CN104107045B - MR imaging method and device - Google Patents

Abstract

The embodiment of the invention discloses a kind of MR imaging method.The method includes: original image carries out plural number high-pass filtering, it is thus achieved that filtered image；From filtered image, obtain view data, the imaginary numbers in described view data is carried out segmentation, and the real part numerical value in the described view data in each segmentation limit is carried out different normalizeds；Numerical value after processing is multiplied as weighted value with the gray value of magnitude image, finally gives MRI.According to embodiments of the present invention, can be while ensureing image definition, it is provided that one MR imaging method quickly and easily.Solve the problem that in prior art, magnetic susceptibility weighted image processing procedure is excessively complicated.The embodiment of the present invention additionally provides a kind of MR imaging apparatus.

Description

Magnetic resonance imaging method and apparatus

Technical Field

The present invention relates to the field of medical image processing, and in particular to a magnetic resonance imaging method and apparatus.

Background

The magnetic resonance Weighted Imaging (SWI) technique is a magnetic resonance contrast enhanced Imaging technique that has been newly developed in recent years. Unlike previous proton density, T1, or T2 weighted imaging, SWI provides image contrast enhancement based on differences in magnetic susceptibility (which can be quantified as susceptibility) between different tissues.

The research finds that because the main component of venous blood is paramagnetic deoxyhemoglobin and the main component of arterial blood is diamagnetic oxyhemoglobin, the magnetic sensitivity difference exists between the venous blood vessel and the arterial blood vessel, and the difference can finally cause the signal intensity of the two blood vessels to be different, thereby providing the possibility for clearly imaging the venous blood vessel independently of the arterial blood vessel. In clinical application, the SWI technology can be applied to the research of brain tumor, cerebral hemorrhage or other lesions related to vein and blood vessel.

At present, the SWI image cannot be directly obtained by the existing magnetic resonance imaging apparatus, but the original image (i.e., the actually detected image) obtained based on the T2 × weighted gradient echo sequence needs to be subjected to complicated image processing to obtain the SWI image.

In the prior art, an image processing method for obtaining an SWI image is provided, and the processing flow thereof is as follows: firstly, generating a phase image and an amplitude image (or called a magnetic distance image) according to original image data; then, high-pass filtering is carried out on the phase image to remove low-frequency disturbance caused by the nonuniformity of the background magnetic field, and a filtered phase image is obtained; calculating a phase image mask (or phase mask) using the filtered phase image; and finally, applying the phase image mask to the amplitude image for n times to obtain a final SWI image.

In the process of implementing the invention, the inventor of the invention finds that at least the following problems exist in the prior art: because the existing image processing method needs to calculate a phase image and calculate the nth power of a phase mask according to the phase image, and the value of the power exponent n is determined by the signal-to-noise ratio of the image (i.e., the value of n is different under different signal-to-noise ratios of the image), the signal-to-noise ratio of the image needs to be determined in advance in the image processing process, thereby increasing the complexity of the whole image processing process. Moreover, the calculation of the nth power itself makes the whole image processing process relatively cumbersome.

Disclosure of Invention

In order to solve the above technical problems, embodiments of the present invention provide a magnetic resonance imaging method and apparatus, so as to solve the problem that an image processing process is too complicated in the prior art while ensuring image sharpness.

The embodiment of the invention discloses the following technical scheme:

a magnetic resonance imaging method, comprising:

carrying out complex high-pass filtering on the original image to obtain a filtered image;

acquiring image data from the filtered image, segmenting imaginary part numerical values in the image data, and performing different normalization processing on real part numerical values in the image data in each segmentation range;

and multiplying the processed numerical value serving as a weight value by the gray value of the amplitude image to finally obtain the magnetic resonance image.

Preferably, the method further comprises:

and performing minimum signal intensity projection on the obtained magnetic resonance images of all the layers.

Preferably, the complex high-pass filtering is homomorphic high-pass filtering.

Preferably, the obtaining image data from the filtered image, segmenting imaginary part values in the image data, and performing different normalization processes on real part values in the image data in each segmentation range includes:

normalizing a real part value in the image data to 1 when an imaginary part value in the image data is greater than or equal to 0; normalizing real values in the image data to values between 0 and 1 when the imaginary values are less than 0;

or,

normalizing a real part value in the image data to a value between 0 and 1 when an imaginary part value in the image data is greater than or equal to 0; normalizing the real part value in the image data to 1 when the imaginary part value is less than 0;

or,

directly normalizing real part values in the image data to values between 0 and 1.

Further preferably, the normalizing the real part value in the image data to a value between 0 and 1 is specifically:

according toNormalizing real part values in the image data to values between 0 and 1;

wherein W (R) is a processed value, R (rho)_HF(r)) is a real part value.

A magnetic resonance imaging apparatus comprising:

the high-pass filtering unit is used for carrying out complex high-pass filtering on the original image to obtain a filtered image;

the normalization processing unit is used for acquiring image data from the filtered image, segmenting imaginary part numerical values in the image data and carrying out different normalization processing on real part numerical values in the image data in each segmentation range;

and the weighting calculation unit is used for multiplying the processed numerical value serving as a weight value with the gray value of the amplitude image to obtain the magnetic resonance image.

Preferably, the apparatus further comprises:

and the projection unit is used for performing minimum signal intensity projection on the obtained magnetic resonance images of all the layers.

Preferably, the complex high-pass filtering is homomorphic high-pass filtering.

Preferably, the normalization processing unit includes:

a first setting subunit, configured to normalize a real part value in the image data to 1 when an imaginary part value in the image data is greater than or equal to 0;

a first transformation subunit, configured to normalize a real part value in the image data to a value between 0 and 1 when an imaginary part value in the image data is smaller than 0;

or,

the normalization processing unit includes:

a second setting subunit, configured to normalize the real part value in the image data to 1 when the imaginary part value in the image data is smaller than 0;

a second transformation subunit, configured to normalize the real part value in the image data to a value between 0 and 1 when the imaginary part value in the image data is greater than or equal to 0;

or,

the normalization processing unit includes:

a third transformation subunit directly normalizing real part values of the image data to values between 0 and 1.

Further preferably, the first transformation sub-unit, the second transformation sub-unit or the third transformation sub-unit is specifically configured to,

according toNormalizing real part values in the image data to values between 0 and 1;

wherein W (r) is treatmentThe latter value, R (ρ)_HF(r)) is a real part value.

It can be seen from the above embodiments that, compared with the prior art, the technical solution of the present invention has the advantages that:

different normalization processes are carried out on the filtered image data of the original image, and the processed weight function and the power exponent function (namely (phi) of the phase mask in the prior art are utilized_MASK(r))ⁿ) The processed weight function replaces the power exponent function of the phase mask in the prior art. The processed weight function is not changed along with the change of the signal-to-noise ratio of the image, so that the stability is good. Therefore, when the processed weight function is adopted to replace the power exponent function of the phase mask, the phase image does not need to be calculated in the image processing process, and the power does not need to be determined through the signal-to-noise ratio of the image, so that the whole image processing process is simplified. Moreover, the calculation of the nth power is not needed, and the whole image processing process is relatively simple and easy.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

Fig. 1 is a flowchart of a magnetic resonance imaging method according to an embodiment of the present invention;

FIG. 2 is a similar comparison of the weight curve of the present invention and the power curve of the prior art;

FIG. 3 is another similar comparison of the weight curve of the present invention and a prior art power curve;

FIG. 4 is another similar comparison of the weight curve of the present invention and a prior art power curve;

fig. 5 is a flowchart of a magnetic resonance imaging method according to a second embodiment of the present invention;

fig. 6 is a block diagram of a magnetic resonance imaging apparatus according to a third embodiment of the present invention;

fig. 7 is a block diagram of another magnetic resonance imaging apparatus according to a third embodiment of the present invention;

FIG. 8 is a block diagram of a normalization processing unit according to the present invention;

fig. 9(a) - (b) are the original image and the processed image of the present invention, respectively.

Detailed Description

In order to remove low-frequency phase interference caused by nonuniform background magnetic field and further enhance the magnetic sensitivity contrast between tissues so as to more clearly display the anatomical structure, a series of post-processing needs to be carried out on the original image.

In the related art, a phase image and a magnitude image are obtained from image data of an original image (the image data is a complex number), respectively. Wherein,

the phase image is:

φ(r)＝arctan(I(r)/R(r))

the amplitude image is:

${\mathrm{\ρ}}_m\left(r\right)=\sqrt{(R{\left(r\right)}^2+I{\left(r\right)}^2)}$

i (r) and r (r) are the imaginary and real parts of the image data, respectively, and r is the phase value.

While conventional Magnetic Resonance Imaging (MRI) utilizes only single magnitude information, the SWI technique utilizes phase information that has been ignored and applies the phase image to the magnitude image through a series of image processing to form a unique image contrast enhancement. The processing process comprises the following steps:

1. and high-pass filtering is carried out on the phase image to remove low-frequency disturbance caused by nonuniform background magnetic field.

For example, the phase image is first low-pass filtered, and then in the complex domain, the original image is divided by the low-pass filtered k-space data, resulting in a high-pass filtered phase image.

2. Then, the filtered phase image is normalized to generate a phase mask phi_MASK(r)。

3. And finally, generating an SWI image according to the phase mask and the amplitude image:

ρ_SWI(r)＝(φ_MASK(r))ⁿ×ρ_m(r), n determines the weight, and generally, an image with a high signal-to-noise ratio can be obtained by taking 3 to 6 as n.

The inventor of the present invention found in the research that, after the original image is processed by complex high-pass filtering, if the real part value in the image data of the processed image is respectively normalized according to the segmentation of the imaginary part value, the weight function obtained after the processing and the power exponent function of the phase mask (i.e., (phi) are respectively performed_MASK(r))ⁿ) The method is very approximate, and the weight function is not changed along with the change of the image signal-to-noise ratio, so that the stability is good. Therefore, when the weighting function is used for replacing the power exponent function of the phase mask, the phase image and the phase mask do not need to be calculated in the image processing process, and the signal-to-noise ratio of the image is determined, so that the phase image and the phase mask do not need to be calculated, and the signal-to-noise ratio of the image is determined, therebyThe whole image processing process is simplified. Moreover, the calculation of the nth power is not needed, and the whole image processing process is relatively simple and easy.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.

Example one

Please refer to fig. 1, which is a flowchart illustrating a magnetic resonance imaging method according to an embodiment of the present invention, the method including the following steps:

step 101: and carrying out complex high-pass filtering on the original image to obtain a filtered image.

In order to eliminate low-frequency disturbance caused by non-uniformity of a background magnetic field and obtain a clearer image, high-pass filtering needs to be performed on an original image. Since the image data in the original image is complex, the high-pass filtering is complex high-pass filtering.

In a preferred embodiment of the invention, homomorphic high-pass filtering may be used to implement complex form high-pass filtering.

For example, the original image is ρ (r), and the homomorphic high-pass filtered image of the original image is:

ρ_HF(r)＝exp(FFT^-1(Han(r)×FFT(ln(ρ(r)))))

where ρ is_HF(r) is image data of the high-pass filtered image, Han (r) is a Hanning function, $\mathrm{Han}\left(r\right)=0.5\×(1-\cos \left(2\mathrm{\π}\frac{r}{N}\right)),0\≤r\≤N.$

step 102: obtaining image data from the filtered image, segmenting imaginary part numerical values in the image data, and performing different normalization processing on real part numerical values in the image data in each segmentation range.

Since the phase values of paramagnetic substances (such as vein vessels) in the phase image are obviously negative, while the phase values of most brain parenchyma and cerebrospinal fluid are usually positive or less negative, in the prior art, it is necessary to calculate a phase mask (or phase mask) for the phase image and multiply the nth power of the phase mask by the amplitude of the amplitude image, so as to highlight the visibility of small structures.

Since the imaginary value in the high-pass filtered image data includes information of sine function of the phase value, and the real part includes information of cosine function of the phase value, when the phase value of the paramagnetic substance (such as vein) shows a significant negative value, the imaginary value in the image data of the part of the tissue also shows a significant negative value, and when the phase value of most of the brain parenchyma and cerebrospinal fluid, etc. usually shows a positive value or a smaller negative value, the imaginary value in the image data of the part of the tissue also shows a positive value accordingly. That is, the imaginary part values of the image data of different tissues are different. Therefore, the imaginary part values in the obtained image data can be segmented by using the characteristic that the imaginary part values of the image data of different tissues are different, and after segmentation, the imaginary part values in the same segment are the imaginary part values of the image data of the same tissue. And then carrying out different normalization processing on the real part numerical value in the image data in each segmentation range. Namely, for the real part numerical values of the image data of different tissues, a differentiation normalization processing mode is adopted, so that the effect of differentiation display of different tissues is finally achieved.

How the different normalization processes are performed will be described in detail later.

Step 103: and multiplying the processed numerical value serving as a weight value by the gray value of the amplitude image to finally obtain the magnetic resonance image.

It is clear that the finally obtained magnetic resonance image can highlight small structures in the original image, such as venous vessels.

In the prior art, there are generally three methods for generating a phase mask by performing mask processing on a phase image.

The first method comprises the following steps: when the phase value is in [0, pi ]]In between, set the phase value to 1; when the phase value is between-pi, 0), the phase value is transformed to a value between 0 and 1, e.g.,phi (r) is the phase value.

The second method comprises the following steps: setting the phase value to 1 when the phase value is between [ -pi, 0); when the phase value is between 0, pi, the phase value is transformed to a value between 0 and 1.

The third method comprises the following steps: when the phase value is between [ -pi, pi ], the phase value is transformed to a value between 0 and 1.

Corresponding to the three methods, the different normalization processing manners in the step 102 may be:

a first method of normalizing a real part value in the image data to 1 when an imaginary part value in the image data is greater than or equal to 0; normalizing real values in the image data to values between 0 and 1 when imaginary values are less than 0;

a second method of normalizing real values in the image data to a value between 0 and 1 when the imaginary values in the image data are greater than or equal to 0; normalizing the real value to 1 in the image data when the imaginary value is less than 0.

A third method, directly normalizing real part values in the image data to values between 0 and 1.

In a preferred embodiment of the present invention, normalizing the real part values in the image data to values between 0 and 1 is specifically:

according toNormalizing real part values in the image data to values between 0 and 1;

wherein W (R) is a normalized value, R (rho)_HF(r)) is a real part value.

For example, taking the first method as an example, the weight values after the segment normalization processing are:

$W\left(r\right)=\left\{\begin{array}{cc}1& I\left({\mathrm{\&rho;}}_{\mathrm{HF}}\left(r\right)\right)\&GreaterEqual;0)\\ 1-\sqrt{\frac{1-R\left({\mathrm{\&rho;}}_{\mathrm{HF}}\left(r\right)\right)}{2}}& I\left({\mathrm{\&rho;}}_{\mathrm{HF}}\left(r\right)\right)<0\end{array}\right.$

where ρ is_HF(r) image data of the high-pass filtered image, I (ρ)_HF(R)) is the imaginary value of the image data, R (ρ)_HF(r)) is a real part value of the image data.

Finally, taking W (r) after the segmented normalization processing as a weight value and the amplitude image rho_M(r) performing point-to-point multiplication to obtain a magnetic resonance image highlighting the small structure:

${\mathrm{\&rho;}}_{\mathrm{HFI}}={\mathrm{\&rho;}}_M\left(r\right)\&times;W\left(r\right)=\left\{\begin{array}{cc}{\mathrm{\&rho;}}_M\left(r\right)& I\left({\mathrm{\&rho;}}_{\mathrm{HF}}\left(r\right)\right)\&GreaterEqual;0)\\ {\mathrm{\&rho;}}_M\left(r\right)\&times;(1-\sqrt{\frac{1-R\left({\mathrm{\&rho;}}_{\mathrm{HF}}\left(r\right)\right)}{2}})& I\left({\mathrm{\&rho;}}_{\mathrm{HF}}\left(r\right)\right)<0\end{array}\right.$

when the formula is used for transformation processing, a similar comparison graph of the weight curve of the invention and the power exponent curve of the prior art is shown in fig. 2 for a first normalization processing method, a similar comparison graph of the weight curve of the invention and the power exponent curve of the prior art is shown in fig. 3 for a second normalization processing method, and a similar comparison graph of the weight curve of the invention and the power exponent curve of the prior art is shown in fig. 4 for a third normalization processing method. Where line 1 is a power-of-1 function of the phase mask, line 2 is a sine function consisting of the normalized imaginary value, and line 3 is a power-of-4 function of the phase mask.

It can be seen from the above embodiments that, compared with the prior art, the technical solution of the present invention has the advantages that:

taking the imaginary part value of the image data in the filtered image as the segmentation basis of the image data, carrying out different normalization processing on the image data by using the real part value, and using the processed weight function and the power exponent function (namely (phi) of the phase mask in the prior art_MASK(r))ⁿ) The processed weights replace the power exponential function of the phase mask in the prior art. The processed weight function is not changed along with the change of the signal-to-noise ratio of the image, so that the stability is good. Therefore, when the processed weight function is adopted to replace the power exponent function of the phase mask, the phase image and the phase mask do not need to be calculated in the image processing process, and the power does not need to be determined through the signal-to-noise ratio of the image, so that the whole image processing process is simplified. Moreover, the calculation of the nth power is not needed, and the whole image processing process is relatively simple and easy.

Example two

By the magnetic resonance imaging method in the first embodiment, magnetic resonance images of the respective slices can be obtained. In order to further make the magnetic sensitivity signals of the vein vessels scattered on each slice continuous and finally display the continuous vein vessel structure, the second embodiment performs the minimum signal intensity projection on the obtained magnetic resonance image of each slice on the basis of the first embodiment.

Please refer to fig. 5, which is a flowchart illustrating a magnetic resonance imaging method according to a second embodiment of the present invention, the method comprising the following steps:

step 501: and carrying out complex high-pass filtering on the original image to obtain a filtered image.

Step 502: obtaining image data from the filtered image, segmenting imaginary part numerical values in the image data, and performing different normalization processing on real part numerical values in the image data in each segmentation range.

Step 503: and multiplying the processed numerical value serving as a weight value by the gray value of the amplitude image to finally obtain the magnetic resonance image.

Step 504: and performing minimum signal intensity projection on the obtained magnetic resonance images of all the layers.

It can be seen from the above embodiments that, compared with the prior art, the technical solution of the present invention has the advantages that:

taking the imaginary part value of the image data in the filtered image as the segmentation basis of the image data, carrying out different normalization processing on the image data by using the real part value, and using the processed weight function and the power exponent function (namely (phi) of the phase mask in the prior art_MASK(r))ⁿ) The processed weights replace the power exponential function of the phase mask in the prior art. The processed weight function is not changed along with the change of the signal-to-noise ratio of the image, so that the stability is good. Therefore, when the processed weight function is adopted to replace the power exponent function of the phase mask, the phase image and the phase mask do not need to be calculated in the image processing process, and the power does not need to be determined through the signal-to-noise ratio of the image, so that the whole image processing process is simplified. Moreover, the calculation of the nth power is not needed, and the whole image processing process is relatively simple and easy.

EXAMPLE III

Corresponding to the magnetic resonance imaging method, the embodiment of the invention also provides a magnetic resonance imaging device. Please refer to fig. 6, which is a block diagram of a magnetic resonance imaging apparatus according to a third embodiment of the present invention, the apparatus includes: a high-pass filtering unit 601, a normalization processing unit 602, and a weight calculation unit 603. The internal structure and connection relationship of the device will be further described below in conjunction with the working principle of the device.

The high-pass filtering unit 601 is configured to perform complex high-pass filtering on the original image to obtain a filtered image.

A normalization processing unit 602, configured to obtain image data from the filtered image, segment an imaginary part value in the image data, and perform different normalization processing on a real part value in the image data in each segment range.

And a weighting calculation unit 603, configured to multiply the processed value as a weight value with a gray value of the amplitude image to obtain a magnetic resonance image.

In a preferred embodiment of the present invention, as shown in fig. 7, the apparatus further comprises:

a projection unit 604, configured to perform minimum signal intensity projection on the obtained magnetic resonance image of each slice.

In another preferred embodiment of the present invention, the complex high-pass filtering is homomorphic high-pass filtering.

In another preferred embodiment of the present invention, as shown in fig. 8, the normalization processing unit 602 includes:

a first setting subunit 6021, configured to normalize the real part value in the image data to 1 when the imaginary part value in the image data is greater than or equal to 0.

A first transformation subunit 6022 for normalizing the real part value in the image data to a value between 0 and 1 when the imaginary part value in the image data is less than 0.

As an alternative, the normalization processing unit 602 includes:

a second setting subunit, configured to normalize the real part value in the image data to 1 when the imaginary part value in the image data is smaller than 0.

A second transformation subunit, configured to normalize the real part value in the image data to a value between 0 and 1 when the imaginary part value in the image data is greater than or equal to 0.

Or, as another alternative, the normalization processing unit 602 includes:

a third transformation subunit directly normalizing real part values of the image data to values between 0 and 1.

In another preferred embodiment of the present invention, the first transform subunit, the second transform subunit or the third transform subunit is specifically adapted to,

according toNormalizing real part values in the image data to values between 0 and 1;

wherein W (R) is a processed value, R (rho)_HF(r)) is a real part value.

It can be seen from the above embodiments that, compared with the prior art, the technical solution of the present invention has the advantages that:

segmenting the imaginary part numerical value of the image data in the filtered image, obtaining weighted image data by utilizing different normalization processing of the real part numerical value, and utilizing the processed weight function and the power exponential function (namely (phi) of the phase mask in the prior art_MASK(r))ⁿ) The processed weights replace the power exponential function of the phase mask in the prior art. The processed weight function is not changed along with the change of the signal-to-noise ratio of the image, so that the stability is good. Therefore, when the processed weight function is adopted to replace the power exponent function of the phase mask, the phase image and the phase mask do not need to be calculated in the image processing process, and the power does not need to be determined through the signal-to-noise ratio of the image, so that the whole image processing process is simplified. Moreover, the calculation of the nth power is not needed,the whole image processing process is relatively simple and easy.

The technical scheme of the invention can obtain the effect of highlighting the small structure through the experimental data of healthy subjects. The result is shown in fig. 9, in which fig. 9(a) is the original image; fig. 9(b) is an image processed by the present method, highlighting the veins.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when the actual implementation is performed, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some interfaces, and may be in an electrical, mechanical or other form.

The units described as separate parts may be or may be physically separate, and parts displayed as units may be or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can be realized in a form of a software functional unit.

It should be noted that, as will be understood by those skilled in the art, all or part of the processes in the methods of the above embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.

The magnetic resonance imaging method and apparatus provided by the present invention are described in detail above, and the principles and embodiments of the present invention are explained herein by using specific embodiments, which are merely used to help understand the method and core ideas of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. A magnetic resonance imaging method, comprising:

carrying out complex high-pass filtering on the original image to obtain a filtered image;

acquiring image data from the filtered image, segmenting imaginary part numerical values in the image data, and performing different normalization processing on real part numerical values in the image data in each segmentation range;

and multiplying the processed numerical value serving as a weight value by the gray value of the amplitude image to finally obtain the magnetic resonance image.

2. The method of claim 1, further comprising:

and performing minimum signal intensity projection on the obtained magnetic resonance images of all the layers.

3. The method of claim 1, wherein the complex high-pass filtering is homomorphic high-pass filtering.

4. The method of claim 1, wherein the obtaining image data from the filtered image, segmenting imaginary values in the image data, and performing different normalization processes on real values in the image data within respective segmentation ranges comprises:

normalizing a real part value in the image data to 1 when an imaginary part value in the image data is greater than or equal to 0; normalizing real values in the image data to values between 0 and 1 when the imaginary values are less than 0;

or,

normalizing a real part value in the image data to a value between 0 and 1 when an imaginary part value in the image data is greater than or equal to 0; normalizing the real part value in the image data to 1 when the imaginary part value is less than 0;

or,

directly normalizing real part values in the image data to values between 0 and 1.

5. The method according to claim 4, characterized in that the normalization of the real part values in the image data to values lying between 0 and 1 is in particular:

according toNormalizing real part values in the image dataTo a value between 0 and 1;

wherein W (R) is a processed value, R (rho)_HF(r)) is a real part value.

6. A magnetic resonance imaging apparatus, characterized by comprising:

the high-pass filtering unit is used for carrying out complex high-pass filtering on the original image to obtain a filtered image;

the normalization processing unit is used for acquiring image data from the filtered image, segmenting imaginary part numerical values in the image data and carrying out different normalization processing on real part numerical values in the image data in each segmentation range;

and the weighting calculation unit is used for multiplying the processed numerical value serving as a weight value with the gray value of the amplitude image to obtain the magnetic resonance image.

7. The apparatus of claim 6, further comprising:

and the projection unit is used for performing minimum signal intensity projection on the obtained magnetic resonance images of all the layers.

8. The apparatus of claim 6, wherein the complex high-pass filtering is homomorphic high-pass filtering.

9. The apparatus of claim 6,

the normalization processing unit includes:

a first setting subunit, configured to normalize a real part value in the image data to 1 when an imaginary part value in the image data is greater than or equal to 0;

a first transformation subunit, configured to normalize a real part value in the image data to a value between 0 and 1 when an imaginary part value in the image data is smaller than 0;

or,

the normalization processing unit includes:

a second setting subunit, configured to normalize the real part value in the image data to 1 when the imaginary part value in the image data is smaller than 0;

a second transformation subunit, configured to normalize the real part value in the image data to a value between 0 and 1 when the imaginary part value in the image data is greater than or equal to 0;

or,

the normalization processing unit includes:

a third transformation subunit directly normalizing real part values of the image data to values between 0 and 1.

10. The apparatus according to claim 9, wherein the first transform subunit, the second transform subunit, or the third transform subunit are specifically configured to,

according toNormalizing real part values in the image data to values between 0 and 1;

wherein W (R) is a processed value, R (rho)_HF(r)) is a real part value.