KR101674326B1 - Functional magnetic resonance imaging method without baseline epochs information by mapping true T2* change with multi-echo EPI signal similarity analysis - Google Patents

Abstract

A functional magnetic resonance imaging (fMRI) imaging method and an fMRI imaging system are disclosed. According to an embodiment of the present invention, there is provided an fMRI imaging method comprising the steps of: (a) obtaining multi-echo echo planar imaging (ME-EPI) data for a region of interest of a subject; (b) calculating a standard deviation of a pure signal component in which noise components are excluded for at least two TE data among a plurality of TE time data of the ME-EPI data; (c) estimating T2 * deviation information of the ME-EPI data based on the calculated standard deviations; And (d) displaying a functional image of a region of interest of the object by an intensity indication proportional to the estimated T2 * deviation information.

Description

The present invention relates to a method and an apparatus for measuring an image of a subject, and more particularly, to a method and apparatus for measuring a fMRI image,

The present invention relates to a fMRI (functional MRI) imaging method, and more specifically, it relates to a method and apparatus for detecting a T2 * change component without baseline epochs information by analyzing a similarity between multi-echo EPI And the fMRI imaging method that can be used.

The EPI (Echo Planar Imaging) data value used in the fMRI imaging method is determined by the amount of hydrogen molecules (SO) in the voxel and the value of T2 (T2 *) of substances containing hydrogen molecules. When neuronal activity occurs in the brain, the concentration of oxyhemoglobin (OxyHb) in the capillaries increases and the value of T2 * increases and EP data increases. However, because the EPI data values due to S0 and T2 * deviations between regions of the brain are greater than the EPI data values due to changes in neuronal activity, the EPI data values of a particular voxel alone are used to determine the magnitude of neural activity in the voxel I can not judge.

Due to this limitation, in the conventional fMRI imaging method, baseline epoch data providing T2 * value information serving as a comparison reference for the T2 * value increased or decreased due to nerve activity according to a specific condition And functional imaging based on EPI data is performed in a manner that statistically analyzes the signal value difference between the reference time zone data and the specific condition time zone data.

However, according to the conventional fMRI imaging method using the baseline epoch information, since the time zone setting of the reference stimulus is required and the information of the time zone is indispensably required, it is difficult to set the reference stimulus or difficult to grasp the information of the reference stimulus time zone There is a limitation that a normal fMRI experiment and analysis can not be performed.

In the above-described conventional fMRI imaging method, data analysis is performed through HRD (hemodynamic response function) modeling from a stimulus protocol. Because HRF may be different for each brain region and may vary from stimulus to stimulus in the same region, There is a high possibility that an error is generated because the lost and irrelevant neural activity signal is emphasized.

In addition, the analysis result of the above-described conventional fMRI imaging method has a problem that not only the brain activity characteristic component related to nerve activity but also the brain activity characteristic components not related to the nerve activity such as cerebral blood flow activity and brain movement are reflected.

US registered patent US 8,200,648 B2 (Jun. 12, 2012)

The present invention provides an fMRI imaging method capable of imaging brain functions without using reference time zone information, but also removing signal components irrelevant to neural activity.

Accordingly, the present invention provides a method comprising: (a) obtaining multi-echo echo planar imaging (ME-EPI) data for a region of interest of a subject; (b) calculating a standard deviation of a pure signal component in which noise components are excluded for at least two TE data among a plurality of TE time data of the ME-EPI data; (c) estimating T2 * deviation information of the ME-EPI data based on the calculated standard deviations; And (d) displaying a functional image for a region of interest of the object by an intensity indication proportional to the estimated T2 * deviation information.

In step (b), the calculation of the standard deviation of the pure signal component may be performed based on a signal similarity (SS) analysis between the TE data.

The step (c) includes the steps of: (c1) calculating a coefficient of variance (CV) for each TE data by dividing an average value of each TE data by the standard deviation value calculated for the at least two TE data; And (c2) calculating a slope value of the primary function graph having the calculated dispersion coefficient value, wherein the dispersion coefficient is a Y-axis variable and TE is an X-axis variable.

The functional image display in the step (d) may be performed by applying the slope value calculated in the step (c2) as an index reflecting the T2 * deviation information.

The present invention also relates to an MRI apparatus for obtaining multi-echo echo planar imaging (ME-EPI) data on a region of interest of a target object; Calculating a standard deviation of a pure signal component from which noise components are excluded for at least two TE data among a plurality of TE time data of the ME-EPI data, calculating ME-EPI based on the calculated standard deviations, A microprocessor for estimating T2 * deviation information of the data; And a display unit for displaying a functional image for a region of interest of the target object by an intensity indication proportional to the estimated T2 * deviation information.

The microprocessor can calculate the standard deviation of the pure signal component based on a signal similarity (SS) analysis between the TE data.

The microprocessor calculates a coefficient of variance (CV) for each TE data by dividing the average value of each TE data by the standard deviation value calculated for the at least two TE data, , The slope value of the linear function graph in which the dispersion coefficient is the Y-axis variable and TE is the X-axis variable is calculated, and the slope value can be used as the estimated value of the T2 * deviation information of the ME-EPI data.

The display unit may display the functional image for the region of interest of the target object using the slope value as an index reflecting the T2 * deviation information.

According to the fMRI imaging method of the present invention, it is possible to obtain an image useful for analyzing the neural activity activity of the brain without using the reference time zone information as in the prior art.

In the fMRI imaging method of the present invention, since the baseline epoch information is not required, the normal fMRI experiment and analysis can be performed even in a situation where the setting of the reference stimulus is difficult or the information of the reference stimulus time period is difficult to grasp, Since there is no need to apply HRF (Hemodynamic response function) modeling, the problem of error generation can be solved.

In the fMRI imaging method of the present invention, only the T2 * deviation information directly related to the neural activity is excluded from the TE data and the signal components not related to the neural activity are reflected in the functional image The problem can be solved.

In the fMRI imaging method of the present invention, since deviation information on a pure signal from which noises are removed is used through correlation analysis between TE data, it is possible to obtain a white noise component according to the application of an exponential fitting technique. A problem that an estimation error can not be ignored can be solved.

In the fMRI imaging method of the present invention, the standard deviation of each data is calculated through the correlation analysis between the TE data, and the slope of the first-order function graph for the TE data and TE is calculated therefrom, and the functional image is displayed Therefore, the data operation procedure for functional image acquisition can be considerably simplified as compared with the prior art.

1 is a block diagram illustrating an fMRI imaging system in accordance with an embodiment of the present invention.
2 is a diagram for explaining a linear function relationship between the standard deviation of TE data included in ME-EPI data and TE.
3 (a) shows an example of a brain functional image according to the prior art using baseline epoch information. FIG. 3 (b) shows a brain functional image obtained by applying the fMRI imaging method of the present embodiment Is shown.

Hereinafter, embodiments of the present invention will be described in more detail with reference to the accompanying drawings.

1 is a block diagram illustrating an fMRI imaging system in accordance with an embodiment of the present invention.

1, an fMRI imaging system 100 according to an exemplary embodiment of the present invention includes an MRI apparatus 110, a memory 120, a microprocessor 130, a user interface 140, and a display unit 150 .

The MRI equipment 110 provides multi-echo echo planar imaging (EPI) data for functional imaging. In this case, a multi-echo method is a method of acquiring MR signals in a plurality of TE (echo time) for the same RF pulse applied to a target object. This means that, for the same RF pulse applied to the object, Is obtained.

The memory 120 stores the MR signals obtained through the MRI apparatus 110. Also, the memory 120 may store a computer program for implementing the fMRI imaging method according to the present invention. Although memory 120 is separate from MRI equipment 110 in FIG. 1, memory 120 may be integrated into MRI equipment 110 in an alternative embodiment.

The microprocessor 130 controls the operation of the MRI apparatus 110 and the display unit 150 based on the computer program stored in the memory 120 and the user input information through the user interface 140, And performs processing and calculation of data for the imaging method. 1, the microprocessor 130 is separate from the MRI equipment 110, but in an alternative embodiment the microprocessor 130 may be integrated into the MRI equipment 110.

The user interface 140 may be provided as an input device such as a keyboard and a mouse for user input.

The display unit 150 displays the EPI data provided by the MRI apparatus 110 and the functional images obtained by the fMRI imaging method according to the present invention on the screen.

The fMRI imaging method according to the present invention can be performed through the fMRI imaging system 100 described above. Although the fMRI imaging method according to the present invention is not limited to be practiced only through the fMRI imaging system 100 described above, the fMRI imaging method according to an embodiment of the present invention described below may be performed by the fMRI imaging system 100 described above Is executed.

According to the fMRI imaging method of the present embodiment, first, multi-echo (ME) EPI data for a target object is obtained using the MRI apparatus 110 (S10).

As described above, the multi-echo (ME) method refers to a method of applying RF pulses to an object and obtaining MR signals at a plurality of TE (echo time). Unlike the present embodiment, in the prior art in which functional imaging is performed using baseline epoch information, an RF pulse is applied to a target object to obtain an MR signal from a single TE.

Individual signals (each TE signal) of ME-EPI data obtained through the MRI equipment 110 can be expressed by the following equation.

M (t) = S0 (t) * exp (-TE / T2 (t)) + white noise (t)

In this equation, T2 (t) is the data related to the neural function of the brain. S0 (t) is brain activity data not directly related to neural function activity related to brain movement, blood flow into cerebral blood vessels, or CSF activity of the ventricle. And, white noise (t) is external noise and has nothing to do with brain activity. TE is the echo time. (T) and white noise (t) in the functional imaging process according to the present embodiment, since T2 (t) is data that reflects the neural activity of the brain, while S0 ) Are the objects to be removed. The removal process will be described later.

Next, a standard deviation of a pure signal component from which noise components are excluded for at least two TE data among a plurality of TE data of the ME-EPI data is calculated (S20).

The step S20 may be performed by the microprocessor 130. In step S20, the standard deviation of the pure signal components may be calculated for each of the plurality of TE data included in the ME-EPI data. Alternatively, only the TE data selected from the plurality of TE data may be calculated The standard deviation of the pure signal component may be calculated.

For example, if the ME-EPI data includes three TE data, i.e., TE1 data, TE2 data, and TE3 data, the standard deviation for pure signal components can be calculated for TE1 and TE2 data in step S20, The standard deviation for pure signal components may be calculated for TE1 and TE3 data or the standard deviation for pure signal components for TE2 and TE3 data may be calculated.

The selection of the TE data to be subjected to the standard deviation calculation in step S20 may be performed by a computer program stored in the memory 120 or directly by the user via the user interface 140. [

As described above, each TE data can be expressed by the following equation.

M (t) = S0 (t) * exp (-TE / T2 (t)) + white noise (t)

Here, the white noise (t) is particularly emphasized in the region of the brain where the MRI signal is weak, thereby distorting the brain function image based on the T2 * deviation, and step S20 is a process for removing such white noise (t) .

Step S20 is performed based on signal similarity analysis between TE data. If two TE data are X (t) and Y (t), the signal similarity SS between the data is SS It can be calculated as a formula.

CC (X (t), Y (t)) = CC (X (t), Y (t)))

(X (t) -mean (Y (t)))) = CC (X (t), Y

Based on the calculated signal similarity SS, the following series of arithmetic operations can be developed.

X (t)) = Var (X (t)) = std (X (t)) ^ 2

Assume, Wn (t) = mean 0, white noise

put, sTE1 (t) = S0 (t) * exp (-TE1 / T2 (t)

put, sTE2 (t) = S0 (t) * exp (-TE2 / T2 (t))

put, mean (sTE1 (t)) = m1 , put, mean (sTE2

(t) -m1 = d1 (t), put, sTE2 (t)

put, m2 / m1 = alpha ~ = exp ((TE1-TE2) / mean (t2))

put, m1 / m2 = beta ~ = exp ((TE2-TE1) / mean (t2))

put, std (sTE2 (t)) = X1 , put, std (sTE2

(tn) / X1 = R1 , put, std (Wn (t)) / X2 = R2

put, TE1 (t) = sTE1 (t) + Wn (t)

put, TE2 (t) = sTE2 (t) + Wn (t)

2) (TE1 * std (T2) / mean (T2) ^ 2) (2)

(T2) / mean (T2) ^ 2) 2) (2 / mo> st2 / mo>

(T2) / mean (T2) ^ 2) 2) (2 / mo> s 2 / X 1 = m 2 / m 1 * sqrt

/ sqrt ((std (s0) / mean (s0)) ^ 2+ (TE1 * std (T2) / mean (T2) ^ 2)

(t2) ^ 2 / mean (t2) ^ 4 << (std (s0) / mean (s0)) ^ 2)

(X (t)) = Wn (t)) = 0 (t)

mean (TE1) = mean (sTE1) = m1, mean (sTE2) = mean (TE2) = m2

(TE1 (t) - m2) TE2 (t) - TE2 (t) = mean

                    = mean ((d1 (t) + Wn (t)) * (d2 (t) + Wn (t)))

     = CC (d1 (t), d2 (t)) + CC (Wn (t), d1 ))

     = CC (d1 (t), d2 (t))

                   = mean ((sTE1 (t) -m1) * (sTE2 (t) -m2))

                  ~ = mean ((sTE1 (t) -m1) * alpha * (sTE1 (t) -m1))

                   = alpha * Var (sTE1 (t))

= alpha * X1 ^ 2 ~ = 1 / alpha * X2 ^ 2 = beta * X2 ^ 2

(TE1 (t) -m1) * (TE1 (t) - TE1 (t)) = mean

   (sTE1 (t) -m1 + Wn (t))) &lt; / RTI &gt;

   = mean (sTE1 (t) -m1) * (sTE1 (t) -m1) + mean Wn (t) * Wn (t)

2 ? X2? 2? X2? 2 + X2? R2? 2? X2?

(TE2 (t) -m2) * (TE2 (t) - TE2 (t)) = mean

      (t) -m2 + Wn (t)) = mean ((sTE2 (t) - m2 + Wn

      = mean (sTE2 (t) -m2) * (sTE2 (t) -m2) + mean Wn (t) * Wn (t)

(t) * Wn (t)) + = mean (alpha * (sTE1 (t) -m1) * alpha * (sTE1

     (t) * wn (t)) = alpha2 * mean ((sTE1 (t) -m1) * (sTE1

= alpha ^ 2 * X1 ^ 2 + (X1 * R1) ^ 2 ~ = X2 ^ 2 + (X2 * R2) ^ 2

(TE1 (t), TE2 (t)) = CC (TE1 (t), TE2 (t)))

  2? X1? 2 + X1? R1 + 2? X1? 2 + X1? R1?

  = X1 ^ 4 * alpha ^ 2 / (X1 ^ 4 * (1 + R1 ^ 2) * (alpha ^ 2 + R1 ^ 2)

= alpha ^ 2 / (alpha ^ 2 + R1 ^ 2 * (1 + alpha ^ 2) + R1 ^ 4)

(1 + alpha ^ 2) + R1 ^ 4) = alpha ^ 2 (t)

(1 + alpha ^ 2) * R1 ^ 2 + alpha ^ 2 * (SS1 (t), TE2 (TE1 (t), TE2 (t)) - 1) = 0

put, P1 = SS (TE1 (t), TE2 (t)),

P2 = SS (TE1 (t), TE2 (t)) * (1 + alpha ^ 2)

P3 = alpha ^ 2 (SS (TE1 (t), TE2 (t)) - 1)

then, R1 ^ 2 = (- P2 + sqrt (P2 ^ 2-4P1P3)) / (2 * P1) (by 2nd order simultaneous equations) ----> equation ①

with the same method, P1 = SS (TE1 (t), TE2 (t)),

P2 = SS (TE1 (t), TE2 (t)) * (1 + beta2)

P3 = beta ^ 2 (SS (TE1 (t), TE2 (t)) - 1)

then, R2 ^ 2 = (- P2 + sqrt (P2 ^ 2-4P1P3)) / (2 * P1) (by 2nd order simultaneous equations) ----> equation ②

std (TE1 (t)) = std (sTE1 (t) + Wn (t))

            = sqrt (std (sTE1 (t)) ^ 2 + std (Wn (t)) ^ 2)

            = sqrt (X1 ^ 2 + (X1 * R1) ^ 2)

then, X1 = std (TE1 ( t)) / sqrt (1 + R1 ^ 2)) ----> equation ③

with same method, X2 = std ( TE2 (t)) / sqrt (1 + R2 ^ 2)) ----> equation ④

In the above equation (3), X1 is the standard deviation of pure signal components excluding noise in the TE1 data, and R1 is the ratio of the standard deviation of the noise to the X1. Similarly, in the above equation (4), X2 is the standard deviation of the pure signal components excluding noise in the TE2 data, and R2 is the ratio of the standard deviation of the noise to the X2. P1, P2, and P3 described in equations (1) and (2) can be calculated from the similarity (SS) between TE1 data and TE2 data, the average (m1) of TE1 data, , And the average (m2) of the TE2 data.

Next, T2 * deviation information of the ME-EPI data is estimated based on the standard deviations calculated for the pure signal components of at least two TE data (S30). The estimated computation of the T2 * deviation information performed at this stage may be performed by the microprocessor 130. [

First, the value obtained by dividing the standard deviation by the average for each TE data is defined as CV. That is, CV is defined as follows.

CV = coefficient of variance = (standard deviation / mean)

Assuming that each TE data contains no white noise, each TE data can be expressed by the following equation.

MRI (t) = S0 (t) * exp (-TE / T2 (t))

Based on the above definition of CV and the assumption for TE data, a series of arithmetic operations described below can be developed.

put, ET (t) = exp (-TE / T2 (t))

put, mean (S0 (t)) = ms

put, mean (ET (t)) = mt

put, S0 (t) - ms = ds (t)

put, ET (t) -mt = dt (t)

put, std (S0 (t)) = std (ds (t)) = ss

put, std (T2 (t)) = sdt (dt (t)) = st2

st, st = st, mt * TE * st2 / mean (T2 (t)) ^ 2

(t) = ET (t) = (ms + ds (t)) * (mt + dt

then, mean (MRI (t)) - = ms * mt

then, std (MRI (t) ) = std ((ms + ds (t)) * (mt + dt (t)))

                dt (t) + ds (t) * dt (t)) = std (ms * mt +

                = std (mt * ds (t) + ms * dt (t) + ds (t) * dt

                = sqrt (mt ^ 2 * ss ^ 2 + ms ^ 2 * st ^ 2 + st ^ 2 * ss ^ 2)

~ = sqrt (mt ^ 2 * ss ^ 2 + ms ^ 2 * st ^ 2)

Then, CV (TE) ^ 2 = std (MRI (t)) ^ 2 / mean (MRI (t)) ^ 2

           = (mt ^ 2 * ss ^ 2 + ms ^ 2 * st ^ 2) / (ms ^ 2 * mt ^ 2)

           = st ^ 2 / mt ^ 2 + ss ^ 2 / ms ^ 2

           = (mt * TE * st2 / mean (T2) ^ 2) ^ 2 / mt ^ 2 + ss ^ 2 / ms ^ 2

= (TE * st2) ^ 2 / mean (T2) ^ 4 + ss ^ 2 / ms ^ 2

2 * (st2 * ss * TE) / (ms * mean (T2) ^ 2)

CV (TE) = st2 * TE / mean (T2) ^ 2 + ss / ms--

In equation (5), which is the final formula according to the above calculation procedure, the coefficient of variance (CV) is the value obtained by dividing the standard deviation for each TE data by the average, as defined above. In Equation (5), st2 is the standard deviation of T2 (t), TE is the echo time, mean (T2) is the mean of T2 (t), ss is the standard deviation of S0 Is the average of S0 (t).

If CV and TE are variables in equation (5) above, CV is a linear function of TE. The slope in the first order graph is st2 / mean (T2) ^ 2, which is proportional to st2, the standard deviation of T2 (t), and the Y intercept is ss / ms, which is proportional to ss, which is the standard deviation of S0 (t).

Referring to FIG. 2 showing an example of such a linear function graph, the Y-axis variable CV (a value obtained by dividing the standard deviation of the TE data by the average thereof) with respect to the X-axis variable TE has a first-order function relationship.

The T2 * deviation information of ME-EPI data can be estimated based on the linear function relationship between TE and CV. This will be described in detail as follows.

The CV value for each TE data can be obtained by dividing the standard deviation of the pure signal components of each TE data calculated in step S20 by the average thereof. That is, CV1 = X1 / m1 and CV2 = X2 / m2. From this, two coordinates lying on the linear function graph can be obtained. That is, (TE1, CV1) and (TE2, CV2). From these two coordinates, the slope and the Y intercept of the above-mentioned first order function graph can be obtained. As mentioned above, the slope is proportional to the standard deviation of T2 (t) and the Y intercept is proportional to the standard deviation of S0 (t). Since the slope values obtained using the two coordinates (TE1, CV1) and (TE2, CV2) are proportional to the standard deviation of T2 (t), the slope value can be used as an index for indicating the T2 * deviation information. On the other hand, since the Y intercept value obtained by using the two coordinates (TE1, CV1) and (TE2, CV2) is proportional to the standard deviation of S2 (t), the Y intercept value can be used as an index representing the S0 deviation information. In the imaging process, the information of interest is T2 * deviation information, and the S0 deviation information is irrelevant to the neural activity of the brain.

Next, the functional image for the region of interest of the object is displayed through the intensity display proportional to the T2 * deviation information estimated in the previous step (S40). Such a functional image can be displayed by the display unit 150 described above according to an instruction of the microprocessor 130. [

Since the T2 * deviation information estimated in step S30 is an index related to T2 * related to neurofunctional activity of the region of interest (for example, the brain), the functional image for the region of interest is displayed in such a manner that the intensity corresponding to the index value is displayed .

FIG. 3 (b) shows an example of a brain functional image obtained by applying the fMRI imaging method of the present embodiment described above. FIG. 3 (a) shows a brain functional image according to the prior art using baseline epoch information Is shown.

The distribution of the neural functional activity in the functional image of FIG. 3 (b) is quite similar to the distribution of the neural functional activity of the brain in the functional image of FIG. 3 (a). Therefore, according to the fMRI imaging method according to the present embodiment, it is possible to obtain an image useful for the analysis of the neural functional activity of the brain without using the reference time zone information as in the prior art.

Furthermore, the fMRI imaging method according to the present invention has the following additional advantages as compared with the conventional fMRI imaging method.

Since the baseline epoch information is not required in the fMRI imaging method of the present invention, normal fMRI experiment and analysis can be performed even in the case where the setting of the reference stimulus is difficult or the information of the reference stimulus time period is difficult to grasp, Since hemodynamic response function modeling does not need to be applied, the problem of error generation can be solved.

In the fMRI imaging method of the present invention, only the T2 * deviation information directly related to the neural activity is excluded from the TE data and the signal components not related to the neural activity are reflected in the functional image The problem can be solved.

In the fMRI imaging method of the present invention, since deviation information on a pure signal from which noises are removed is used through correlation analysis between TE data, it is possible to obtain a white noise component according to the application of an exponential fitting technique. A problem that an estimation error can not be ignored can be solved.

In the fMRI imaging method of the present invention, the standard deviation of each data is calculated through the correlation analysis between the TE data, and the slope of the first-order function graph for the TE data and TE is calculated therefrom, and the functional image is displayed Therefore, the data operation procedure for functional image acquisition can be considerably simplified as compared with the prior art.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention as defined in the appended claims. It can be understood that it is possible.

100: System
110: MRI equipment
120: Memory
130: Microprocessor
140: User interface
150:

Claims (8)

(a) obtaining multi-echo echo planar imaging (ME-EPI) data for a region of interest of a subject;
(b) calculating a standard deviation of a pure signal component in which noise components are excluded for at least two TE data among a plurality of TE time data of the ME-EPI data;
(c) estimating T2 * deviation information of the ME-EPI data based on the calculated standard deviations; And
(d) displaying a functional image for a region of interest of the subject by an intensity indication proportional to the estimated T2 * variance information.
functional magnetic resonance imaging (fMRI) imaging.
The method according to claim 1,
In the step (b), the calculation of the standard deviation of the pure signal component is performed based on a signal similarity (SS) analysis between the TE data.
fMRI imaging method.
The method according to claim 1,
The step (c)
(c1) calculating a coefficient of variance (CV) for each TE data by dividing an average value of each TE data by the standard deviation value calculated for the at least two TE data; And
(c2) calculating a slope value of a linear function graph having the calculated dispersion coefficient value, wherein the dispersion coefficient is a Y-axis variable and TE is an X-axis variable.
fMRI imaging method.
The method of claim 3,
The functional image display in the step (d) is performed by applying the slope value calculated in the step (c2) as an index reflecting the T2 * deviation information.
fMRI imaging method.
MRI equipment for obtaining multi-echo echo planar imaging (ME-EPI) data for a region of interest of a subject;
Calculating a standard deviation of a pure signal component from which noise components are excluded for at least two TE data among a plurality of TE time data of the ME-EPI data,
A microprocessor for estimating T2 * deviation information of the ME-EPI data based on the calculated standard deviations; And
And a display unit for displaying a functional image for a region of interest of the object by an intensity display proportional to estimated T2 *
fMRI imaging system.
6. The method of claim 5,
Wherein the microprocessor performs a calculation of a standard deviation of the pure signal component based on a signal similarity (SS) analysis between TE data,
fMRI imaging system.
6. The method of claim 5,
The microprocessor,
Calculating a coefficient of variance (CV) for each TE data by dividing an average value of each TE data by the standard deviation value calculated for the at least two TE data, and calculating the dispersion coefficient Is the Y-axis variable and TE is the X-axis variable, and uses the slope value as the estimated value of the T2 * deviation information of the ME-EPI data.
fMRI imaging system.
8. The method of claim 7,
The display unit displays the functional image for the region of interest of the target object using the slope value as an index reflecting the T2 *
fMRI imaging system.

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