KR20210155772A - Myelin mapping method using magnetic resonance imaging - Google Patents

Abstract

The present invention relates to a method for evaluating the myelin using magnetic resonance imaging. According to one embodiment of the present invention, the method comprises the following steps: applying a plurality of inverted RF pulses in one sequence, acquiring a plurality of echo signals by giving a plurality of echo times to each RF pulse, and acquiring a plurality of echo images therefrom; acquiring a first image from the plurality of echo images; and forming a second image capable of evaluating the myelin on the basis of the first image. Accordingly, images can be acquired in a short time, high-resolution images can be acquired, and objective numerical information can be acquired, thereby providing an effect of enabling quantitative evaluation of the myelin. A qualitative myelin map and a quantitative myelin map can be acquired at the same time, thereby providing an advantage of implementing multi-faceted analysis of the myelin.

Description

Myelin mapping method using magnetic resonance imaging

The present invention relates to a method for evaluating nerve myelination using magnetic resonance imaging.

Myelin, a fatty substance that surrounds the axons of nerve cells, is an essential component for maintaining efficient brain function. Myelin maintains the integrity of nerve fibers and improves the rate of propagation of action potentials essential for the proper functioning of the brain. When this myelin is damaged, it interferes with the normal nerve transmission of nerves and causes various symptoms in sense, cognition, and behavior. Therefore, the accurate evaluation of Myelin is based on multiple sclerosis (MS), NeuroMyelitis Optica Spectrum Disorder (NMOSD), Traumatic Brain Injury (TBI), Alzheimer's Disease (AD), etc. considered important in disability.

Myelin can be quantitatively evaluated histopathologically, but since it can only be measured post-mortem, a number of studies on non-invasive myelin evaluation methods using MRI have been recently conducted. Current methods that usually used in the clinical study was a T1-weighted images taken with a 3T MRI (T1 Weighted Image; T1WI ) and T2 ^* weighted images (T2 ^* Weighted Image; T2 ^* WI) and Myelin Mapping Technique Using, already obtained in the clinical It has a great advantage in that it uses one general T1WI, T2 ^{* WI.} Myelin can check the level of Myelin by T1WI, T2 ^* WI. The spatial distribution of Myelin can be known through T1WI, where the fat component is bright, and the low intensity of ^{T2 * WI, which is related to proton transfer, molecular exchange, and water diffusion.} reflects the relatively large Myelin content. ^{By dividing T2 *} WI from this T1WI, a change in Myelin content and a structural change according to inflammation can be confirmed. However, the highlighted images only show similar values and have a limitation in that accurate quantitative information cannot be known.

Although some studies are being conducted to overcome the qualitative limitations, there are limitations in that it is difficult to apply various Myelin evaluation methods, excessively increasing the shooting time, or additional work for image correction and registration. the current situation.

Accordingly, a new method of Myelin evaluation is required.

Republic of Korea Patent Registration No. 10-1771513

Ganzetti, M., Wenderoth, N., & Mantini, D. (2014). Whole brain myelin mapping using T1-and T2-weighted MR imaging data. Frontiers in human neuroscience, 8, 671

It is an object of one aspect of the present invention to provide a new method for evaluating myelin nerve myelination in which a qualitative Myelin Map and a quantitative Myelin Map are simultaneously obtained, and a multi-faceted analysis of Myelin is possible by comparing them with each other.

In order to achieve the above object, in one aspect of the present invention

In a method for evaluating nerve myelin using magnetic resonance imaging,

applying a plurality of inverted RF pulses in one sequence, obtaining a plurality of echo signals by giving a plurality of echo times to each RF pulse, and obtaining a plurality of echo images therefrom;

obtaining a first image from the plurality of echo images; and

forming a second image capable of evaluating neural myelination based on the first image;

There is provided a method for evaluating nerve myelination comprising a.

In addition, in another aspect of the present invention

As a neural myelination evaluation system using magnetic resonance imaging,

an echo image acquisition unit that applies an inverted RF pulse a plurality of times in one sequence, obtains a plurality of echo signals by giving a plurality of echo times to each RF pulse, and obtains a plurality of echo images therefrom;

a first image acquisition unit configured to acquire a first image from the plurality of echo images; and

a second image acquisition unit configured to form a second image capable of evaluating neural myelination based on the first image;

A neural myelination evaluation system comprising a is provided.

Furthermore, in another aspect of the present invention

In a computer program combined with a processor and stored in a computer-readable recording medium,

The computer program is executed in the processor by instructions stored in the recording medium,

The processor is

a first operation of applying inverted RF pulses a plurality of times within one sequence, obtaining a plurality of echo signals by giving a plurality of echo times to each RF pulse, and obtaining a plurality of echo images therefrom;

a second operation of acquiring a first image from the plurality of echo images; and

a third operation of forming a second image capable of evaluating neural myelination based on the first image;

There is provided a neural myelination evaluation program using magnetic resonance imaging, including a.

The neural myelination evaluation method provided in one aspect of the present invention has the effect that it is possible to acquire an image within a short time, obtain a high-resolution image, and obtain objective numerical information, thereby enabling quantitative evaluation of neural myelination.

In addition, by enabling the simultaneous acquisition of a qualitative Myelin Map and a Quantitative Myelin Map, there is an advantage that multi-faceted analysis of Myelin is possible.

1 schematically shows a flowchart of a neural myelination evaluation method according to an embodiment of the present invention;
2 is a comparison of the mapping images according to the magnetic field strength of the magnetic resonance image used in the evaluation of nerve myelination according to an embodiment of the present invention;
3 is a schematic view by comparing the sequence of a comparative example and an embodiment of the present invention,
4 schematically shows an image processing and correction process when evaluating neural myelination according to an embodiment of the present invention;
5 schematically shows an image processing process when using the sequence of another comparative example of the present invention;
6 schematically shows the process of obtaining various images when using a sequence according to an embodiment of the present invention;
7 is a graph showing an example of finally evaluating and analyzing nerve myelination when evaluating nerve myelination according to an embodiment of the present invention.

Since the present invention can have various changes and can have various embodiments, specific embodiments will be described in detail with reference to the drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the present invention. In describing each figure, like reference numerals have been used for like elements.

Terms such as first, second, A, and B may be used to describe various elements, but the elements should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, a first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component. and/or includes a combination of a plurality of related description items or any of a plurality of related description items.

When a component is referred to as being “connected” or “connected” to another component, it may be directly connected or connected to the other component, but it should be understood that other components may exist in between. something to do. On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

It should also be understood that the terms "first" and "second" are used herein for distinguishing purposes only, and are not meant to indicate or anticipate sequence or priority in any way.

Unless defined otherwise, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. does not

Throughout the specification and claims, when a part includes a certain element, it means that other elements may be further included, rather than excluding other elements, unless specifically stated to the contrary.

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

In one aspect of the invention

In a method for evaluating nerve myelin using magnetic resonance imaging,

applying a plurality of inverted RF pulses in one sequence, obtaining a plurality of echo signals by giving a plurality of echo times to each RF pulse, and obtaining a plurality of echo images therefrom;

obtaining a first image from the plurality of echo images; and

forming a second image capable of evaluating neural myelination based on the first image;

There is provided a method for evaluating nerve myelination comprising a.

First, the neural myelination evaluation method provided in one aspect of the present invention includes obtaining a plurality of echo images.

The step may be performed using a magnetic resonance imaging apparatus.

The magnetic resonance imaging apparatus may use a magnetic field of 3 T or more, preferably 4 T or more, and more preferably 7 T or more.

Accordingly, a more detailed analysis of the structure and distribution may be possible.

In the above step, the inverted RF pulse may be applied a plurality of times within one sequence.

In an embodiment, the inverted RF pulse may be applied twice.

The sequence in the above step may be a Magnetization Prepared 2 Rapid Gradient Echo (MP2RAGE) sequence.

In this case, one echo image is obtained by applying two inverted RF pulses, and a quantitative T1 map can be constructed based on this (FIG. 2).

The R1 Map (= 1/T1 Map) obtained by inverting the T1 Map can be used as an evaluation index for Myelin, and has the advantage of being able to accurately evaluate it because it has objective numerical information.

However, since only T1WI and T1 Map can be obtained using only the MP2RAGE sequence, there is a limitation that other Myelin evaluation methods such as ^{T2 *} Map, T1WI/T2 ^* WI, R1 Map/T2 ^{* Map, and SWI (QSM) cannot be applied.} have. Using yet another sequence, such as the ME-GRE by taking the T2 ^* weighted images, but to overcome the critical point, there is a disadvantage that the recording time is further action is required for the disadvantages with image compensation and matching that more than doubled.

Accordingly, in the above step, a plurality of echo times are given to each RF pulse to obtain a plurality of echo signals, and a plurality of echo images can be obtained therefrom.

In an embodiment, a Multi-Echo Magnetization Prepared 2 RApid Gradient Echo (ME-MP2RAGE) technique for acquiring a plurality of echo signals for each RF pulse may be used.

In this case, the above-described disadvantages can be solved while taking advantage of the advantages of MP2RAGE.

In more detail, since the T1 Map and the T2 ^* Map can be obtained at the same time through reconstruction through one-time image capture, the image acquisition time is short and the image matching process is unnecessary.

Referring to the embodiment of the present invention that can be confirmed in FIG. 3 , the ME-MP2RAGE sequence is different from the conventional imaging method in which one echo image is acquired after applying two inversion RF pulses, in the present invention, each given the four echo Time (TE) on Inversion was echo image and four of the 1 ^st Inversion echo image of four 2 ^nd Inversion so as to obtain a total of eight images simultaneously (FIG. 3).

Next, the neural myelination evaluation method provided in one aspect of the present invention includes acquiring a first image from the plurality of echo images.

The first image may include at least one selected from the group consisting of T1 Map, T2* Map, T1WI, and T2*WI.

Referring to the embodiment of the present invention, which can be confirmed in FIG. 6 , the T1 Map is configured by performing image signal estimation for quantitatively estimating the T1 value using the TE1 image of ^{1 st} inversion and the TE1 image of 2 ^{nd inversion.} and it may be the TE1, TE2, TE3, TE4 image of the 2 ^nd Inversion by estimating the T2 ^* value quantitatively configure the T2 ^* Map.

^{In addition, T1WI and T2 *} WI, which are a T1-weighted image and a T2-weighted image, may be created through the plurality of echo images described above. In general, T1WI has parameters of short TR and short TE. T1WI can be obtained by using TE1 or TE2 in ^{1st Inversion.} In addition, T2 ^* WI with parameters of the long TR and long TE may be constructed using TE3 or TE4 of the 2 ^nd Inversion (Fig. 6).

Next, the method for evaluating nerve myelination provided in one aspect of the present invention includes forming a second image capable of evaluating nerve myelination based on the first image.

The second image may include an image capable of quantitatively evaluating the distribution of the nerve myelin.

The second image may include at least one selected from the group consisting of R1 Map, R1 Map/T2 ^* Map, and T1WI/T2 ^{* WI Map.} In this case, the R1 Map means a 1/T1 Map in which the T1 Map is inverted.

^{When the method of dividing T2 *} WI from T1WI, which is one of the Myelin Mapping techniques generally used in clinical research, is used, there is a possibility that the result is always changed depending on the image acquisition parameters used to acquire the emphasized image. In other words, since the use of an emphasis image is a similar value and not a quantitatively determined objective number, the quantitative values of the R1 Map (= 1 / T1 Map) and T2 ^* Map images that can be supplemented are used in Equation 1 below. can be used to obtain the Myelin Map.

<Equation 1>

Myelin Map = R1 Map / T2* Map

In addition to the Myelin Map of Equation 1, neural myelination can be evaluated using images such as ^{R1 Map, R1 Map/T2 *} Map, and T1WI/T2 ^{* WI.}

That is, the second image may include both a qualitative myelination map and a quantitative myelination map. Accordingly, there is an advantage that a multi-faceted analysis of nerve myelination may be possible.

In addition, the neural myelination evaluation method provided in an aspect of the present invention may further include correcting the first image before forming the second image.

The step may include forming a mask image from one or more of the echo images; and masking the first image.

Through these steps, a first image in which noises are corrected may be obtained.

In one embodiment, 2 ^nd Inversion of TE1 images Brain Extraction, Smoothing, and through a series of processes of Thresholding form a mask image, the surrounding noise to the correction by masking them to T1 Map and T2 ^* Map T1 Map and T2 ^* A Map is obtained (FIG. 4).

If, as in the present invention, an inverted RF pulse is applied a plurality of times, but a plurality of echo times are given to each RF pulse to obtain a plurality of echo signals, and not a method of obtaining a plurality of echo images from the TE, but a MP2RAGE sequence. ^{When using the method of constructing a T1 Map by giving only one and constructing a T2 *} Map by giving n TEs as a ME-GRE sequence, there is a disadvantage in that an additional image matching process must be performed.

That is, in the case of shooting with different MR sequences, an additional image registration process must be performed in order to correct the mismatch due to movement or the problem of merging two images due to the use of a different imaging sequence, whereas In the case of the neural myelination evaluation method provided in one aspect of the present invention, since images are simultaneously captured in the same sequence, concerns about movement can be completely excluded.

Therefore, in the case of the neural myelination evaluation method provided in one aspect of the present invention, a separate expensive image processing process is unnecessary and an optimal result can be obtained by image processing using simple arithmetic operations.

In addition, the method for evaluating nerve myelination provided in an aspect of the present invention may further include evaluating the nerve myelination by acquiring two or more of the second images.

In an embodiment, the average, standard deviation, and distribution of nerve myelin content in the region of interest may be checked by using two or more second images.

In more detail, as shown in (a) of FIG. 7 , the content of the nerve myelin in the region of interest (ROI) is expressed in a graph so that the average, standard deviation, and distribution can be confirmed for each combination of the second images. can

In another embodiment, the neural myelin may be evaluated by checking a relationship between the plurality of second images.

In more detail, as can be seen in FIG. 7B , the relationship between the second images can be confirmed by forming a pair of a plurality of second images to represent a scatter plot.

Accordingly, it is possible to have the effect of being able to analyze the nerve myelination from multiple angles rather than a fragmentary analysis by indicating the ratio and distribution of the nerve myelination in a graph.

This change in various distributions can be used as important information to identify the characteristics of the patient group in which the nerve myelination is reduced, and can be used as data to analyze the difference from the normal group.

The neural myelination evaluation method provided in one aspect of the present invention obtains a plurality of echo signals at the same time for each pulse in a sequence of applying an inverted RF pulse a plurality of times to obtain a first image having an objective and quantitative value from a plurality of echo images At the same time, it is possible to reconstruct and finally create a second image of various combinations capable of evaluating neural myelination based on this.

This compensates for the limitation that only qualitative information of the prior art can be known, so that quantitative quantification is possible, and it also has the advantage of rapidly acquiring a high-resolution Myelin distribution image. In addition, by simultaneously checking and comparing various combinations of Myelin Map, it is possible to obtain the advantage of being able to analyze nerve myelination from multiple angles.

In another aspect of the invention

As a neural myelination evaluation system using magnetic resonance imaging,

an echo image acquisition unit that applies an inverted RF pulse a plurality of times in one sequence, obtains a plurality of echo signals by giving a plurality of echo times to each RF pulse, and obtains a plurality of echo images therefrom;

a first image acquisition unit configured to acquire a first image from the plurality of echo images; and

a second image acquisition unit configured to form a second image capable of evaluating neural myelination based on the first image;

A neural myelination evaluation system comprising a is provided.

In this case, the neural myelination evaluation system may further include a data processing unit for acquiring two or more of the second images to evaluate the neural myelination.

In addition to this, the neural myelination evaluation system provided in another aspect of the present invention is the same as that described in the neural myelination evaluation method, and thus the description thereof will not be repeated.

In another aspect of the invention

In a computer program combined with a processor and stored in a computer-readable recording medium,

The computer program is executed in the processor by instructions stored in the recording medium,

The processor is

a first operation of applying inverted RF pulses a plurality of times within one sequence, obtaining a plurality of echo signals by giving a plurality of echo times to each RF pulse, and obtaining a plurality of echo images therefrom;

a second operation of acquiring a first image from the plurality of echo images; and

a third operation of forming a second image capable of evaluating neural myelination based on the first image;

There is provided a neural myelination evaluation program using magnetic resonance imaging, including a.

In this case, the processor further includes a fourth operation of acquiring two or more of the second images to evaluate the neural myelination.

In addition to this, the neural myelination evaluation program provided in another aspect of the present invention is the same as that described in the neural myelination evaluation method, and thus the description thereof will not be repeated.

Hereinafter, the present invention will be described in more detail through Examples, Comparative Examples and Experimental Examples. The scope of the present invention is not limited to specific embodiments, and should be construed according to the appended claims. In addition, those skilled in the art will understand that many modifications and variations are possible without departing from the scope of the present invention.

<Example 1>

Four echo times (Echo Time, TE) are given to each inversion RF pulse to obtain a total of eight images simultaneously with four echo images of the first inversion and four echo images of the second inversion (FIG. 3). At this time, image signal evaluation (Estimation) that quantitatively estimates the T1 value is performed using the TE1 image of the first inversion and the TE1 image of the second inversion to configure the T1 Map, and the TE1, TE2, TE3, TE4 images of the second inversion ^{T2 *} Map is constructed by quantitatively estimating the T2 ^{* value.}

As the MRI equipment, a 7 T ultra-high magnetic field MRI equipment was used.

In addition, a mask image was made with the TE1 image of the secondary inversion, and the estimated T1 MAP and T2 ^* Map were masked to obtain a corrected T1 Map and T2 ^* Map (FIG. 4).

In addition, as shown in FIG. 6 , T1 Map and T2 ^* Map can be configured ^{with the obtained 8 images, and T1WI and T2 *} WI can be created.

In this case, in general, T1WI has parameters of short TR and short TE. T1WI can be obtained by using TE1 or TE2 in ^{1st Inversion.} In addition, T2 ^* WI with parameters of the long TR and long TE may be constructed using TE3 or TE4 of the 2 ^nd Inversion (Fig. 6). ^{However, when the method of dividing T2 *} WI from T1WI, which is one of the Myelin mapping techniques used in clinical research, is used, there is a possibility that the result will always change depending on the image acquisition parameters used to acquire the emphasized image.

That is, since using the emphasis image is a similar value and not a quantitatively determined objective value, it is necessary to use a quantitative value that can compensate for this. In Example 1, R1 Map (= 1 / T1 Map) and T2 ^* Using the quantitative value of the map image, the Myelin Map was obtained by Equation 1.

<Equation 1>

Myelin Map = R1 Map / T2* Map

<Comparative Example 1>

Was used for MRI equipment of 3 T, to each other after the acquisition by each image processing for quantitative T1 images and T2 ^* images using the other two MR sequence, using a ratio (T1 / T2 ^*) of the two images myelin (myelinated ) information was extracted.

(Ganzetti, M., Wenderoth, N., & Mantini, D. (2014). Whole brain myelin mapping using T1-and T2-weighted MR imaging data. Frontiers in human neuroscience, 8, 671)

<Comparative Example 2>

Unlike Example 1, only the MP2RAGE sequence was used (FIG. 3).

<Comparative Example 3>

Unlike Example 1, only one TE was given as the MP2RAGE sequence to construct a T1 Map, and n TEs were given as the ME-GRE sequence to construct a ^{T2 * Map.}

<Experimental Example 1> Comparison of Example 1 and Comparative Example 1

The left image of FIG. 2 shows the myelin image obtained in Comparative Example 1, and the right image is Example 1. The image of Comparative Example 1 is from Ganzetti, M., Wenderoth, N., & Mantini, D. (2014). Whole brain myelin mapping using T1-and T2-weighted MR imaging data. Images obtained from Frontiers in human neuroscience , 8, 671.

As can be seen in FIG. 2 , in the case of Example 1, compared to Comparative Example 1, a high-resolution image can be obtained by imaging with a high magnetic field MRI apparatus, and as a result, detailed analysis is possible.

<Experimental Example 2> Comparison of Example 1 and Comparative Example 2

In Comparative Example 2, since the MP2RAGE sequence is used, two inverted RF pulses are applied to obtain one echo image, respectively, and a quantitative T1 Map can be constructed based on this.

However, Comparative Example 2, the case of using only MP2RAGE sequence as shown, because it can only get T1WI and T1 Map, T2 ^* Map, T1WI / T2 ^* WI, R1 Map / T2 ^* Map, SWI other neural plants evaluated in the same way as (QSM) There is a limitation that it cannot be applied.

On the other hand, in the case of Example 1, as described above, T1WI, T2 ^* WI, T1 Map, and T2 ^* Map can be obtained using the eight images obtained by the ME-MP2RAGE technique as described above. can be obtained As such, the images obtained by the ME-MP2RAGE technique are highly useful, so most of the Myelin Mapping methods used in clinical studies such ^{as T2 *} Map, T1WI/T2WI, SWI (QSM) as well as ^{R1 Map/T2 * Map described above} can be used to obtain a Myelin Map (FIG. 1).

Therefore, in the case of Example 1, it is possible to obtain a qualitative Myelin Map and a quantitative Myelin Map at the same time, and compare them with each other to perform multi-faceted analysis on Myelin.

Therefore, it is much more preferable to apply the sequence of Example 1 than that of Comparative Example 2.

<Experimental Example 3> Comparison of Example 1 and Comparative Example 3

In the case of Comparative Example 3, since the MP2RAGE sequence and the ME-GRE sequence were used, different MR sequences were used to capture the images, so there is a problem in that the two images have to be merged due to inconsistency due to motion or using a different imaging sequence. . Therefore, in order to correct this, an additional image registration process must be performed (FIG. 5).

On the other hand, in the case of Example 1, since images are simultaneously captured in the same sequence, concerns about motion can be completely excluded, which eliminates the need for a separate high-cost image processing process and provides optimal results by image processing using simple arithmetic operations. can be obtained.

Therefore, it is much more preferable to apply the sequence of Example 1 than that of Comparative Example 3.

<Experimental Example 4> Multi-faceted analysis using Example 1

As can be seen in FIG. 7 , a region of interest (ROI) was set in the Myelin Map obtained in Example 1.

Thereafter, the average, standard deviation, and distribution for each combination of the Myelin content in the region of interest were graphed and shown (FIG. 7(a)).

In addition, each combination is shown as a graph so that the relationship between the two Myelin Maps can be confirmed by showing a scatter plot by forming a pair (FIG. 7 (b)).

That is, in the case of Example 1, by showing the ratio and distribution of Myelin in a graph, it has the effect of being able to analyze Myelin from multiple angles rather than a fragmentary analysis, and the change in these various distributions is to identify the characteristics of the patient group in which Myelin is reduced. It can be used as important information for this purpose, and it can be used as data to analyze the difference from the normal group.

Claims (10)

In a method for evaluating nerve myelin using magnetic resonance imaging,
applying a plurality of inverted RF pulses in one sequence, obtaining a plurality of echo signals by giving a plurality of echo times to each RF pulse, and obtaining a plurality of echo images therefrom;
obtaining a first image from the plurality of echo images; and
forming a second image capable of evaluating neural myelination based on the first image;
Neuromyelination evaluation method comprising a.
According to claim 1,
The first image is a neural myelination evaluation method, characterized in that it comprises at least one selected from the group consisting of T1 Map, T2 ^* Map, T1WI and T2 ^{* WI.}
According to claim 1,
The second image is a nerve myelination evaluation method, characterized in that it includes an image capable of quantitatively evaluating the distribution of the nerve myelination.
3. The method of claim 2,
The second image is a neural myelination evaluation method comprising at least one selected from the group consisting of R1 Map, R1 Map/T2 ^* Map, and T1WI/T2 ^{* WI Map (where R1 Map is 1/T1 Map)} ).
According to claim 1,
The neural myelination evaluation method further comprises the step of correcting the first image before the formation of the second image.
According to claim 1,
The nerve myelination evaluation method is
evaluating the neural myelin by acquiring two or more of the second images;
Nerve myelination evaluation method, characterized in that it further comprises.
As a neural myelination evaluation system using magnetic resonance imaging,
an echo image acquisition unit that applies an inverted RF pulse a plurality of times in one sequence, obtains a plurality of echo signals by giving a plurality of echo times to each RF pulse, and obtains a plurality of echo images therefrom;
a first image acquisition unit configured to acquire a first image from the plurality of echo images; and
a second image acquisition unit configured to form a second image capable of evaluating neural myelination based on the first image;
A neural myelination evaluation system comprising a.
8. The method of claim 7,
The neural myelination evaluation system is
a data processing unit that acquires two or more second images to evaluate neural myelination;
Neural myelination evaluation system, characterized in that it further comprises.
In a computer program combined with a processor and stored in a computer-readable recording medium,
The computer program is executed in the processor by instructions stored in the recording medium,
The processor is
a first operation of applying inverted RF pulses a plurality of times within one sequence, obtaining a plurality of echo signals by giving a plurality of echo times to each RF pulse, and obtaining a plurality of echo images therefrom;
a second operation of acquiring a first image from the plurality of echo images; and
a third operation of forming a second image capable of evaluating neural myelination based on the first image;
A neural myelination evaluation program using magnetic resonance imaging, including a.
10. The method of claim 9,
The processor is
A fourth operation of evaluating neural myelination by acquiring two or more second images.

Images