CN104331681B - Method for obtaining contrast agent relaxation time - Google Patents
Method for obtaining contrast agent relaxation time Download PDFInfo
- Publication number
- CN104331681B CN104331681B CN201410470363.1A CN201410470363A CN104331681B CN 104331681 B CN104331681 B CN 104331681B CN 201410470363 A CN201410470363 A CN 201410470363A CN 104331681 B CN104331681 B CN 104331681B
- Authority
- CN
- China
- Prior art keywords
- image
- roi
- contrast agent
- time
- sample
- 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.)
- Expired - Fee Related
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
Abstract
The invention relates to a method for obtaining contrast agent relaxation time. The method comprises the following steps: automatically identifying a contrast agent sample in an image, precisely positioning a sample center point, and calculating the radius of a sample test tube, automatically selecting ROI (Region of Interest) by the center point of the test tube as the center according to the radius of the test tube, calculating the mean value of a pixel signal in the ROI, and experimenting a standard deviation; and finally, carrying out nonlinear fitting to an automatically-extracted signal, and accurately, quickly and efficiently calculating a spinning-lattice relaxation time T1 value and a spinning-lattice relaxation time T2 value of the contrast agent. A process that a plurality of samples are identified in the image and the ROI of the samples is selected is a computer automation process, an artificial process is omitted, and the positioning accuracy, the ROI shape consistency and the result accuracy of the invention are far better than those of a traditional method. The whole flow from ROI selection and ROI signal extraction to the nonlinear fitting is a fully-automatic process, and the time efficiency of the method is far higher than the time efficiency of the traditional method.
Description
Technical field
The present invention relates to a kind of magnetic resonance imaging arts, in more particularly to a kind of automation extraction of magnetic resonance contrast agent imaging
Signal and the method for being used to the Fitting Calculation contrast agent slow Henan time.
Background technology
Difference in signal strength between the tissue of picture contrast reflection two in magnetic resonance, contrast enhancing is to change this signal intensity
Process.Contrast agent, also known as contrast medium(Contrast agents)It is a kind of medicament, the physical characteristic of tissue can be changed, so that shadow
Physical parameter is rung, and then strengthens picture contrast.Magnetic resonance contrast agent changes image by changing the local magnetic environment of tissue
Contrast.In magnetic resonance imaging, magnetic resonance signal and its SPIN LATTICE slow Henan time produced by proton(During longitudinal Henan late
Between)T1 and spin-spin slow Henan time(The horizontal Henan time late)T2 decides contrast of the different tissues on MRI, magnetic
Resonance contrast agent interacts to influence the T1 and T2 slow Henan times with proton.
Magnetic resonance contrast agent is divided into paramagnetism, superparamagnetism and the class of ferromagnetism three according to its magnetic characteristic.Paramagnetic contrast's agent
It is made up of paramagnetic metal element, such as Gd, Mn.When contrast medium concentrations are low, mainly shorten T2 and make signal enhancing, concentration is high
When, then tissue T 1 shortens more than T2 effects, makes the reduction of MR signals, commonly uses positive contrast of its T1 effect as T1 weightings as in
Agent;Ferromagnetism and superparamagnetism contrast medium are made up of iron oxide, are different size microcrystalline metal particle, and the two influences local magnetic
Field uniformity and generation magnetic susceptibility effect, accelerate proton dephasing position, and the T2 slow Henan times shorten.
Therefore in synthetically prepared for contrast agent and application, T1 and T2 is the important attribute of contrast agent, is also that measurement is made
Shadow agent acts on the important indicator with influence, and the accurate measurement using mr techniques to the contrast agent slow Henan time turns into key.Magnetic
Compared to magnetic resonance spectroscopy techniques, the measurement to reagent sample has the time fast to resonance image-forming, the advantage such as efficiency is high, has turned at present
The important means of the slow Henan time measurement of contrast agent.
The T1 slow Henan times in mr imaging technique to sample reagent can generally use saturation recovery method or inversion recovery
Method is measured.Saturation recovery method utilizes SE sequences, by changing the recovery time in imaging parameters(TR)Value be imaged
To the image of different TR times;Reverse-revert method immediately SE Sequence composition after applying a 180 degree inversion pre-pulse,
180 degree upset prepulsing midpoint is defined as reversing time to the time interval of 90 degree of pulse center points(TI), by changing over picture
Parameter TI values carry out being imaged the image for obtaining the different TI times;Measurement to the T2 slow Henan times of sample can be used many echo SE sequences
Row, by the echo time for setting monotonic increase(TE)Imaging is acquired to each echo-signal, so as to obtain during different TE
Between image.
Sample in the image of the different imaging parameters times obtained to above-mentioned three kinds of imaging methods chooses ROI, extracts figure
As signal, then T1 and T2 values can be measured by doing non-linear fitting method.Existing some image processing softwares such as MRIcro
It is the ROI for manually choosing sample, obtains the average value of picture element signal in Each point in time ROI, it is then soft with Origin again
Part does nonlinear fitting and obtains sample T1 or T2 value;The method of the measurement slow Henan time of also some softwares such as ImageJ is to figure
Sample signal all pixels as in do nonlinear fitting, a map figure of T1 and T2 are finally given, finally in map figure left-hand seats
Animation ROI, using ROI in pixel average as sample T1 or T2 value.
Can calculate T1 the and T2 times of contrast agent by above-mentioned post processing of image method, but the first of the above method
It is individual to have the disadvantage:Either ROI is chosen in original image before(Region of Interest)Or afterwards on the map of T1 or T2
It is all manually process to choose ROI, is imaged for multiple samples(It is as even up to a hundred in being equipped with tens in Polaroid test tube case
Branch cuvette sample), manually process is very time-consuming and consistent for what ROI location of the core accuracy and shape were chosen
Property is very low;Second shortcoming is also to do nonlinear fitting using Origin softwares after above-mentioned first method extracts ROI signals,
Its process is carried out in two steps, and each data that the first step is obtained need to be input manually into Origin, and its process takes very much.And
Although above-mentioned second method directly does nonlinear fitting, be the map of T1 or T2 due to be pixel is carried out one by one it is non-linear
The Fitting Calculation, one side efficiency is very low, and another aspect magnetic resonance noise is very big to single pixel effect of signals, for being first fitted
The method accuracy for taking ROI calculating average values afterwards is not high.
The content of the invention
The present invention be directed to be obtained in present image processing method, the contrast agent slow Henan time is time-consuming, efficiency is low and acceptor
A kind of problem that viewing rings, it is proposed that method of acquisition contrast agent slow Henan time, contrast agent sample in automatic identification image, essence
True localizing sample central point calculates sample tube radius size, and is chosen automatically according to test tube radius size with test tube central point
ROI, calculates picture element signal average value, experimental standard deviation in ROI.Nonlinear fitting finally is done to the signal that automation is extracted,
The accurate T1 and T2 values for quickly and efficiently calculating contrast agent.
The technical scheme is that:A kind of method for obtaining the contrast agent slow Henan time, specifically includes following steps:
1)Read magnetic resonance samples view data:For image with sample signal and the best image of background signal contrast
On the basis of, wherein, for measure SPIN LATTICE slow Henan time T1 view data, with recovery time TR or reversing time TI most
On the basis of big image;For measure spin-spin slow Henan time T2 view data, with the echo time TE of monotonic increase most
On the basis of small image;
2)Automatic identification sample and centre of location selection ROI extraction signals:To step 1)The image of selection does simple two
Value is processed, and sample and background are distinguished;Then using the method for loop truss Hough transformation, by image data transformation for the center of circle is tired out
The parametric image data of enumeration, and the image data point corresponding to the peak point in parametric image data is defined as each radiography
The center of agent test tube;In conjunction with binary image, the radius of each test tube is accurately calculated;To each image with test tube central point
It is ROI centers, ROI is chosen with fixed proportion automation relative to test tube radius size;Calculate the average value of picture element signal in ROI
And experimental standard deviation;Digital numbering finally is carried out to each sample cell after automatic identification;
3)Nonlinear fitting calculates contrast agent T1 and T2 value:Using step 2)It is middle to extract each image each radiography for obtaining
The signal value of the ROI of agent sample,
And the imaging parameters value recovery time TR of the corresponding each image of saturation recovery method, reverse-revert method are corresponding every
The imaging parameters value reversing time TI of width image does nonlinear fitting and can obtain T1 values;Or the corresponding every width figure of many echo sequence methods
The imaging parameters value TE of picture does non-linear curve fitting and can obtain T2 values,
It is for the fitting formula that saturation recovery method surveys T1:
M = M0* [1-exp(-TR/T1)];
It is for the fitting formula that reverse-revert method surveys T1:
M = M0* [1-2exp(-TI/T1)] ;
It is for the fitting formula that many echo sequences survey T2:
M = M0*exp(-TE/T2) ;
M represents the contrast agent images signal value in the ROI that extraction is obtained, M in formula0Represent the maximum in M evolutionary processes
Value, during different TR time points and the corresponding M image signal values that obtain of extraction are substituted into correspondence fitting formula respectively, to M0 、
T1 is fitted calculating;
For reverse-revert method, the corresponding M image signal values difference that different TI time points and extraction are obtained
In substitution correspondence fitting formula, to M0, T1 be fitted calculating;
For many echo sequence methods, the corresponding M image signal values point that different TE time points and extraction are obtained
Dai Ru correspondence fitting formula in, to M0, T2 be fitted calculating;
The mathematical method of nonlinear fitting uses Levenberg-Marquardt alternative manners, finally measures each contrast agent
T1 or T2 values.
The beneficial effects of the present invention are:The method that the present invention obtains the contrast agent slow Henan time, recognizes multiple in the picture
It is computer automation process that sample chooses sample ROI processes, it is not necessary to artificial process, consistent in location accuracy, ROI shapes
Property, result accuracy be far above conventional method;The method is chosen from ROI, ROI signal extractions are to nonlinear fitting whole flow process
Full-automatic process, is significantly larger than conventional method in time efficiency.
Brief description of the drawings
Fig. 1 be the embodiment of the present invention in inversion recovery SE sequence TI parameters be 5000ms when contrast agent its magnetic resonance into
As figure;
Fig. 2 adds up a point parameter field image graph to carry out the center of circle after loop truss Hough transformation in the embodiment of the present invention to Fig. 1;
Fig. 3 is the numbering figure of contrast agent sample gone out to each automatic detection in the embodiment of the present invention.
Specific embodiment
Concrete scheme of the invention is as follows:
1st, automatic identification sample and centre of location selection ROI extraction signals
Magnetic resonance samples view data is read, is with the sample signal image best with background signal contrast for image
Benchmark.Wherein, it is maximum with recovery time TR or reversing time TI for the view data of measurement SPIN LATTICE slow Henan time T1
Image on the basis of;It is minimum with the echo time TE of monotonic increase for the view data for measuring spin-spin slow Henan time T2
Image on the basis of.Then simple binary conversion treatment first is done to the image chosen, sample and background is distinguished, then examined using circle
The method for surveying Hough transformation, by image data transformation for the center of circle adds up parametric image data a little, and by parametric image data
Peak point corresponding to image data point be defined as the center of each contrast agent test tube, effectively determining each contrast agent test tube
Center.In conjunction with binary image, the radius of each test tube is accurately calculated, and be with test tube central point to each image
ROI centers, ROI is chosen relative to test tube radius size with fixed proportion automation.Calculate ROI in picture element signal average value with
And experimental standard deviation.Digital numbering finally is carried out to each sample cell after automatic identification.
2nd, nonlinear fitting calculates contrast agent T1 and T2 value
Using the signal value of the ROI that each image each contrast agent sample for obtaining is extracted in above-mentioned steps 1, and saturation
The imaging parameters value recovery time TR of the corresponding each image of restoring method, the imaging parameters of the corresponding each image of reverse-revert method
Value reversing time TI does nonlinear fitting and can obtain T1 values;Or the imaging parameters value TE of the corresponding each image of many echo sequence methods
Do non-linear curve fitting and can obtain T2 values,
It is for the fitting formula that saturation recovery method surveys T1:
M = M0* [1-exp(-TR/T1)];
It is for the fitting formula that reverse-revert method surveys T1:
M = M0* [1-2exp(-TI/T1)] ;
It is for the fitting formula that many echo sequences survey T2:
M = M0*exp(-TE/T2) ;
M represents the contrast agent images signal value in the ROI that extraction is obtained, M in formula0Represent the maximum in M evolutionary processes
Value, is also one of parameter of needs fitting.For saturation recovery method, the correspondence that different TR time points and extraction are obtained
M image signal values substitute into above-mentioned formula respectively, to M0, T1 be fitted calculating;For reverse-revert method, will not
The corresponding M image signal values obtained with TI time points and extraction are substituted into above-mentioned formula respectively, to M0, T1 is fitted
Calculate;
For many echo sequence methods, the corresponding M image signal values point that different TE time points and extraction are obtained
Dai Ru not be in above-mentioned formula, to M0, T2 be fitted calculating.The mathematical method of nonlinear fitting uses Levenberg-
Marquardt alternative manners, finally measure T1 the or T2 values of each contrast agent.
Now as a example by the view data of T1 values of contrast agent is surveyed with reverse-revert method, step-by-step instructions is automated to image
The method that identification sample and the centre of location choose ROI extraction signal methods and nonlinear fitting calculating contrast agent slow Henan time.
The view data that the reverse-revert method of this example surveys the T1 values of contrast agent derives from Siemens MAGNETOM Trio
Tim 3.0T magnetic resonance imaging systems, imaging sequence is SE IR sequences, specific imaging parameters:TR/TE:9000ms/11ms,
FOV:640*640,ETL:7, choose 10 difference TI values and be imaged, respectively:24ms、100ms、200ms、400ms、
600ms、900ms、1200ms、2000ms、3000ms、5000ms.Contrast agent sample number totally 71 in the present embodiment.
First, automatic identification sample and centre of location selection ROI extraction signals, are contrasted with sample signal and background signal
On the basis of the best image of degree, for TI for the image of 5000ms is shown in accompanying drawing 1 in this example.Then simple binaryzation is first done to image
Treatment, sample and background are distinguished, and are that the center of circle adds up point by image data transformation then using the method for loop truss Hough transformation
Parametric image data, effectively determine that accompanying drawing 2 is seen at the center of each contrast agent test tube.In conjunction with binary image, accurate meter
Calculate the radius of each test tube, and to each image with test tube central point be ROI centers, relative to test tube radius size with fixation
ROI is chosen in ratio automation.Calculate the average value and experimental standard deviation of picture element signal in ROI.Finally to automatic identification after
Each sample cell carries out numeral numbering and sees accompanying drawing 3, and contrast agent sample is identified with the descendant for facilitating.
Followed by nonlinear fitting calculates contrast agent T1 and T2 value.It is right using Levenberg-Marquardt alternative manners
The ROI signal values of each contrast agent sample extraction do nonlinear fitting in each image, so as to measure the T1 of each contrast agent sample
Or T2 values.Formula is utilized in the present embodiment(2)To said extracted to signal do nonlinear fitting calculate, each is detected
Each contrast agent sample data carry out 100 times iterative calculation, finally give fitting the equation for obtaining and the T1 values for measuring.This reality
The part contrast agent sample result measured in example is shown in Table 1.
Table 1
Claims (1)
1. it is a kind of obtain the contrast agent slow Henan time method, it is characterised in that specifically include following steps:
1)Read magnetic resonance samples view data:For image with sample signal and the best image of background signal contrast as base
Standard, wherein, for the view data for measuring SPIN LATTICE slow Henan time T1, with recovery time TR or reversing time TI maximums
On the basis of image;For the view data for measuring spin-spin slow Henan time T2, with the echo time TE minimums of monotonic increase
On the basis of image;
2)Automatic identification sample and centre of location selection ROI extraction signals:To step 1)The image of selection does simple binaryzation
Treatment, sample and background are distinguished;Then it is that the center of circle adds up point by image data transformation using the method for loop truss Hough transformation
Parametric image data, and by the image data point corresponding to the peak point in parametric image data be defined as each contrast agent examination
The center of pipe;In conjunction with binary image, the radius of each test tube is accurately calculated;It is with test tube central point to each image
ROI centers, ROI is chosen relative to test tube radius size with fixed proportion automation;Calculate ROI in picture element signal average value with
And experimental standard deviation;Digital numbering finally is carried out to each sample cell after automatic identification;
3)Nonlinear fitting calculates contrast agent T1 and T2 value:Using step 2)It is middle to extract each image each contrast agent sample for obtaining
The signal value of the ROI of product, and the corresponding each image of saturation recovery method imaging parameters value recovery time TR, reverse-revert method
The imaging parameters value reversing time TI of corresponding each image does nonlinear fitting and can obtain T1 values;Or many echo sequence method correspondences
The imaging parameters value TE of each image do non-linear curve fitting and can obtain T2 values,
It is for the fitting formula that saturation recovery method surveys T1:
M = M0* [1-exp(-TR/T1)];
It is for the fitting formula that reverse-revert method surveys T1:
M = M0* [1-2exp(-TI/T1)] ;
It is for the fitting formula that many echo sequences survey T2:
M = M0*exp(-TE/T2) ;
M represents the contrast agent images signal value in the ROI that extraction is obtained, M in formula0The maximum in M evolutionary processes is represented, it is right
For saturation recovery method, the corresponding M image signal values that different TR time points and extraction are obtained are substituted into correspondence plan respectively
In conjunction formula, to M0, T1 be fitted calculating;
For reverse-revert method, the corresponding M image signal values that different TI time points and extraction are obtained are substituted into respectively
In correspondence fitting formula, to M0, T1 be fitted calculating;
For many echo sequence methods, by different TE time points and the corresponding M image signal values that obtain of extraction generation respectively
Enter correspondence fitting formula in, to M0, T2 be fitted calculating;
The mathematical method of nonlinear fitting uses Levenberg-Marquardt alternative manners, finally measures the T1 of each contrast agent
Or T2 values.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410470363.1A CN104331681B (en) | 2014-09-16 | 2014-09-16 | Method for obtaining contrast agent relaxation time |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201410470363.1A CN104331681B (en) | 2014-09-16 | 2014-09-16 | Method for obtaining contrast agent relaxation time |
Publications (2)
Publication Number | Publication Date |
---|---|
CN104331681A CN104331681A (en) | 2015-02-04 |
CN104331681B true CN104331681B (en) | 2017-05-24 |
Family
ID=52406403
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201410470363.1A Expired - Fee Related CN104331681B (en) | 2014-09-16 | 2014-09-16 | Method for obtaining contrast agent relaxation time |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN104331681B (en) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107015181B (en) | 2017-04-07 | 2020-01-14 | 厦门大学 | Method for measuring proton longitudinal relaxation time under inhomogeneous magnetic field |
CN111568420B (en) * | 2019-02-15 | 2021-05-14 | 深圳先进技术研究院 | Method and device for determining relaxation time in rotation coordinate and storage medium |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1509690A (en) * | 2002-12-20 | 2004-07-07 | Ge医药系统环球科技公司 | Analyzing method and apparatus for contrast medium intensity-duration curve |
CN1538796A (en) * | 2003-04-15 | 2004-10-20 | 西门子公司 | Method for digita image reducing angiography using primary stereo data |
CN1914642A (en) * | 2004-01-29 | 2007-02-14 | 皇家飞利浦电子股份有限公司 | Automatic segmentation of tissues by dynamic change characterization |
US8090176B2 (en) * | 2007-06-20 | 2012-01-03 | Siemens Aktiengesellschaft | Evaluation method for a temporal sequence of x-ray images |
-
2014
- 2014-09-16 CN CN201410470363.1A patent/CN104331681B/en not_active Expired - Fee Related
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1509690A (en) * | 2002-12-20 | 2004-07-07 | Ge医药系统环球科技公司 | Analyzing method and apparatus for contrast medium intensity-duration curve |
CN1538796A (en) * | 2003-04-15 | 2004-10-20 | 西门子公司 | Method for digita image reducing angiography using primary stereo data |
CN1914642A (en) * | 2004-01-29 | 2007-02-14 | 皇家飞利浦电子股份有限公司 | Automatic segmentation of tissues by dynamic change characterization |
US8090176B2 (en) * | 2007-06-20 | 2012-01-03 | Siemens Aktiengesellschaft | Evaluation method for a temporal sequence of x-ray images |
Also Published As
Publication number | Publication date |
---|---|
CN104331681A (en) | 2015-02-04 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Buonincontri et al. | Multi-site repeatability and reproducibility of MR fingerprinting of the healthy brain at 1.5 and 3.0 T | |
Ben‐Eliezer et al. | Rapid and accurate T2 mapping from multi–spin‐echo data using Bloch‐simulation‐based reconstruction | |
Moerel et al. | Sensitivity and specificity considerations for fMRI encoding, decoding, and mapping of auditory cortex at ultra-high field | |
EP3617733A1 (en) | Method and apparatus for processing magnetic resonance data using machine learning | |
Jeurissen et al. | Automated correction of improperly rotated diffusion gradient orientations in diffusion weighted MRI | |
Vos et al. | Trade-off between angular and spatial resolutions in in vivo fiber tractography | |
EP3385743A1 (en) | System and method for phase cycling magnetic resonance fingerprinting (phc-mrf) | |
Ambrosen et al. | Validation of structural brain connectivity networks: The impact of scanning parameters | |
US20120268120A1 (en) | Method for error compensated chemical species signal separation with magnetic resonance imaging | |
St-Jean et al. | Automated characterization of noise distributions in diffusion MRI data | |
CN104331681B (en) | Method for obtaining contrast agent relaxation time | |
US8995738B2 (en) | System and method for magnetic resonance imaging parametric mapping using confidence maps | |
CN105997074B (en) | A kind of magnetic resonance quantifies the more phase of echo approximating methods of susceptibility imaging | |
von Gladiss et al. | Reconstruction of 1D images with a neural network for magnetic particle imaging | |
Liao et al. | Referenceless distortion correction of gradient-echo echo-planar imaging under inhomogeneous magnetic fields based on a deep convolutional neural network | |
Snyder et al. | T2 quantification in brain using 3D fast spin‐echo imaging with long echo trains | |
US10429479B2 (en) | Rapid measurement of perfusion using optimized magnetic resonance fingerprinting | |
US11313931B2 (en) | System and method for quantifying T1, T2 and resonance frequency using rosette trajectory acquisition and read segmented reconstruction | |
JP2023156431A (en) | System and method for imaging tissue | |
CN106659420A (en) | Magnetic resonance imaging device | |
US11069063B2 (en) | Systems and methods for noise analysis | |
US10859653B2 (en) | Blind source separation in magnetic resonance fingerprinting | |
US20170172453A1 (en) | System and method for quantitative imaging using multiple magnetization pathways | |
Chen et al. | Magnetic resonance spectroscopy quantification aided by deep estimations of imperfection factors and overall macromolecular signal | |
US20190353736A1 (en) | System and Method for Quantifying Perfusion Using a Dictionary Matching |
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 | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170524 Termination date: 20190916 |
|
CF01 | Termination of patent right due to non-payment of annual fee |