Apparatus and method for visualizing tissue macro- and microstructure for pathology evaluation in magnetic resonance imaging

1. A method for detecting a brain tissue pathology in magnetic resonance (MR) images of a brain of a patient, which comprises the steps of:

acquiring multiple MR imaging data and using the multiple MR imaging data for creating at least four different contrast maps of the brain of the patient;

performing an estimation of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) concentration for each voxel of at least a part of the brain from the multiple MR imaging data acquired;

creating from the estimation, GM, WM and CSF concentration maps for the part of the brain;

segmenting the part of the brain in different regions-of-interest (ROIs) according to a chosen atlas from the multiple MR imaging data acquired;

computing, for each voxel of each the contrast maps created of the brain, for at least the part of the brain, a deviation score obtained by combining voxel intensity of a contrast map in the brain of the patient, voxel tissues concentrations from the GM, WM and CSF concentration maps in the brain, and parameters values characterizing a contrast map intensity distribution of each tissue in healthy controls obtained for the ROI to which the voxel belongs to;

creating from the deviation score and for each of the contrast maps being quantitative contrast maps, a deviation map representing the part of the brain in dependence on the deviation score calculated for each said voxel; and

combining deviation maps into a single pathology map.

2. The method according to claim 1 , wherein the four contrast maps are T1, T2 and T2* relaxometry maps and a magnetisation transfer ratio (MTR) map.

3. The method according to claim 1 , which further comprises extracting lesions and identifying brain tissues to create a lesions mask from the multiple MR imaging data.

4. The method according to claim 3 , which further comprises using the lesions mask for modifying the WM, GM and CSF concentration maps by changing voxel concentrations in order to precisely identify a tissue type altered by the lesions.

5. The method according to claim 1 , which further comprises performing a determination of the parameters values characterizing the contrast map intensity distribution of each said tissue in healthy subjects based on an analysis of a group of the healthy subjects.

6. The method according to claim 1 , wherein the pathology map is a colored pathology map, wherein each voxel of the colored pathology map is a combination of deviation score intensities of each of previously obtained deviation maps into a color, where a color value identifies a pathology and its intensity characterizes a deviation.

7. The method according to claim 1 , wherein the deviation score is given by:

z

v

=

C

GM

⁡

(

I

v

-

μ

GM

)

+

C

WM

⁡

(

I

v

-

μ

WM

)

+

C

CSF

⁡

(

I

v

-

μ

CSF

)

(

C

GM

2

⁢

σ

GM

2

+

C

WM

2

⁢

σ

WM

2

+

C

CSF

2

⁢

σ

CSF

2

+

2

⁢

C

GM

2

⁢

C

WM

2

⁢

Cov

GM

/

WM

2

+

A

+

B

)

1

2

⁢

⁢

with

⁢

⁢

A

=

2

⁢

C

GM

2

⁢

C

CSF

2

⁢

Cov

GM

/

CSF

2

⁢

⁢

B

=

2

⁢

C

WM

2

⁢

C

CSF

2

⁢

Cov

WM

/

CSF

2

wherein:

C GM is an intensity of a voxel in the GM concentration map;

I v is an intensity of the voxel in the contrast map;

μ GM is a mean value of a healthy distribution of GM in the ROI where the voxel belongs to;

C WM is an intensity of a voxel in the WM concentration map;

μ WM is a mean value of a healthy distribution of WM in the ROI where the voxel belongs to;

C CSF is a intensity of a voxel in the CSF concentration map;

μ CSF is a mean value of a healthy distribution of the CSF in the ROI where the voxel belongs to;

σ WM is a standard deviation value of the healthy distribution of the WM in the ROI where the voxel belongs to;

σ GM is a standard deviation value of the healthy distribution of the GM in the ROI where the voxel belongs to;

σ CSF is a standard deviation value of the healthy distribution of the CSF in the ROI where the voxel belongs to;

Cov GM/WM is a covariance value of the healthy distribution between the GM and WM in the ROI where the voxel belongs to;

Cov GM/CSF is a covariance value of the healthy distribution between the GM and CSF in the ROI where the voxel belongs to; and

Cov WM/CSF is a covariance value of the healthy distribution between the WM and CSF in the ROI where the voxel belongs to.

8. A magnetic resonance imaging apparatus for imaging an object, the magnetic resonance imaging apparatus, comprising:

an imaging unit; and

a processor controlling said imaging unit, said processor configured to automatically detect a brain tissue pathology in magnetic resonance (MR) images of a patient by performing the following steps of:

acquiring multiple MR imaging data and using the multiple MR imaging data for creating at least four different contrast maps of a brain of a patient;

performing an estimation of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) concentration for each voxel of at least a part of the brain from the multiple MR imaging data acquired;

creating from the estimation, GM, WM and CSF concentration maps for the part of the brain;

segmenting the part of the brain in different regions-of-interest (ROIs) according to a chosen atlas from the multiple MR imaging data acquired;

computing, for each voxel of each the contrast maps created of the patient brain, for at least the part of the brain, a deviation score obtained by combining voxel intensity of a contrast map in the brain, voxel tissues concentrations from the GM, WM and CSF concentration maps in the brain, and parameters values characterizing a contrast map intensity distribution of each tissue in healthy controls obtained for the ROI to which the voxel belongs to;

creating from the deviation score and for each of the contrast maps being quantitative contrast maps, a deviation map representing the part of the brain in dependence on the deviation score calculated for each voxel; and

combining deviation maps into a single pathology map.