates the degree of correlation between a plurality of different digital sequences in the frequency band based on a plurality of different digital sequences. The controller 120 can identify the frequency component of which the magnitude of the calculated degree of correlation is greater than or equal to a predetermined magnitude as a frequency component of the neuron resonance signal.

Meanwhile, the controller 120 may convert each of the plurality of different digital sequences into a frequency band, and may calculate a degree of correlation by superimposing a plurality of different digital sequences in the converted frequency band. Alternatively, the control unit 120 may convolve a plurality of different digital sequences, and may convert the convoluted digital sequence into a frequency band to calculate the degree of correlation.

The control unit 120 can padd the dummy data to have a constant sampling period in the readout sequence obtained from the neuron resonance signal at a constant period. For example, the dummy data may be 0, a predetermined constant, or a value interpolating data contained in the corresponding digital sequence.

The control unit 120 sets an arbitrary resonance frequency of the neuron resonance signal, and calculates a correlation based on the set arbitrary resonance frequency, the phase difference between each different repetition period, and each of a plurality of different digital sequence data obtained Can be calculated. Since specific embodiments have been described above, the description is omitted here.

17 to 18 are diagrams showing simulation results for identifying the resonance frequency of a neuron according to an embodiment of the present invention.

Referring to FIG. 17, there is shown a simulation result using two different TRs (90 ms, 91 ms) for one frequency component (25 Hz) of the neuron resonance signal. As described above, since the maximum frequencies of the different TRs are different from each other, the process of adjusting the scales before overlapping the digital sequences of the two frequency regions can be performed. That is, the circles shown in FIG. 17A are displayed at different positions because the scales are different when the TR is 90 ms and when the TR is 91 ms, and all the energy values for the frequency components of 25 Hz are shown. Referring to FIG. 17 (b), when the digital sequences in the frequency domain according to two different TRs are superimposed, the other frequency components are suppressed and the 25 Hz frequency components are reinforced to display a relatively larger energy value.

Referring to FIG. 18, simulation results for two frequency components (10 Hz, 15 Hz) of the neuron resonance signal are shown. Fig. 18 (a) shows the results of sampling the neuron resonance signals according to different TRs, and Fig. 18 (b) shows the results superimposed in the frequency domain. Similar to FIG. 17, when the digital sequences in the frequency domain according to different TRs are superimposed, the two frequency components (10 Hz, 15 Hz) of the neuron resonance signal have a relatively larger energy value As shown in FIG.

The method of analyzing the neuron resonance signal according to the various embodiments described above may be provided as a computer program product. The computer program product may include a software program itself or a non-transitory computer readable medium in which the software program is stored.

A non-transitory readable medium is a medium that stores data for a short period of time, such as a register, cache, memory, etc., but semi-permanently stores data and is readable by the apparatus. In particular, the various applications or programs described above may be stored on non-volatile readable media such as CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM,

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is clearly understood that the same is by way of illustration and example only and is not to be construed as limiting the scope of the invention as defined by the appended claims. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention. Further, by using the embodiments of the present invention described above, those skilled in the art can easily make various changes and modifications without departing from the essential characteristics of the present invention. The contents of each claim in the claims may be combined with other claims without departing from the scope of the claims.

100: MRI signal processing device
110: input unit 120:

Claims (14)

A method for detecting a neuron resonance signal,
Sampling a magnetic resonance signal of the neuron resonance signal according to each of a plurality of different repetition periods to obtain a plurality of different digital sequences corresponding to each of the plurality of different repetition periods; And
And calculating a degree of correlation between the plurality of different digital sequences in a frequency band based on the plurality of different digital sequences. The method according to claim 1,
Identifying a frequency component for which the size of the calculated degree of correlation is calculated to be equal to or larger than a predetermined size as a frequency component of the neuron resonance signal. The method according to claim 1,
Wherein the calculating step comprises:
Wherein the degree of correlation is calculated by converting each of the plurality of different digital sequences into a frequency band and superimposing a plurality of different digital sequences of the converted frequency band. The method according to claim 1,
Wherein the calculating step comprises:
Wherein the plurality of different digital sequences are convoluted and the convoluted digital sequence is converted into a frequency band to calculate the correlation. The method according to claim 1,
Wherein the calculating step comprises:
Setting a certain resonance frequency of the neuron resonance signal, and based on the set arbitrary resonance frequency, the phase difference between each of the different repetition periods, and the data of each of the plurality of different digital sequences obtained, Of the neuron resonant signal. The method according to claim 1,
And padding dummy data so that a predetermined sampling period is obtained in each of the obtained plurality of different digital sequences. The method according to claim 6,
The dummy data includes:
0, a preset constant, or a value interpolated for data included in a digital sequence for each of the plurality of different digital sequences. The method according to claim 1,
Wherein the plurality of digital sequences comprises:
A first digital sequence generated from a first readout sequence indicating a magnitude of the neuron resonance signal obtained at each lead-out point of the magnetic resonance signal according to a first repetition period, and a second repetition period And a second digital sequence generated from a second readout sequence indicative of the magnitude of the neuron resonance signal obtained at each lead-out point of the magnetic resonance signal according to the second readout sequence. 9. The method of claim 8,
Wherein the first digital sequence comprises:
A sequence generated by padding the first dummy data into the first readout sequence so that the sampling period becomes a predetermined period,
Wherein the second digital sequence comprises:
Wherein the second dummy data is a sequence generated by padding second dummy data to the second read-out sequence so that the sampling period becomes the predetermined period. 10. The method of claim 9,
Wherein the calculating step comprises:
Wherein the degree of correlation is calculated by superimposing the first frequency spectrum and the second frequency spectrum on the basis of the first frequency spectrum of the first digital sequence and the second frequency spectrum of the second digital sequence, Way. 10. The method of claim 9,
Wherein the calculating step comprises:
Wherein the first digital sequence and the second digital sequence are convoluted and the convoluted digital sequence is converted to a frequency band to calculate the correlation from the frequency spectrum of the convoluted digital sequence. 10. The method of claim 9,
The predetermined period may be,
Wherein the difference between the first repetition period and the second repetition period is equal to or less than a difference between the first repetition period and the second repetition period. An input unit for receiving a magnetic resonance signal of a neuron resonance signal; And
And a controller for sampling a magnetic resonance signal of the neuron resonance signal according to each of a plurality of different repetition periods to acquire a plurality of different digital sequences corresponding to each of the plurality of different repetition periods,
Wherein,
And calculates a degree of correlation between the plurality of different digital sequences in a frequency band based on the plurality of different digital sequences. Sampling a magnetic resonance signal of a neuron resonance signal according to each of a plurality of different repetition periods to obtain a plurality of different digital sequences corresponding to each of the plurality of different repetition periods; And
And calculating a correlation between the plurality of different digital sequences in a frequency band based on the plurality of different digital sequences.

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