CN105701815B - A kind of MR perfusion imaging post-processing approach and system - Google Patents
A kind of MR perfusion imaging post-processing approach and system Download PDFInfo
- Publication number
- CN105701815B CN105701815B CN201610019251.3A CN201610019251A CN105701815B CN 105701815 B CN105701815 B CN 105701815B CN 201610019251 A CN201610019251 A CN 201610019251A CN 105701815 B CN105701815 B CN 105701815B
- Authority
- CN
- China
- Prior art keywords
- curve
- artery
- perfusion
- cot
- matrix
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
- G06T2207/30104—Vascular flow; Blood flow; Perfusion
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
The present invention provides a kind of MR perfusion imaging post-processing approach and system, method to include：Original brain perfusion image is filtered, removal brain edge obtains filtering hindbrain perfusion image；The regional choice instruction for receiving user obtains the selection area in the middle artery area of brain perfusion image after the filtering, the weighting artery input curve of output assessment arterial input function；Weighting artery input curve and Cot curve are fitted respectively according to improved Gamma functions, the artery input curve and Cot curve optimized；The relative quantification parameter of perfusion is solved according to the improved decomposition of singular matrix method based on nonparametric model.The present invention is reduced the susceptibility to noise, is fitted using simplified and effective Gamma functions, converts nonlinear problem to linear solution, accelerate post-processing speed by being weighted optimization to AIF and solution matrix.
Description
Technical field
The present invention relates to MR perfusion imaging technical field more particularly to a kind of MR perfusion imaging post-processing approach
And system.
Background technology
With the development of science and technology, the application of magnetic resonance imaging system at home and abroad is more and more universal.Magnetic resonance imaging
(Magnetic Resonance Imaging, abbreviation MRI) is strong what is added outside using the hydrogen atom for spreading all over whole body in human body
It is excited by radio-frequency pulse in magnetic field, generates nmr phenomena and detected and received with detector by spatial encoding techniques
With the NMR signal that electromagnetic form is released, the NMR signal of detection is inputted into computer, is converted by data processing,
The form that finally human body is respectively organized forms image.Magnetic resonance imaging can be from different angles to partes corporis humani position, each system
The diseases such as neoplastic lesion, vascular lesions, infection, wound, congenital development deformity and retrograde affection are examined
Look into and diagnose, by adjust magnetic field can section needed for unrestricted choice, can obtain other imaging techniques cannot approach or be difficult to connect
The image of near-end position.Also, magnetic resonance imaging with no ionising radiation injury, soft tissue contrast is high, image resolution ratio is high, at
As the advantages that parameter and flexible scan position selection, so being widely used in clinical medicine diagnosis.Modern medicine shadow in recent years
It is not content with research change to pathological change form as learning, and to reflection tissue and organ physiology and pathological change or even brain function
Imaging direction develops.Wherein brain magnetic resonance Perfusion Imaging (Perfusion-weighted Magnetic Resonance
Imaging, PWI) be one of method of cerebral function imaging, to reflect in tissue capilary distribution and blood perfusion state to
Disclose the biological behaviour of brain tumor；Become the important medical means of observation tumour variation.
Magnetosensitive contrast medium Perfusion Imaging is currently used imaging method.When paramagnetic contrast's agent passes through by inspection tissue
When, the T2* relaxation times of tissue can be shortened, the signal being reflected on corresponding T2* Perfusion Imagings reduces；When paramagnetism pair
After passing through than agent, signal strength is gradually restored.There are lines with contrast medium concentrations in blood for T2* relaxation change rate in a certain range
Sexual intercourse can calculate the relative quantification parameter of evaluation Perfusion Imaging in conjunction with haemodynamic model by post-processing --- and it is opposite
Blood volume (rCBV), relative blood flow speed (rCBF), mean transit time (MTT), initial arrival time (T0).After existing
Processing method is mainly to carry out solving sxemiquantitative ginseng using the convolution relation of arterial input function (AIF) and concentration-time signal
Number.
The determination method of wherein AIF has two classes, the non-automatic selection of the first kind；Second class is to be based on statistical method and Fuzzy C
Means clustering method automatically selects.Automatically selecting reduces user's operation, but its calculation amount and accuracy still wait for it is rigorous
Proof.In order to improve computational accuracy, most researchers propose based on Gamma Function Fitting Cot curves, this method
It is larger for the lower region error of fitting of signal-to-noise ratio to image quality requirements height；And nonlinear fitting operation time is longer.
The calculating of rCBV, rCBF are mainly based upon the nonparametric model analysis method of singular value decomposition (SVD) and based on parameter models
Analysis method.Wherein the method based on parameter model needs that suitable mathematical model is pre-selected, if selection is improper often
Cause larger calculating error.Therefore there is larger difficulty in practical applications.
Therefore, the prior art could be improved and develop.
Invention content
Place in view of above-mentioned deficiencies of the prior art, after a kind of MR perfusion imaging
Manage method and system, it is intended to which the post-processing approach for solving contrast medium Perfusion Imaging in the prior art mainly inputs letter using artery
Number (AIF) and the convolution relation of concentration-time signal carry out solution semi-quantitative parameters, and solving result is influenced by mathematical model selection,
And to image quality requirements height, the problem of can not fast and accurately obtaining semi-quantitative parameters.
In order to achieve the above object, this invention takes following technical schemes：
A kind of MR perfusion imaging post-processing approach, wherein the described method comprises the following steps：
A, original brain perfusion image is filtered, removal brain edge obtains filtering hindbrain perfusion image；
B, the regional choice instruction for receiving user, obtains the selection area in the middle artery area of brain perfusion image after the filtering,
The weighting artery input curve of output assessment arterial input function；
C, weighting artery input curve and Cot curve are fitted respectively according to improved Gamma functions, are obtained
To the artery input curve and Cot curve of optimization；
D, according to improved, the decomposition of singular matrix method based on nonparametric model solves the relative quantification parameter being perfused.
The MR perfusion imaging post-processing approach, wherein the step B is specifically included：
B1, the regional choice instruction for receiving user, obtain the selection area in the middle artery area of brain perfusion image after the filtering；
B2, centering artery area the concentration-time signal of selection area be weighted smoothly, obtain calculating for parameter
Arterial input function, and weighting artery input curve is exported according to arterial input function.
The MR perfusion imaging post-processing approach, wherein the step C is specifically included：
C1, according to the maximum value of Cot curve, the rising stage, decline phase Improvement of estimation Gamma Function Fittings just
Initial value；
C2, in [T0,Tmax+ n] combine Gamma Function Fittings in curve ranges be initially worth to improved Gamma functions
Optimal Parameters；Wherein T0It is initial arrival time, TmaxIt it is the peak-peak corresponding time, n is the point that fitting is participated in after peak value
Number.
C3, it is fitted, is optimized according to the improved Gamma function pairs weighting artery input curve and Cot curve
Artery input curve and Cot curve.
The MR perfusion imaging post-processing approach, wherein the step D is specifically included：
D1, combination is weighted according to the matrix a in the improved decomposition of singular matrix method based on nonparametric model,
And SVD is carried out to matrix a and decomposes to obtain diagonal matrix W, orthogonal matrix V, orthogonal matrix U；
D2, according to the threshold strategies diagonal matrix s that obtains that treated, and the relatively fixed of perfusion is determined according to diagonal matrix s
Measure parameter；Wherein s=1/W.
A kind of MR perfusion imaging after-treatment system, wherein including：
Filter module, for being filtered to original brain perfusion image, removal brain edge obtains filtering hindbrain perfusion image；
Region is selected and output module, the regional choice for receiving user instruct, and obtains brain perfusion image after the filtering
Middle artery area selection area, output assessment arterial input function weighting artery input curve；
Curve fitting module is used for according to improved Gamma functions respectively to weighting artery input curve and concentration-time
Curve is fitted, the artery input curve and Cot curve optimized；
Parameter acquisition module, for according to the improved decomposition of singular matrix system solution perfusion based on nonparametric model
Relative quantification parameter.
The MR perfusion imaging after-treatment system, wherein the region is selected and output module specifically includes：
Unit is selected in region, and the regional choice for receiving user instructs, and obtains in brain perfusion image after the filtering and moves
The selection area in arteries and veins area；
Output unit is weighted, the concentration-time signal of the selection area for centering artery area is weighted smoothly, obtains
The arterial input function calculated for parameter；Wherein, the arterial input function is weighting artery input curve.
The MR perfusion imaging after-treatment system, wherein the curve fitting module specifically includes：
Evaluation unit, for according to the maximum value of Cot curve, rising stage, the Gamma letters for declining phase Improvement of estimation
The initial value of number fitting；
Gamma function parameter acquiring units, in [T0,Tmax+ n] in range, the initial value that is exported in conjunction with evaluation unit
Obtain the Gamma function parameters for curve matching；Wherein T0It is initial arrival time, TmaxIt is the peak-peak corresponding time,
N is the points that fitting is participated in after peak value.
Curve matching unit, for bent according to the improved Gamma function pairs weighting artery input curve and concentration-time
Line is fitted, the artery input curve and Cot curve optimized.
The MR perfusion imaging after-treatment system, wherein the parameter acquisition module specifically includes：
Resolving cell, for being carried out according to the matrix a in the improved decomposition of singular matrix method based on nonparametric model
Weighted array, and SVD is carried out to matrix a and decomposes to obtain diagonal matrix W, orthogonal matrix V, orthogonal matrix U；
Relative quantification parameter acquiring unit, for according to the threshold strategies diagonal matrix s that obtains that treated, and according to diagonal
Matrix s determines the relative quantification parameter of perfusion；Wherein s=1/W.
MR perfusion imaging post-processing approach of the present invention and system, method include：To original brain perfusion image
Filtering, removal brain edge obtain filtering hindbrain perfusion image；The regional choice instruction for receiving user, obtains brain after the filtering
The selection area in the middle artery area of perfusion image, the weighting artery input curve of output assessment arterial input function；According to improvement
Gamma functions weighting artery input curve and Cot curve are fitted respectively, the input of the artery that is optimized is bent
Line and Cot curve solve the relative quantification of perfusion according to the improved decomposition of singular matrix method based on nonparametric model
Parameter.The present invention reduces the susceptibility to noise, using simplified and effective by being weighted optimization to AIF and solution matrix
Gamma functions be fitted, convert nonlinear problem to linear solution, accelerate post-processing speed.
Description of the drawings
Fig. 1 is the flow chart of MR perfusion imaging post-processing approach preferred embodiment of the present invention.
Fig. 2 is the result schematic diagram that AIF is assessed in MR perfusion imaging post-processing approach of the present invention.
Fig. 3 is the parameter mapping graph of MR perfusion imaging post-processing approach specific implementation of the present invention.
Fig. 4 is the functional block diagram of MR perfusion imaging after-treatment system preferred embodiment of the present invention.
Specific implementation mode
A kind of MR perfusion imaging post-processing approach of present invention offer and system, to make the purpose of the present invention, technical side
Case and effect are clearer, clear, and the present invention is described in more detail for the embodiment that develops simultaneously referring to the drawings.It should be appreciated that
Described herein specific examples are only used to explain the present invention, is not intended to limit the present invention.
Fig. 1 is referred to, is the flow chart of MR perfusion imaging post-processing approach preferred embodiment of the present invention.Such as
Shown in Fig. 1, the MR perfusion imaging post-processing approach includes：
Step S100, original brain perfusion image is filtered, removal brain edge obtains filtering hindbrain perfusion image.
In the embodiment of the present invention, by being filtered in step S100, ambient noise is removed, and combine threshold method
Skull is removed, the calculating of regions of non-interest is reduced, the operation of post-processing can be accelerated.
Step S200, the regional choice instruction for receiving user, obtains the choosing in the middle artery area of brain perfusion image after the filtering
Determine region, the weighting artery input curve of output assessment arterial input function；
Step S300, weighting artery input curve and Cot curve are carried out respectively according to improved Gamma functions
Fitting, the artery input curve and Cot curve optimized；
Step S400, according to improved, the decomposition of singular matrix method based on nonparametric model, which solves, obtains the opposite of perfusion
Quantitative parameter.
Specifically, the relative quantification parameter of perfusion includes relative blood volume (rCBV), relative blood flow speed (rCBF), is averaged
By the time (MTT) and reach time to peak (TTP)
Further, the step S200 is specifically included：
Step S201, the regional choice instruction for receiving user, obtains the choosing in the middle artery area of brain perfusion image after the filtering
Determine region.
In step s 201, manually arteriosomes in selection of the interactive mode on perfusion image, the selection in the region
Can be rectangle, ellipse, irregular, but chosen area be not easy it is excessive.The rectangular area of 3*3 is chosen in this embodiment.
Step S202, the concentration-time signal of the selection area in centering artery area is weighted smoothly, obtains being used for parameter
The arterial input function of calculating, i.e. output weighting artery input curve.
That is the corresponding curve of every bit of the selection area in centering artery area carries out smothing filtering, reduces influence of noise, so
The signal of each point is weighted again afterwards to obtain weighting artery input curve.
To reduce the reflux response of contrast medium, the artery in improved Gamma function pairs step S202 is used in the present invention
Input function is fitted to obtain the artery input curve Ca (t) calculated eventually for parameter, refers to Fig. 2.
Gamma Function Fittings have great importance in Perfusion Imaging, and expression formula is as follows：
G (t)=A* (t-D)B*e(-(t-D)/C) (0)
Wherein A, D, B, C are the Optimal Parameters for needing to estimate；T is time variable.But existing approximating method mostly uses non-
It is longer to calculate the time for linear method.A kind of improved Gamma approximating methods are proposed in the present invention using linear solution to add
It fast post-processing speed and calculates effect and meets clinical needs.
Further, the step S300 is specifically included：
Step S301, quasi- according to the maximum value of Cot curve, rising stage, the Gamma functions of decline phase Improvement of estimation
The initial value of conjunction；
Initial value [the A of Gamma functions in embodiment of the present invention0,B0,C0,D0] be estimated as follows：
A0Take the peak-peak of Cot curve Ct；
D0The first corner position i.e. contrast medium of Cot curve Ct is taken to initially enter the time of tissue；
C0Cot curve Ct is taken to decline the time corresponding with D0 at moment；
B0=(Tmax-T0)/C0；Tmax is the A0 corresponding times.
Step S302, in [T0,Tmax+ n] in range according to the initial value of improved Gamma Function Fittings to weighting artery
Input curve carries out linear fit, the artery input curve optimized；Wherein T0It is initial arrival time, TmaxIt is maximum peak
It is worth the corresponding time, n is the points that fitting is participated in after peak value.
In step s 302, the initial value [A of Gamma functions is inputted0,B0,C0,D0] optimize unknown parameter.In order to add
Contrast medium head crosses the principle of component in fast optimal speed and combination perfusion, and the present invention chooses [T0,Tmax+ n] data in range
Linear least square is carried out to be fitted.Wherein n is the points that fitting is participated in after most peak value.In the specific implementation of the present invention
N chooses the third time point after peak-peak.
Cot curve C (t) and artery input curve C in the Perfusion Imaging of magnetic susceptibility contrast mediuma(t) there are convolution
Relationship：
Introduce contrast medium residual concentration function R (t) in the tissue and blood flow velocity Ft；Then according to formula (2)：
△ t are the dynamic intervals of Perfusion Imaging；Formula (3) is indicated as follows with matrix：
It enablesThen its vector is expressed as：
Ab=C (5)
Decomposition of singular matrix (SVD) is carried out to matrix a, is indicated as follows：
a-1=V*W*UT (6)
Wherein W is diagonal matrix, and V is orthogonal matrix, and U is orthogonal matrix, then
B=V*W* (UT*C) (7)
Being shown according to more literature research can be by the F of agent in the tissue as a comparison of the maximum value in vectorial bt.But above-mentioned
Handling result is more sensitive to noise, and the factor present invention is improved this process in step S400.
Further, the step S400 is specifically included：
Step S401, the matrix a according to improved in the decomposition of singular matrix method based on nonparametric model is weighted
Combination, and SVD is carried out to matrix a and decomposes to obtain diagonal matrix W, orthogonal matrix V, orthogonal matrix U.
Combination is weighted to matrix a, the linear optimization thought of step S300 is combined to utilize the rising of AIF curves first
The concentration signal that phase, decline phase choose particular time range forms new matrix
Wherein D0≤l≤Total (t)；D0≤m≤n；Total (t) indicate contrast medium by overall time.
Remember aijIt is the element of matrix M after improving, when meeting 0≤j≤i：
aij=△ t (Ca(ti-j)+4*Ca(ti-j+1)+Ca(ti-j+2))/6 (8)
A in the case of otherij=0；Then formula (5), which changes, is
Mb=C (9)
SVD is carried out to formula (9) to decompose to obtain diagonal matrix W, orthogonal matrix V, orthogonal matrix U.
Step S402, according to the threshold strategies diagonal matrix s that obtains that treated, and perfusion is determined according to diagonal matrix s
Relative quantification parameter；Wherein s=1/W.
Utilize the threshold strategies diagonal matrix s that obtains that treated；And s=1/W；Threshold value is chosen in embodiment of the present invention
0.2*max(W)；The threshold value is according to obtained by magnetic resonance acquisition system experience in the present invention.Then formula (7) can be written as：
B=V*s* (UT*C) (10)
The pertinent literature so calculated according to perfusion can obtain rCBF=max (b).To according to perfusion relative quantification parameter
Relationship can find out parameters variable successively, and Fig. 3 is in embodiment of the present invention to clinical patient Perfusion Imaging data processing
One of as a result.
As it can be seen that the present invention reduces the susceptibility to noise, using letter by being weighted optimization to AIF and solution matrix
Change and effective Gamma functions are fitted, converts nonlinear problem to linear solution, accelerate post-processing speed.
Based on above method embodiment, the present invention also provides a kind of MR perfusion imaging after-treatment systems.Such as Fig. 4 institutes
Show, the MR perfusion imaging after-treatment system includes：
Filter module 100, for being filtered to original brain perfusion image, removal brain edge obtains filtering hindbrain perfusion figure
Picture；
Region is selected and output module 200, the regional choice for receiving user instruct, and obtains brain perfusion figure after the filtering
The selection area in the middle artery area of picture, the weighting artery input curve of output assessment arterial input function；
Curve fitting module 300, for according to improved Gamma functions respectively to weighting artery input curve and when concentration
Half interval contour is fitted, the artery input curve and Cot curve optimized；
Parameter acquisition module 400, for being filled according to the improved decomposition of singular matrix system solution based on nonparametric model
The relative quantification parameter of note.
Further, in the MR perfusion imaging after-treatment system, the region is selected and output module 200 has
Body includes：
Unit is selected in region, and the regional choice for receiving user instructs, and obtains in brain perfusion image after the filtering and moves
The selection area in arteries and veins area；
Output unit is weighted, the concentration-time signal of the selection area for centering artery area is weighted smoothly, obtains
For the arterial input function that parameter calculates, i.e. output weighting artery input curve.
Further, in the MR perfusion imaging after-treatment system, the curve fitting module 300 is specifically wrapped
It includes：
Evaluation unit, for according to the maximum value of Cot curve, rising stage, the Gamma letters for declining phase Improvement of estimation
The initial value of number fitting；
Gamma function parameter acquiring units, in [T0,Tmax+ n] in range, the initial value that is exported in conjunction with evaluation unit
Obtain the Gamma function parameters for curve matching；Wherein T0It is initial arrival time, TmaxWhen being that peak-peak is corresponding
Between, n is the points that fitting is participated in after peak value.
Curve matching unit, for being fitted to weighting artery input curve and Cot curve, the artery optimized
Input curve and Cot curve.
Further, in the MR perfusion imaging after-treatment system, the parameter acquisition module 400 is specifically wrapped
It includes：
Resolving cell, for being carried out according to the matrix a in the improved decomposition of singular matrix method based on nonparametric model
Weighted array, and SVD is carried out to matrix a and decomposes to obtain diagonal matrix W, orthogonal matrix V, orthogonal matrix U；
Relative quantification parameter acquiring unit, for according to the threshold strategies diagonal matrix s that obtains that treated, and according to diagonal
Matrix s determines the relative quantification parameter of perfusion；Wherein s=1/W.
In conclusion the present invention provides a kind of MR perfusion imaging post-processing approach and system, method to include：To original
Beginning brain perfusion image filters, and removal brain edge obtains filtering hindbrain perfusion image；The regional choice instruction for receiving user, is obtained
Take the selection area in the middle artery area of brain perfusion image after the filtering, the weighting artery input of output assessment arterial input function bent
Line；Weighting artery input curve and Cot curve are fitted respectively according to improved Gamma functions, optimized
Artery input curve and Cot curve solve perfusion according to the improved decomposition of singular matrix method based on nonparametric model
Relative quantification parameter.The present invention reduces the susceptibility to noise, uses by being weighted optimization to AIF and solution matrix
Simplify and effective Gamma functions are fitted, converts nonlinear problem to linear solution, accelerate post-processing speed.
It, can according to the technique and scheme of the present invention and this hair it is understood that for those of ordinary skills
Bright design is subject to equivalent substitution or change, and all these changes or replacement should all belong to the guarantor of appended claims of the invention
Protect range.
Claims (6)
1. a kind of MR perfusion imaging post-processing approach, which is characterized in that the described method comprises the following steps：
A, original brain perfusion image is filtered, removal brain edge obtains filtering hindbrain perfusion image；
B, the regional choice instruction for receiving user, obtains the selection area in the middle artery area of brain perfusion image after the filtering, output
Assess the weighting artery input curve of arterial input function；
C, weighting artery input curve and Cot curve are fitted respectively according to improved Gamma functions, are obtained excellent
The artery input curve and Cot curve of change；
D, according to improved, the decomposition of singular matrix method based on nonparametric model solves the relative quantification parameter being perfused；
The step C is specifically included：
C1, according to the maximum value of Cot curve, the rising stage, decline phase Improvement of estimation Gamma Function Fittings initial value；
C2, combine Gamma Function Fittings in [T0, Tmax+n] curve ranges be initially worth to improved Gamma functions
Optimal Parameters；Wherein T0 is initial arrival time, and Tmax is the peak-peak corresponding time, and n is the point that fitting is participated in after peak value
Number；
C3, it is fitted according to the improved Gamma function pairs weighting artery input curve and Cot curve, what is optimized is dynamic
Arteries and veins input curve and Cot curve；
Gamma function expressions are as follows：
G (t)=A* (t-D)B*e(-(t-D)/C)
Wherein A, D, B, C are the Optimal Parameters for needing to estimate；T is time variable；
The initial value [A0, B0, C0, D0] of Gamma functions is estimated as follows：
A0 takes the peak-peak of Cot curve Ct；
D0 takes the first corner position i.e. contrast medium of Cot curve Ct to initially enter time of tissue；
C0 takes Cot curve Ct declining the time corresponding with D0 at moment；
B0=(Tmax-T0)/C0；Tmax is the A0 corresponding times.
2. MR perfusion imaging post-processing approach according to claim 1, which is characterized in that the step B is specifically included：
B1, the regional choice instruction for receiving user, obtain the selection area in the middle artery area of brain perfusion image after the filtering；
B2, centering artery area the concentration-time signal of selection area be weighted the artery for smoothly obtaining calculating for parameter
Input function, and weighting artery input curve is exported according to arterial input function.
3. MR perfusion imaging post-processing approach according to claim 1, which is characterized in that the step D is specifically included：
D1, according to the improved decomposition of singular matrix method based on nonparametric model to the concentration signal matrix of particular time range
It carries out decomposition of singular matrix and obtains diagonal matrix W, orthogonal matrix V, orthogonal matrix U；D2, obtain that treated according to threshold strategies
Diagonal matrix s, and the relative quantification parameter being perfused is determined according to diagonal matrix s；Wherein s=1/W.
4. a kind of MR perfusion imaging after-treatment system, which is characterized in that including：
Filter module, for being filtered to original brain perfusion image, removal brain edge obtains filtering hindbrain perfusion image；
Region is selected and output module, the regional choice for receiving user are instructed, obtained in brain perfusion image after the filtering
The selection area in artery area, the weighting artery input curve of output assessment arterial input function；
Curve fitting module is used for according to improved Gamma functions respectively to weighting artery input curve and Cot curve
It is fitted, the artery input curve and Cot curve optimized；
Parameter acquisition module, for according to the opposite of the improved decomposition of singular matrix system solution perfusion based on nonparametric model
Quantitative parameter；
Evaluation unit, for quasi- according to the maximum value of Cot curve, rising stage, the Gamma functions of decline phase Improvement of estimation
The initial value of conjunction；
Gamma function parameter acquiring units, in [T0, Tmax+n] range, being obtained in conjunction with the initial value that evaluation unit exports
Take the Gamma function parameters in curve matching；Wherein T0 is initial arrival time, and Tmax is peak-peak corresponding time, n
It is the points that fitting is participated in after peak value；
Curve matching unit, for quasi- according to the improved Gamma function pairs weighting artery input curve and Cot curve
It closes, the artery input curve and Cot curve optimized；
Gamma function expressions are as follows：
G (t)=A* (t-D)B*e(-(t-D)/C)
Wherein A, D, B, C are the Optimal Parameters for needing to estimate；T is time variable；
The initial value [A0, B0, C0, D0] of Gamma functions is estimated as follows：
A0 takes the peak-peak of Cot curve Ct；
D0 takes the first corner position i.e. contrast medium of Cot curve Ct to initially enter time of tissue；
C0 takes Cot curve Ct declining the time corresponding with D0 at moment；
B0=(Tmax-T0)/C0；Tmax is the A0 corresponding times.
5. MR perfusion imaging after-treatment system according to claim 4, which is characterized in that the region is selected and exports
Module specifically includes：
Unit is selected in region, and the regional choice for receiving user instructs, and obtains the middle artery area of brain perfusion image after the filtering
Selection area；
Output unit is weighted, the concentration-time signal of the selection area for centering artery area is weighted smoothly, is used for
The arterial input function that parameter calculates；Wherein, the arterial input function is weighting artery input curve.
6. MR perfusion imaging after-treatment system according to claim 4, which is characterized in that the parameter acquisition module tool
Body includes：
Resolving cell is used for according to the improved decomposition of singular matrix method based on nonparametric model to the dense of particular time range
Degree signal matrix carries out decomposition of singular matrix and obtains diagonal matrix W, orthogonal matrix V, orthogonal matrix U；
Relative quantification parameter acquiring unit, for according to the threshold strategies diagonal matrix s that obtains that treated, and according to diagonal matrix
S determines the relative quantification parameter of perfusion；Wherein s=1/W.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610019251.3A CN105701815B (en) | 2016-01-12 | 2016-01-12 | A kind of MR perfusion imaging post-processing approach and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610019251.3A CN105701815B (en) | 2016-01-12 | 2016-01-12 | A kind of MR perfusion imaging post-processing approach and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105701815A CN105701815A (en) | 2016-06-22 |
| CN105701815B true CN105701815B (en) | 2018-11-02 |
Family
ID=56226332
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610019251.3A Active CN105701815B (en) | 2016-01-12 | 2016-01-12 | A kind of MR perfusion imaging post-processing approach and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105701815B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106960458B (en) * | 2017-03-14 | 2020-08-25 | 深圳安科高技术股份有限公司 | Magnetic resonance magnetic sensitivity weighted imaging post-processing method and system |
| CN109726752A (en) * | 2018-12-25 | 2019-05-07 | 脑玺(上海)智能科技有限公司 | The dividing method and system of perfusion dynamic image based on time signal curve |
| CN110301900B (en) * | 2019-07-01 | 2020-06-05 | 脑玺(上海)智能科技有限公司 | Quantitative analysis method and system for dynamic perfusion image processing |
| CN111127586B (en) * | 2019-12-14 | 2021-10-29 | 深圳先进技术研究院 | Artery input function curve generation method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1689510A (en) * | 2004-04-19 | 2005-11-02 | 中国科学院自动化研究所 | Digitalized method for magnetic resonance perfusion imaging |
| CN101718848A (en) * | 2009-12-14 | 2010-06-02 | 西北工业大学 | Optimized quantitative method of blood volume in magnetic resonance perfusion imaged image |
| CN103261908A (en) * | 2010-10-11 | 2013-08-21 | 奥利尔医疗公司 | System and process for estimating a quantity of interest of a dynamic artery/tissue/vein system |
| WO2015039670A1 (en) * | 2013-09-19 | 2015-03-26 | Aarhus Universitet | A method for estimating perfusion indices |
-
2016
- 2016-01-12 CN CN201610019251.3A patent/CN105701815B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1689510A (en) * | 2004-04-19 | 2005-11-02 | 中国科学院自动化研究所 | Digitalized method for magnetic resonance perfusion imaging |
| CN101718848A (en) * | 2009-12-14 | 2010-06-02 | 西北工业大学 | Optimized quantitative method of blood volume in magnetic resonance perfusion imaged image |
| CN103261908A (en) * | 2010-10-11 | 2013-08-21 | 奥利尔医疗公司 | System and process for estimating a quantity of interest of a dynamic artery/tissue/vein system |
| WO2015039670A1 (en) * | 2013-09-19 | 2015-03-26 | Aarhus Universitet | A method for estimating perfusion indices |
Non-Patent Citations (1)
| Title |
|---|
| 奇异值分解在磁共振灌注成像中的研究;彭川;《万方数据》;20110328;第29-31页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105701815A (en) | 2016-06-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Windischberger et al. | On the origin of respiratory artifacts in BOLD-EPI of the human brain | |
| Fieselmann et al. | Deconvolution-based CT and MR brain perfusion measurement: theoretical model revisited and practical implementation details | |
| Vonken et al. | Maximum likelihood estimation of cerebral blood flow in dynamic susceptibility contrast MRI | |
| CN105701815B (en) | A kind of MR perfusion imaging post-processing approach and system | |
| Liu | Neurovascular factors in resting-state functional MRI | |
| US8301224B2 (en) | System and method for automatic, non-invasive diagnosis of pulmonary hypertension and measurement of mean pulmonary arterial pressure | |
| EP1171028B1 (en) | System for determining haemodynamic indices by use of tomographic data | |
| Beall | Adaptive cyclic physiologic noise modeling and correction in functional MRI | |
| Carbonell et al. | Quantification of the impact of a confounding variable on functional connectivity confirms anti-correlated networks in the resting-state | |
| US9095309B2 (en) | Method and apparatus for quantifying the behavior of an administered contrast agent | |
| Jakab et al. | Microvascular perfusion of the placenta, developing fetal liver, and lungs assessed with intravoxel incoherent motion imaging | |
| Goolaub et al. | Multidimensional fetal flow imaging with cardiovascular magnetic resonance: a feasibility study | |
| Jiang et al. | Reliability and reproducibility of perfusion MRI in cognitively normal subjects | |
| Piché et al. | Characterization of cardiac-related noise in fMRI of the cervical spinal cord | |
| US8774898B2 (en) | Oxygen-enhanced MR imaging technique | |
| US20110230756A1 (en) | Fluid flow assessment | |
| Cizadlo et al. | Image registration issues in the analysis of multiple injection 15OH2O PET studies: BRAINFIT | |
| Stöcklein et al. | Decreased craniocervical CSF flow in patients with normal pressure hydrocephalus: a pilot study | |
| Jackson et al. | Reproducibility and accuracy of automated measurement for dynamic arterial lumen area by cardiovascular magnetic resonance | |
| WO2021196866A1 (en) | Method and apparatus for measuring placental blood flow by using flow-compensated and non-flow compensated diffusion magnetic resonance | |
| Attenberger et al. | MR-based semi-automated quantification of renal functional parameters with a two-compartment model—an interobserver analysis | |
| Glass et al. | Hybrid artificial neural network segmentation of precise and accurate inversion recovery (PAIR) images from normal human brain☆ | |
| WO2014013476A1 (en) | A control point interpolation method for the quantification of cerebral haemodynamics | |
| Kohlmann et al. | Fully-Automatic Determination of the Arterial Input Function for Dynamic Contrast-Enhanced Pulmonary MR Imaging. | |
| Liu et al. | The Effects of Free Breathing on Cerebral Venous Flow: a real-time phase contrast MRI study in healthy adults |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |