CN109308699A - A kind of method of cranial nerve fiber image and its neurotransmitter fusion of imaging - Google Patents
A kind of method of cranial nerve fiber image and its neurotransmitter fusion of imaging Download PDFInfo
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Abstract
The present invention relates to clinical treatment and brain science research field more particularly to a kind of methods of cranial nerve fiber image and its neurotransmitter fusion of imaging.First by cerebral artery vessel data (MRA), cerebral veins, venae cerebri and the venous sinus data (MRV) and skull, the ventricles of the brain, the subduction of brain pond data in head portion MRI scan data, obtain containing only the MRI data of brain tissue, then the image that cranial nerve fiber image is merged with its neurotransmitter is obtained by the way that diffusion tensor technology (DTI) and magnetic resonance pop technology (MRS) is used in combination to pure brain tissue MRI data.The present invention obtains the blending image of cranial nerve fiber image and the variation relation of these nerve fibre functions by the variation of DTI combination MRS, data are provided with the dynamic change of its mediator in the positioning of body cranial nerve fiber for research living body to support, and then provide a kind of new method of atraumatic dissection presentation synchronous with function for the nerve positioning that research participates in brain function variation.
Description
Technical field
The present invention relates to clinical treatment and brain science research field more particularly to a kind of cranial nerve fiber images and its nerve
The method of mediator fusion of imaging.
Background technique
Magnetic resonance imaging (Magnetic Resonance Imaging, MRI) is that one kind of Medical Imaging inspection is important
Method especially has important value in terms of human brain medical diagnosis on disease and brain science research, is a revolution of Medical Imaging.It is raw
Group of objects, which is woven in, to be placed in a high-intensitive stabilizing magnetic field, then after giving an external RF field excitation, in tissue
By excitation energy level transition is occurred for atomic nucleus, mainly hydrogen nuclei, and after stopping emitting radio-frequency pulse, atomic nucleus energy is released
It puts, generates magnetic resonance signal, after reception to signal, amplification, Fourier transform, magnetic resonance signal is changed into image letter
Breath, is exactly the magnetic resonance image that we are commonly seen day, it can be with response organization and the characteristic of lesion, moreover, it can be with
From molecular level, the information in terms of metabolic function is provided, to provide information to diagnose the illness.Therefore, have become medicine and
The important tool of brain science research.
A kind of Molecular imaging techniques that Magnetic Resonance Spectrum (MR spectrum, MRS), which has been the nearly more than ten years, to grow up,
Image-forming principle is essentially identical with MRI, is directly to reflect metabolic alterations on a molecular scale and expressed with wave spectrum and image.
MRS is the non invasive method for studying tissue metabolism and function, it can observe and detect certain metabolites and neurotransmitter
Content and distribution are the noninvasive molecular level detection methods of currently the only living body, therefore, in the diagnosis and brain science of Central Nervous System Diseases
Research in, have important value.Common atomic nucleus has,1H and31P, with1H is main, is distributed in vivo most wide.1HMRS predominantly detects the metabolites such as choline, creatine, fat, amino acid, lactic acid and various neurotransmitters;31PMRS can be to the heart
Muscle infarction changes in energy metabolism is evaluated.
Diffusion tensor (DTI) is nerve fibre imaging method developed in recent years.Applied moisture is being organized
The unevenness of middle disperse, i.e. anisotropic properties, on the basis of conventional magnetic resonance imaging sequence, along with sensitive to disperse
Gradient pulse (disperse coding gradient) obtains, to improve the sensibility of dispersive signal, by diffusion tensor technology, obtains
To the image of the nerve fibre of white matter of brain.For example, if Magnetic resonance imaging is the hydrogen atom tracked in hydrone, that
Diffusion tensor is charted according to hydrone moving direction.Diffusion tensor figure (presentation mode and pervious image
It is different) it can show the anatomical structure of nerve fibre, it is medical diagnosis on disease to provide a kind of neural nerve anatomies information
And brain science research provides a kind of means and tool.Currently, can be further to cerebral white matter fiber using DTI data obtained
Beam imaging, can recognize big intracerebral specialty fibers channel and its mutual connection.That is diffusion tensor tractography
(diffusion tensor tractography, DTT) is the further development of DTI technology.
(MR angiography, MRA) and Magnetic resonance venogram (MR is imaged in magnetic resonance arteries
Venography, MRV) it is a kind of non-invasive, it is not required to the blood vessel imaging method with intubation and Contrast medium, extensively at present
Applied to clinic.
It is all to carry out the technological means of woundless testing research to tissue using mr techniques, but individually take above
Any one changes out come the nerve fibre anatomical structure that all cannot achieve living body cranial nerve and its neururgic neurotransmitter
Presentation and dynamic monitoring in same width imaged image, also just hinder the data of research living body cranial nerve state in cerebration
It collects and studies, limit the further development of cranial nerve science and clinical research, such as, it is studied in biological living in body
In, does which neurotransmitter some nerve fibre bundle have participated in activity in a certain state? how is its active degree? with other minds
It there is there which functional cohesion through nerve fibre? these are all that current brain science field needs to solve the problems, such as and be not resolved.
Summary of the invention
The technical problem to be solved by the present invention is to be directed to the research of cranial nerve at present, living body also cannot achieve in body cranial nerve
The problem of synchronous dynamic dynamic monitoring that Fiber morphology structure changes with its mediator, it is refreshing in cerebration also just to hinder research living body
Data collection and research through state limit the further development of cranial nerve science and clinical research.
In order to solve the above technical problems, the present invention provides a kind of cranial nerve fiber image and its neurotransmitter fusion of imaging
Method, it is therefore an objective to which the variation relation of cranial nerve fiber image He these nerve fibre functions is obtained by the variation of DTI combination MRS
Blending image, for study cranial nerve fiber dynamic change provide data support and new method.
To solve the above problems, the invention discloses the sides of a kind of cranial nerve fiber image and its neurotransmitter fusion of imaging
Method, first by cerebral artery vessel data (MRA), cerebral veins, venae cerebri and the venous sinus data (MRV) and cranium in head portion MRI scan data
Bone, the ventricles of the brain, the subduction of brain pond data, obtain the MRI data for containing only brain tissue, are then made to pure brain tissue MRI data by combining
With diffusion tensor technology (DTI) and magnetic resonance pop technology (MRS), obtains cranial nerve fiber image and melt with its neurotransmitter
The image of conjunction.
Specifically comprise the steps of:
The first step acquires head portion MRI scan data, DTI data, cerebral artery MR under the same state of same test body
(MRA) data, cerebral veins, venae cerebri, venous sinus MRI data (MRV), the MRI data of skull and the ventricles of the brain, brain pond.
Second step, by cerebral artery MR (MRA) data, cerebral veins, venae cerebri, venous sinus MR data (MRV), skull and the ventricles of the brain, brain pond
MRI data is reduced from the head portion MRI scan data of same test body, obtains the MRI data for containing only brain tissue.Wherein data subtract
Except the principle of such as Technology of Digital Subtraction Angiography (DSA) can be used to carry out data rejecting, i.e., by head portion MRI scan number
According to, cerebral artery MR (MRA) data, cerebral veins, venae cerebri, venous sinus MR data (MRV), skull and the ventricles of the brain, brain pond MRI data input meter
Calculation machine obtains the MRI data of brain tissue by subtracting shadow, enhancing and reimaging process.The purpose for reducing non-brain tissue is to make
MRS can obtain data without being influenced by non-brain tissue in any position in the solid space of brain tissue, improve accuracy.Separately
Outside, Free water is contained in the ventricles of the brain, brain pond, data can also inhibit imaging pattern to be eliminated its signal data by water;It can also
Subtract shadow to directly adopt to reduce data using the space K or make original image carry out matching by given threshold, subtraction image passes through
After averages pixels processing, pure brain tissue MRI data or MRI image are rebuild using statistical method.
Third step obtains MRS data and image in the MRI data for containing only brain tissue, sets the target ingredient (mind of MRS
Through mediator) threshold value N0;The target for being higher than threshold value N0 on MRS is marked on DTI image;The ingredient mediator of observation mark and corresponding
The variation relation of cranial nerve fiber image.The basic principle of MRS imaging is to couple two kinds of physical phenomenons according to chemical shift and J-,
Due to chemical shift difference, different compounds can the position difference of formant be distinguished on MRS according to it.Chemical shift
Use hundred a ten thousandths of magnetic field strength for unit (part per million, ppm).The area of formant and the number of resonant nucleus
Mesh is directly proportional, reflects the concentration of compound, therefore can be used to quantitative analysis.The kind of the position representative species of peak value on transverse axis
Class, the quantity of the height of wave crest or the area representative species under wave crest, the content of compound can also be indicated with map color range.Therefore it can
To design program, the MRS data of target ingredient (neurotransmitter) are obtained, while label imaging is determined according to the position of chemical shift
Test point is marked higher than threshold value N0, the different color identifier of different mediator ingredients.
4th step schemes the test point of setting MRS in conjunction with DTI, obtains the mediator delta data of specified cranial nerve fiber, obtain
Obtain the information of image anatomy and function.
Further, DTI and MRS are used in combination, and are analyzed for operator according to two kinds of obtained data of technology
To the blending image for when generating DTI and MRS.
Further, DTI and MRS are used in combination, and are setting computer program, input threshold value N0 in a program, pass through K
The region of threshold value N0 or more is shown as the point or point cluster of different colours by space encoding and Fourier transform technology on DTI figure
(line).It identifies DTI image and MRS target ingredient in any spatial match of intracerebral, upper at an arbitrary position can show, obtain
DTI data and MRS data, obtain DTI image and the blending image of MRS.MRS can also be kept automatic by artificial intelligence technology
Identification and MRS and DTI Auto-matching, it marks, wherein the algorithm logic of computer program such as Fig. 1:
Further, DTI and MRS is used in combination, and is setting computer program, passes through artificial or program selecting and generates number
According to the mediator situation of change for showing specific cranial nerve fiber, the blending image of DTI and MRS are obtained, and show on computers
On some nerve fibre point or the image of certain corresponding mediator of some space section of brain, the anatomical structure of nerve fibre is realized
With the blending image of its neurotransmitter variation.Its algorithm logic such as Fig. 2:
The beneficial effects of the present invention are:
(1) it is put forward for the first time and MRS data and DTI data is subjected to Conjoint Analysis, cranial nerve fiber can be tracked, it is seen that magnetic is total
Mediator situation in vibration wave spectrum.
(2) by MRS data markers on DTI image, are capable of observation mark neurotransmitter and corresponding cranial nerve fiber
The variation relation of image.
(3) by the MRS data of certain nerve fibre on detection DTI image, passing for specified cranial nerve fiber can be obtained
Matter delta data obtains the information of image anatomy and function.
(4) the nerve fibre anatomical structure that living body cranial nerve may be implemented is synchronous with its neururgic neurotransmitter variation
It presents, can be presented in same width imaged image and synchronous dynamic monitors.It is studied for example, can solve biological living in body
In, does which neurotransmitter some nerve fibre bundle have participated in activity in a certain state? how is its active degree? with other minds
It there is there which functional cohesion through nerve fibre? these are all that current brain science field needs to solve the problems, such as and be not resolved.
Detailed description of the invention
Fig. 1 is the algorithm logic figure of embodiment 1;
Fig. 2 is the algorithm logic figure of embodiment 2.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with implementation of the invention
Example, technical scheme in the embodiment of the invention is clearly and completely described.Obviously, described embodiment is the present invention
A part of the embodiment, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not having
Every other embodiment obtained under the premise of creative work is made, shall fall within the protection scope of the present invention.
Embodiment 1:
A kind of method of cranial nerve fiber image and its neurotransmitter fusion of imaging, comprises the steps of:
The first step acquires head portion MRI scan data, DTI data, cerebral artery MR under the same state of same test body
(MRA) data, cerebral veins, venae cerebri, venous sinus MR data (MRV), the MRI data of skull and the ventricles of the brain, brain pond.
Second step, by cerebral artery MR (MRA) data, cerebral veins, venae cerebri, venous sinus MR data (MRV), skull and the ventricles of the brain, brain pond
MRI data is reduced from the head portion MRI scan data of same test body, obtains the MRI data for containing only brain tissue.Wherein data subtract
Subtract shadow except directlying adopt to reduce data using the space K or make original image carry out matching by given threshold, subtraction image passes through
After averages pixels processing, pure brain tissue MRI image is rebuild using statistical method.
Third step sets target ingredient (neurotransmitter) the threshold value N0 (such as acetylcholine, 3.22ppm) of MRS;Containing only brain
MRS data and image are obtained in the MRI data of tissue, and the target that threshold value N0 is higher than on MRS is marked on DTI image;It sees
The variation relation of the ingredient mediator of mark and corresponding cranial nerve fiber image, if people is in thinking, the nerve fibre second of hippocampus
Phatidylcholine mediator can increase.
4th step schemes the test point of setting MRS in conjunction with DTI, obtains the mediator delta data of specified cranial nerve fiber, obtain
Obtain the information of image anatomy and function.
DTI and MRS are used in combination, and are operator according to two kinds in the case where technical conditions are immature or do not allow
The obtained data of technology are analyzed to the blending image for when generating DTI and MRS.Embodiment 2:
A kind of method of cranial nerve fiber image and its neurotransmitter fusion of imaging, comprises the steps of:
The first step acquires head portion MR scan data, DTI data, cerebral artery MR under the same state of same test body
(MRA) data, cerebral veins, venae cerebri, venous sinus MR data (MRV), the MRI data of skull and the ventricles of the brain, brain pond.
Second step, by cerebral artery MR (MRA) data, cerebral veins, venae cerebri, venous sinus MR data (MRV), skull and the ventricles of the brain, brain pond
MRI data is reduced from the head portion MR scan data of same test body, obtains the MRI data for containing only brain tissue.Its midventricle, brain
The MRI data subduction in pond can inhibit imaging pattern to be eliminated its number signal data using water.
Third step, setting target ingredient (neurotransmitter) the threshold value N0 of MRS, (such as N- acetyl aspartate, crest location exist
2.02,2.05 and 2.6ppm);MRS data are obtained in the MRI data for containing only brain tissue, and the mark of threshold value N0 will be higher than on MRS
Label on DTI image;The variation relation of the ingredient mediator of observation mark and corresponding cranial nerve fiber image.
4th step schemes the test point of setting MRS in conjunction with DTI, obtains the mediator delta data of specified cranial nerve fiber, obtain
Obtain the information of image anatomy and function.
As shown in Figure 1, DTI and MRS are used in combination, it is setting computer program, threshold value N0 is inputted in a program, in DTI
The region of threshold value NO or more is shown as to the point or point cluster (line) of different colours on figure;Or it can be made by artificial intelligence technology
MRS automatic identification and MRS and DTI Auto-matching, label.
Embodiment 3:
A kind of method of cranial nerve fiber image and its neurotransmitter fusion of imaging, comprises the steps of:
The first step acquires head portion MR scan data, DTI data, cerebral artery MR under the same state of same test body
(MRA) data, cerebral veins, venae cerebri, venous sinus MR data (MRV), the MRI data of skull and the ventricles of the brain, brain pond.
Second step, by cerebral artery MR (MRA) data, cerebral veins, venae cerebri, venous sinus MR data (MRV), skull and the ventricles of the brain, brain pond
MRI data is reduced from the head portion MR scan data of same test body, obtains the MRI data for containing only brain tissue.Wherein data subtract
Except the principle that such as number can be used to subtract the technology of drawing (DSA) carries out data rejecting, i.e., by head portion MR scan data, cerebral artery
MR (MRA) data, cerebral veins, venae cerebri, venous sinus MR data (MRV), skull and the ventricles of the brain, brain pond MRI data input picture computer,
The MRI data of brain tissue is obtained by subtracting shadow, enhancing and reimaging process.
Third step sets the target ingredient of MRS, if glutamine and glutamic acid crest location are 2.1 to 2.55ppm.;?
It contains only and obtains MRS data and image in the MRI data of brain tissue, the target label that threshold value N0 is higher than on MRS is schemed in DTI
As upper;The variation relation of the ingredient mediator of observation mark and corresponding cranial nerve fiber image.
4th step schemes the test point of setting MRS in conjunction with DTI, obtains the mediator delta data of specified cranial nerve fiber, obtain
Obtain the information of image anatomy and function.It can also make MRS automatic identification and MRS and DTI automatic by artificial intelligence technology
Match, mark.
As shown in Fig. 2, DTI's and MRS is used in combination, it is setting computer program, is generated by artificial or program selecting
Data show the mediator situation of change of specific cranial nerve fiber, obtain the blending image of DTI and MRS, and show on computers
Show on some nerve fibre point or the image of certain corresponding mediator of some space section of brain, realizes the dissection knot of nerve fibre
The blending image of structure and the variation of its neurotransmitter.
Claims (8)
1. a kind of cranial nerve fiber image and its neurotransmitter fusion of imaging method, it is characterised in that: first sweep head portion MRI
Retouch the cerebral artery vessel data in data, i.e. MRA, cerebral veins, venae cerebri and venous sinus data, i.e. MRV and skull, the ventricles of the brain, brain pond data
Subduction, obtains the MRI data for containing only brain tissue;Then to pure brain tissue MRI data by the way that diffusion tensor skill is used in combination
Art, i.e. DTI and magnetic resonance pop technology, i.e. MRS, obtain the image that cranial nerve fiber image is merged with its neurotransmitter.
2. a kind of cranial nerve fiber image according to claim 1 and its neurotransmitter fusion of imaging method, feature exist
In the first step, head portion MRI scan data under the same state of same test body, DTI data, cerebral artery MRI data are acquired,
That is MRA data, cerebral veins, venae cerebri, venous sinus MRI data, i.e. MRV data, the MRI data of skull and the ventricles of the brain, brain pond.
3. a kind of cranial nerve fiber image according to claim 1 and its neurotransmitter fusion of imaging method, feature exist
In second step, by cerebral artery MRI, i.e. MRA data, cerebral veins, venae cerebri, venous sinus MRI data, i.e. MRV data, skull and the ventricles of the brain, brain
The MRI data in pond is reduced from the head portion MRI scan data of same test body, obtains the MRI data for containing only brain tissue.
4. a kind of cranial nerve fiber image according to claim 1 and its neurotransmitter fusion of imaging method, feature exist
In: third step obtains MRS data and image in the MRI data for containing only brain tissue, sets the target ingredient of MRS, i.e. nerve is passed
The threshold value N0 of matter marks the target for being higher than threshold value N0 on MRS on DTI image;The ingredient mediator and corresponding brain of observation mark
The variation relation of nerve fibre image.
5. a kind of cranial nerve fiber image according to claim 1 and its neurotransmitter fusion of imaging method, feature exist
In: the 4th step schemes the test point of setting MRS in conjunction with DTI, obtains the mediator delta data of specified cranial nerve fiber, obtains shadow
As the information of dissection and function.
6. a kind of cranial nerve fiber image and its neurotransmitter fusion of imaging method, feature according to claim 4 and 5
Be: DTI and MRS are used in combination, and are analyzed according to two kinds of obtained data of technology to when generating DTI for operator
With the blending image of MRS.
7. a kind of cranial nerve fiber image according to claim 4 and its neurotransmitter fusion of imaging method, feature exist
In: DTI and MRS are used in combination, and are setting computer program, input threshold value N0 in a program, by threshold value N0 or more on DTI figure
Region be shown as the point of different colours, point cluster or line;Or by artificial intelligence technology, make MRS automatic identification and MRS and DTI
Auto-matching, label.
8. a kind of cranial nerve fiber image according to claim 5 and its neurotransmitter fusion of imaging method, feature exist
In: DTI's and MRS is used in combination, and is setting computer program, generates data by artificial or program selecting and shows specific brain
The mediator situation of change of nerve fibre, obtains the blending image of DTI and MRS, and shows some nerve fibre point on computers
Upper or certain corresponding mediator of some space section of brain image realizes that the anatomical structure of nerve fibre and its neurotransmitter become
The blending image of change.
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112259211A (en) * | 2020-10-23 | 2021-01-22 | 广州中医药大学第一附属医院 | Nasopharyngeal carcinoma radioactive brain injury degree detection system based on DTI and 1H-MRS technology |
CN113283465A (en) * | 2021-04-02 | 2021-08-20 | 电子科技大学 | Diffusion tensor imaging data analysis method and device |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1582863A (en) * | 2004-06-01 | 2005-02-23 | 复旦大学 | Method for correcting brain tissue deformation in navigation system of neurosurgery |
US20050273001A1 (en) * | 2004-06-04 | 2005-12-08 | The Mcw Research Foundation | MRI display interface for medical diagnostics and planning |
CN103049901A (en) * | 2012-08-03 | 2013-04-17 | 上海理工大学 | Magnetic resonance diffusion tensor imaging fiber bundle tracking device |
CN104207776A (en) * | 2014-08-22 | 2014-12-17 | 南昌大学 | Comprehensive magnetic resonance imaging device and method |
CN105631930A (en) * | 2015-11-27 | 2016-06-01 | 广州聚普科技有限公司 | DTI (Diffusion Tensor Imaging)-based cranial nerve fiber bundle three-dimensional rebuilding method |
US20170172495A1 (en) * | 2014-03-18 | 2017-06-22 | The University Of Newcastle | Method and system for detecting and identifying different types of pain and monitoring subsequent therapy |
-
2018
- 2018-10-29 CN CN201811270745.4A patent/CN109308699A/en active Pending
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1582863A (en) * | 2004-06-01 | 2005-02-23 | 复旦大学 | Method for correcting brain tissue deformation in navigation system of neurosurgery |
US20050273001A1 (en) * | 2004-06-04 | 2005-12-08 | The Mcw Research Foundation | MRI display interface for medical diagnostics and planning |
CN103049901A (en) * | 2012-08-03 | 2013-04-17 | 上海理工大学 | Magnetic resonance diffusion tensor imaging fiber bundle tracking device |
US20170172495A1 (en) * | 2014-03-18 | 2017-06-22 | The University Of Newcastle | Method and system for detecting and identifying different types of pain and monitoring subsequent therapy |
CN104207776A (en) * | 2014-08-22 | 2014-12-17 | 南昌大学 | Comprehensive magnetic resonance imaging device and method |
CN105631930A (en) * | 2015-11-27 | 2016-06-01 | 广州聚普科技有限公司 | DTI (Diffusion Tensor Imaging)-based cranial nerve fiber bundle three-dimensional rebuilding method |
Non-Patent Citations (3)
Title |
---|
JEAN J. WANG等: "MRSI and DTI: a multimodal approach for improved detection of white matter abnormalities in alcohol and nicotine dependence", 《RESEARCH ARTICLE》 * |
Q.-G. ZOU等: "In the assessment of supratentorial glioma grade: The combined role of multivoxel proton MR spectroscopy and diffusion tensor imaging", 《CLINICAL RADIOLOGY》 * |
杨海山等: "《影像诊断新技术》", 31 August 2006 * |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112259211A (en) * | 2020-10-23 | 2021-01-22 | 广州中医药大学第一附属医院 | Nasopharyngeal carcinoma radioactive brain injury degree detection system based on DTI and 1H-MRS technology |
CN112259211B (en) * | 2020-10-23 | 2024-01-16 | 广州中医药大学第一附属医院 | Nasopharyngeal carcinoma radioactive brain injury degree detection system based on DTI and 1H-MRS technology |
CN113283465A (en) * | 2021-04-02 | 2021-08-20 | 电子科技大学 | Diffusion tensor imaging data analysis method and device |
CN113283465B (en) * | 2021-04-02 | 2022-04-29 | 电子科技大学 | Diffusion tensor imaging data analysis method and device |
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