CN102334992A - Dispersion tensor imaging method and system - Google Patents
Dispersion tensor imaging method and system Download PDFInfo
- Publication number
- CN102334992A CN102334992A CN2011103202980A CN201110320298A CN102334992A CN 102334992 A CN102334992 A CN 102334992A CN 2011103202980 A CN2011103202980 A CN 2011103202980A CN 201110320298 A CN201110320298 A CN 201110320298A CN 102334992 A CN102334992 A CN 102334992A
- Authority
- CN
- China
- Prior art keywords
- disperse
- probability density
- density function
- data
- preparatory
- 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.)
- Granted
Links
- 239000006185 dispersion Substances 0.000 title claims abstract description 57
- 238000003384 imaging method Methods 0.000 title claims abstract description 32
- 238000005070 sampling Methods 0.000 claims abstract description 79
- 238000000034 method Methods 0.000 claims abstract description 40
- 230000015572 biosynthetic process Effects 0.000 claims description 23
- 238000010586 diagram Methods 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 13
- 230000008569 process Effects 0.000 description 12
- 230000003044 adaptive effect Effects 0.000 description 10
- 230000000694 effects Effects 0.000 description 5
- 210000004885 white matter Anatomy 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 239000012141 concentrate Substances 0.000 description 2
- 238000009792 diffusion process Methods 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 238000005315 distribution function Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 210000001519 tissue Anatomy 0.000 description 2
- 238000005481 NMR spectroscopy Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000002597 diffusion-weighted imaging Methods 0.000 description 1
- 238000002599 functional magnetic resonance imaging Methods 0.000 description 1
- 210000002216 heart Anatomy 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 210000003734 kidney Anatomy 0.000 description 1
- 238000002595 magnetic resonance imaging Methods 0.000 description 1
- VYQNWZOUAUKGHI-UHFFFAOYSA-N monobenzone Chemical group C1=CC(O)=CC=C1OCC1=CC=CC=C1 VYQNWZOUAUKGHI-UHFFFAOYSA-N 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 210000002027 skeletal muscle Anatomy 0.000 description 1
Images
Abstract
The invention discloses a dispersion tensor imaging method. The method is characterized by comprising the following steps of: calculating a probability density function; performing variable density sampling according to the probability density function to acquire data corresponding to a dispersion-free image and multiple dispersion images; and rebuilding the data corresponding to the dispersion-free image and the multiple dispersion images to acquire a rebuilt image. By calculating the probability density function and performing variable density sampling on the data of the dispersion-free image and the multiple dispersion images according to the probability density function in the dispersion tensor imaging method, the data to be sampled are greatly reduced, the acquisition time is greatly reduced, and quick dispersion tensor imaging is realized. Moreover, the invention also provides a dispersion tensor imaging system.
Description
[technical field]
The present invention relates to mr techniques, particularly relate to a kind of dispersion tensor formation method and system.
[background technology]
Dispersion tensor imaging (DTI) is the new method that on diffusion-weighted imaging (DWI) basis, grows up, and is the specific form of nuclear magnetic resonance (MRI), is to develop new mr imaging technique rapidly in recent years.The utilization of dispersion tensor imaging technique is to utilize the disperse anisotropy of hydrone to be carried out to picture, can provide disease condition at cell and molecular level from the integrity of the field evaluation of tissue structure of microcosmic, is an important component part of functional mri.Dispersion tensor imaging is the inspection technology of unique non-invasive viviperception alba structure and white matter bundle form at present, also is that the inspection method of hydrone function of exchange situation between each component of organization is formed under information and the pathological state in present unique reflection biological tissue space.Based on the high spatial resolution of dispersion tensor imaging, the advantage of noninvasive; The dispersion tensor imaging is mainly used in the evaluation of brain all directions white matter fiber and white matter fiber tract; And expand to other positions (such as heart, kidney, skeletal muscle etc.) of human body gradually, can more information be provided for diagnosis, the treatment of disease.
The disperse figure that CALCULATION OF PARAMETERS needs a no disperse figure and a plurality of (at least 6) to apply the disperse gradient in the dispersion tensor imaging comes compute tensor D.This computational process is a fit procedure, and in order to make fitting effect better, we can adopt many b values to carry out match usually.B value in many b values is big more, and disperse is big more, and the signal to noise ratio of signal is also just more little; In order to satisfy the requirement of signal to noise ratio, need repeat repeatedly usually; In order to obtain more physiologic information, also will select a plurality of aspect imagings, but this has often caused total sampling data time long.In traditional dispersion tensor imaging process, total image data time can be very long, thereby cause whole complete dispersion tensor imaging process overlong time.
[summary of the invention]
Based on this, be necessary to provide a kind of formation method of dispersion tensor fast.
A kind of dispersion tensor formation method may further comprise the steps: the calculating probability density function; Carry out the variable density sampling according to said probability density function and obtain not having disperse figure and the pairing data of a plurality of disperse figure; Said no disperse figure and the pairing data of a plurality of disperse figure are rebuild obtain reconstructed image.
Further, also comprise before the step of said calculating probability density function: sampling in advance obtains preparatory sampled data; The step of said calculating probability density function is: respectively said preparatory sampled data is calculated probability density function.
Further, said preparatory sampled data comprises no disperse figure and the corresponding preparatory sampled data of a plurality of disperse figure; The said step that respectively said preparatory sampled data is calculated probability density function is specially: respectively said no disperse figure and the corresponding preparatory sampled data of a plurality of disperse figure are calculated no disperse figure and the corresponding probability density function of a plurality of disperse figure.
Further, saidly also comprise after respectively said preparatory sampled data being calculated the step of probability density function: said no disperse figure and the corresponding probability density function of a plurality of disperse figure are carried out match obtain the first unified probability density function; Saidly carry out the step that variable density sampling obtains not having disperse figure and the pairing data of a plurality of disperse figure according to said probability density function and be specially: carry out the variable density sampling according to the said first unified probability density function, obtain and said no disperse figure and the corresponding data of a plurality of disperse figure.
Further, saidly also comprise after respectively said preparatory sampled data being calculated the step of probability density function: the probability density function corresponding to a plurality of disperse figure carries out match, obtains the step of the second unified probability density function; Saidly carry out the step that variable density sampling obtains not having disperse figure and the pairing data of a plurality of disperse figure according to said probability density function and be specially: carry out variable density according to the probability density function of no disperse figure and sample, obtain not having the data of disperse figure; Carry out the variable density sampling according to the said second unified probability density function, obtain the pairing data of a plurality of disperse figure.
Further, said preparatory sampled data comprises the preparatory sampled data of no disperse figure or any disperse figure; The said step that respectively said preparatory sampled data is calculated probability density function is specially: the preparatory sampled data to said no disperse figure or any disperse figure calculates no disperse figure or the corresponding probability density function of any disperse figure; Saidly carry out the step that variable density sampling obtains not having disperse diagram data and the pairing data of a plurality of disperse figure according to said probability density function and be specially: carry out the variable density sampling according to said no disperse figure or the corresponding probability density function of any disperse figure, obtain not having disperse diagram data and the pairing data of a plurality of disperse figure.
Further, said preparatory sampled data comprises the preparatory sampled data of no disperse figure and any disperse figure; The said step that respectively said preparatory sampled data is calculated probability density function is specially: the preparatory sampled data to said no disperse figure and any disperse figure calculates no disperse figure and the corresponding probability density function of any disperse figure; Saidly carry out the step that variable density sampling obtains not having disperse diagram data and the pairing data of a plurality of disperse figure according to said probability density function and be specially: the probability density function according to said no disperse figure carries out the variable density sampling, obtains not having the data of disperse figure; Carry out the variable density sampling according to the corresponding probability density function of said arbitrary disperse figure, obtain the data of a plurality of disperse figure.
In addition, also be necessary to provide a kind of imaging system of dispersion tensor fast.
A kind of dispersion tensor imaging system is characterized in that, comprises processing module, sampling module and rebuilding module; Processing module is used for the calculating probability density function; Sampling module is electrically connected with said processing module, and said sampling module is used for carrying out the variable density sampling according to said probability density function and obtains not having disperse diagram data and disperse diagram data; Rebuilding module is electrically connected with said sampling module, is used for said no disperse figure and the pairing data of a plurality of disperse figure are rebuild obtaining reconstructed image.
Further, said sampling module also is used to sample in advance and obtains preparatory sampled data.
In above-mentioned dispersion tensor formation method and the system; Through calculating probability density function, and the data of no disperse figure and each disperse figure are carried out the variable density sampling, significantly reduced the data of required sampling according to probability density function; Make acquisition time significantly reduce, realize quick dispersion tensor imaging.
[description of drawings]
Fig. 1 is the flow chart of dispersion tensor formation method;
Fig. 2 is the sketch map of no disperse probability density function among another embodiment, each disperse probability function and the first unified probability density function;
Fig. 3 is the flow chart of the dispersion tensor formation method of another embodiment;
Fig. 4 is the flow chart of the dispersion tensor formation method of another embodiment;
Fig. 5 is the sketch map of no disperse probability density function among another embodiment, each disperse probability function and the second unified probability density function;
Fig. 6 is the flow chart of the dispersion tensor formation method of another embodiment;
Fig. 7 is the flow chart of the dispersion tensor formation method of another embodiment;
Fig. 8 is the module map of dispersion tensor imaging system.
[specific embodiment]
In order to solve the problem of image data overlong time total in traditional dispersion tensor imaging process, proposed the time that a kind of formation method of dispersion tensor fast shortens whole dispersion tensor imaging process.
CALCULATION OF PARAMETERS needs a no disperse figure and a plurality of disperse figure that has applied the disperse gradient to come compute tensor D in the dispersion tensor imaging (DTI), thereby carries out image reconstruction.D is the symmetrical matrix of a 3*3, so want to solve D, need apply six not disperse gradients of conllinear at least, promptly wants to calculate D, needs six disperse figure at least.Eigenvalue and characteristic vector through separating symmetrical matrix D just can obtain analyzing some parameters commonly used that dispersion tensor forms images; Like fractional anisotropy (fractional anisotropy; FA); Relatively anisotropy (relative anisotropy, RA) and apparent diffusion coefficient (apparent diffusion coefficient, ADC) etc.
See also Fig. 1, a kind of dispersion tensor formation method may further comprise the steps:
Step S10, the calculating probability density function.
Probability density function is used for confirming sampling density, with the control sampling process.In the sampling process of reality; Only partial data is sampled and to reduce consumed time effectively; And the information spinner of image will concentrate on low frequency region, and the contained quantity of information of high-frequency region is considerably less, therefore can sample through the mode of the employing variable density sampling controlled by probability density function; So both consumed time in the sampling process can be reduced, the generation of the pseudo-shadow of aliasing can also be reduced effectively.
Particularly, probability density function comprises the probability density function and the adaptive probability density function of non-self-adapting.Do not having to construct the probability density function of non-self-adapting, for example gauss of distribution function under the situation of prior information.The probability density function construction process of non-self-adapting is comparatively simple, can be applicable to the lower dispersion tensor imaging process of degree of accuracy.In a preferred embodiment, probability density function is adaptive probability density function, and adaptive probability density function is to construct according to the prior information that is looked like to form by a picture group, and its sample effect obviously is better than the probability density function of non-self-adapting.
Owing to more obtain better sample effect according to adaptive probability density function, in one embodiment, also comprise sampling in advance before the step S10 obtaining the step of preparatory sampled data.Simultaneously, step S10 is specially: respectively preparatory sampled data is calculated probability density function.Through sampling in advance, obtain prior information, make up adaptive probability density function.
Carry out a little square through the corresponding sampled data in advance of image institute in preparatory sampling institute on each row of phase code y direction, and add with, again through normalization, finally produce the corresponding probability density function of image.According to each image, make up corresponding adaptive probability density function, can obtain better sample effect.
Step S20 carries out the variable density sampling according to probability density function and obtains not having disperse figure and the pairing data of a plurality of disperse figure.
Because the energy of image mainly concentrates on low frequency region; The contained quantity of information of high-frequency region seldom; We just can adopt the mode of variable density sampling like this, mainly adopt low frequency signal, and the signal of high frequency region is adopted less or do not adopted as far as possible; So just can reduce the pseudo-shadow of aliasing effectively, improve the speed of sampling.According to adaptive probability density function the data of no disperse figure and a plurality of disperse gradient map are carried out the variable density sampling, only partial data is sampled, can significantly reduce sweep time.
Step S30 rebuilds no disperse figure and the pairing data of a plurality of disperse gradient map and to obtain reconstructed image.
The data of each image are carried out compressed sensing, and (Compressed Sensing CS) rebuilds and total variation is handled, and obtains reconstructed image.
In the above-mentioned dispersion tensor formation method; Through calculating probability density function, and the data of no disperse figure and each disperse figure are carried out the variable density sampling, significantly reduced the data of required sampling according to probability density function; Make acquisition time significantly reduce, realize quick dispersion tensor imaging.
As shown in Figure 2, B
0Be the probability density function of the no disperse figure that calculates according to real image, B
1～B
6Probability density function for each disperse figure of calculating according to real image.Can find out that by Fig. 2 each disperse figure and the correlation coefficient that does not have between the disperse figure approach 1.Therefore, as shown in Figure 3 in another embodiment in order further to reduce the time of whole dispersion tensor imaging process, sampled data is no disperse figure and the corresponding preparatory sampled data of a plurality of disperse figure in advance, and above-mentioned dispersion tensor formation method comprises the steps:
Step S301, sampling in advance obtains not having disperse figure and the corresponding preparatory sampled data of a plurality of disperse figure.
Step S303 calculates no disperse figure and the corresponding probability density function of a plurality of disperse figure to no disperse figure and the corresponding preparatory sampled data of a plurality of disperse figure respectively.
Step S305 carries out match to the corresponding probability density function of no disperse figure and a plurality of disperse figure and obtains the first unified probability density function.MeanB among Fig. 2
0-B
6The first unified probability density function that simulates for all probability density functions.
Step S307 carries out the variable density sampling according to the first unified probability density function, obtains and does not have disperse figure and the corresponding data of a plurality of disperse figure.
Probability density function through no disperse figure and a plurality of disperse figure; Simulate first a unified probability density function, and come the data of no disperse figure and a plurality of disperse figure are carried out the variable density sampling according to this first unified probability density function applicable to having or not disperse figure and a plurality of disperse figure.Based on the dependency between each image in the dispersion tensor imaging, adopt same probability density function that each image is carried out the variable density sampling, further saved acquisition time.
Step S309 rebuilds no disperse figure and the pairing data of a plurality of disperse figure and to obtain reconstructed image.
It is pointed out that through Fig. 3 and can find out that the probability density function of no disperse figure wants big with the diversity of the probability density function of a plurality of disperse figure than the diversity of the probability density function between each disperse figure.
In order to improve the precision of image, as shown in Figure 4 in another embodiment, step S305 and step S307 be replaceable to become following steps:
Step S401, the probability density function corresponding to a plurality of disperse figure carries out match, obtains the second unified probability density function.
Step S403 carries out the variable density sampling according to the probability density function of no disperse figure, obtains not having the data of disperse figure.
Step S405 carries out the variable density sampling according to the second unified probability density function, obtains the pairing data of a plurality of disperse figure.
See also Fig. 5, MeanB
1-B
6The second unified probability density function that simulates for the probability density function of all disperse figure.Above step is utilized the dependency between a plurality of disperse figure, makes up one second unified probability density function, comes the pairing data of disperse figure are sampled, and makes sampled result more accurate.
In another embodiment, as shown in Figure 6, sampled data is the preparatory sampled data of no disperse figure or any disperse figure in advance, and above-mentioned dispersion tensor formation method comprises the steps:
Step S601, sampling in advance obtains not having the preparatory sampled data of disperse figure or any disperse figure.
Step S603 calculates no disperse figure or the corresponding probability density function of any disperse figure to the preparatory sampled data of no disperse figure or any disperse figure.
Step S605 carries out the variable density sampling according to no disperse figure or the corresponding probability density function of any disperse figure, obtains not having disperse diagram data and the pairing data of a plurality of disperse figure.
Step S607 rebuilds no disperse figure and the pairing data of a plurality of disperse figure and to obtain reconstructed image.
Because each disperse figure and the correlation coefficient that does not have between the disperse figure approach 1; Here any one probability density function comes no disperse figure and a plurality of disperse figure are carried out the variable density sampling among the no disperse figure of employing or a plurality of disperse figure; Need not calculate the probability density function of other image, when guaranteeing certain accuracy, further reduce acquisition time.
In another embodiment, as shown in Figure 7 in order further to improve the speed of image data, sampled data is the preparatory sampled data of no disperse figure and any disperse figure in advance, and above-mentioned dispersion tensor formation method comprises the steps:
Step S701, sampling in advance obtains not having the preparatory sampled data of disperse figure and any disperse figure.
Step S703 calculates no disperse figure and the corresponding probability density function of any disperse figure to the preparatory sampled data of no disperse figure and any disperse figure.
Step S705 carries out the variable density sampling according to the probability density function of no disperse figure, obtains not having the data of disperse figure.
Step S707 carries out the variable density sampling according to the corresponding probability density function of arbitrary disperse figure, obtains the data of a plurality of disperse figure.
Step S709 rebuilds no disperse figure and the pairing data of a plurality of disperse figure and to obtain reconstructed image.
The probability density function of no disperse figure wants big with the diversity between the probability density function of a plurality of disperse figure than the diversity of the probability density function between each disperse figure, and the difference between the probability density function between each disperse figure is very little.Probability density function according to no disperse figure comes the data of no disperse figure are carried out the variable density sampling in the present embodiment; Probability density function according to arbitrary disperse figure comes the data of each disperse figure are carried out the variable density sampling, makes that the result of data acquisition is more accurate.
See also Fig. 8, a kind of dispersion tensor imaging system also is provided, comprise processing module 100, sampling module 200 and rebuilding module 300.
Rebuilding module 300 is electrically connected with sampling module 200, is used for said no disperse figure and the pairing data of a plurality of disperse figure are rebuild obtaining reconstructed image.The data of each image are carried out compressed sensing, and (Compressed Sensing CS) rebuilds and total variation is handled, and obtains reconstructed image.
In the above-mentioned dispersion tensor imaging system; Through calculating probability density function; And no disperse figure and the pairing data of a plurality of disperse figure are carried out the variable density sampling according to probability density function; Significantly reduce the data of required sampling, made acquisition time significantly reduce, realized quick dispersion tensor imaging.
It is to be noted; Because the dependency between each image; Processing module 100 can also be used for that the corresponding probability density function of a plurality of images is carried out match and obtain a probability density function; Only to use a probability density function that each image is sampled, make the speed of sampling further improve.
Simultaneously, processing module 100 also can only calculate the probability density function of any figure, and through this probability density function the data of no disperse figure and a plurality of disperse figure is carried out the variable density sampling.Because the dependency between each image is sampled to a plurality of images through same probability density function, when having guaranteed certain accuracy, has saved acquisition time.
The above embodiment has only expressed several kinds of embodiments of the present invention, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to claim of the present invention.Should be pointed out that for the person of ordinary skill of the art under the prerequisite that does not break away from the present invention's design, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with accompanying claims.
Claims (9)
1. a dispersion tensor formation method is characterized in that, may further comprise the steps:
The calculating probability density function;
Carry out the variable density sampling according to said probability density function and obtain not having disperse figure and the pairing data of a plurality of disperse figure;
Said no disperse figure and the pairing data of a plurality of disperse figure are rebuild obtain reconstructed image.
2. dispersion tensor formation method according to claim 1 is characterized in that, also comprises before the step of said calculating probability density function:
Sample in advance and obtain preparatory sampled data;
The step of said calculating probability density function is:
Respectively said preparatory sampled data is calculated probability density function.
3. dispersion tensor formation method according to claim 2 is characterized in that, said preparatory sampled data comprises no disperse figure and the corresponding preparatory sampled data of a plurality of disperse figure;
The said step that respectively said preparatory sampled data is calculated probability density function is specially:
Respectively said no disperse figure and the corresponding preparatory sampled data of a plurality of disperse figure are calculated no disperse figure and the corresponding probability density function of a plurality of disperse figure.
4. dispersion tensor formation method according to claim 3 is characterized in that, saidly also comprises after respectively said preparatory sampled data being calculated the step of probability density function:
The corresponding probability density function of said no disperse figure and a plurality of disperse figure is carried out match obtain the first unified probability density function;
Saidly carry out the step that variable density sampling obtains not having disperse figure and the pairing data of a plurality of disperse figure according to said probability density function and be specially:
Carry out the variable density sampling according to the said first unified probability density function, obtain and said no disperse figure and the corresponding data of a plurality of disperse figure.
5. dispersion tensor formation method according to claim 3 is characterized in that, saidly also comprises after respectively said preparatory sampled data being calculated the step of probability density function:
The probability density function corresponding to a plurality of disperse figure carries out match, obtains the step of the second unified probability density function;
Saidly carry out the step that variable density sampling obtains not having disperse figure and the pairing data of a plurality of disperse figure according to said probability density function and be specially:
Probability density function according to no disperse figure carries out the variable density sampling, obtains not having the data of disperse figure;
Carry out the variable density sampling according to the said second unified probability density function, obtain the pairing data of a plurality of disperse figure.
6. dispersion tensor formation method according to claim 2 is characterized in that, said preparatory sampled data comprises the preparatory sampled data of no disperse figure or any disperse figure;
The said step that respectively said preparatory sampled data is calculated probability density function is specially:
Preparatory sampled data to said no disperse figure or any disperse figure calculates no disperse figure or the corresponding probability density function of any disperse figure;
Saidly carry out the step that variable density sampling obtains not having disperse diagram data and the pairing data of a plurality of disperse figure according to said probability density function and be specially:
Carry out the variable density sampling according to said no disperse figure or the corresponding probability density function of any disperse figure, obtain not having disperse diagram data and the pairing data of a plurality of disperse figure.
7. dispersion tensor formation method according to claim 2 is characterized in that, said preparatory sampled data comprises the preparatory sampled data of no disperse figure and any disperse figure;
The said step that respectively said preparatory sampled data is calculated probability density function is specially:
Preparatory sampled data to said no disperse figure and any disperse figure calculates no disperse figure and the corresponding probability density function of any disperse figure;
Saidly carry out the step that variable density sampling obtains not having disperse diagram data and the pairing data of a plurality of disperse figure according to said probability density function and be specially:
Probability density function according to said no disperse figure carries out the variable density sampling, obtains not having the data of disperse figure;
Carry out the variable density sampling according to the corresponding probability density function of said arbitrary disperse figure, obtain the data of a plurality of disperse figure.
8. a dispersion tensor imaging system is characterized in that, comprising:
Processing module is used for the calculating probability density function;
Sampling module is electrically connected with said processing module, and said sampling module is used for carrying out the variable density sampling according to said probability density function and obtains not having disperse diagram data and disperse diagram data; And
Rebuilding module is electrically connected with said sampling module, is used for said no disperse figure and the pairing data of a plurality of disperse figure are rebuild obtaining reconstructed image.
9. dispersion tensor imaging system according to claim 8 is characterized in that, said sampling module also is used to sample in advance and obtains preparatory sampled data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110320298.0A CN102334992B (en) | 2011-10-19 | 2011-10-19 | Dispersion tensor imaging method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110320298.0A CN102334992B (en) | 2011-10-19 | 2011-10-19 | Dispersion tensor imaging method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102334992A true CN102334992A (en) | 2012-02-01 |
| CN102334992B CN102334992B (en) | 2014-04-16 |
Family
ID=45511108
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201110320298.0A Active CN102334992B (en) | 2011-10-19 | 2011-10-19 | Dispersion tensor imaging method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102334992B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103919552A (en) * | 2013-01-14 | 2014-07-16 | 张剑戈 | Water dispersion movement visualizing method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5539310A (en) * | 1993-08-06 | 1996-07-23 | The United States Of America As Represented By The Department Of Health And Human Services | Method and system for measuring the diffusion tensor and for diffusion tensor imaging |
| US20100308821A1 (en) * | 2007-10-25 | 2010-12-09 | Cyril Poupon | Real-Time Magnetic Resonance Diffusion Imaging |
-
2011
- 2011-10-19 CN CN201110320298.0A patent/CN102334992B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5539310A (en) * | 1993-08-06 | 1996-07-23 | The United States Of America As Represented By The Department Of Health And Human Services | Method and system for measuring the diffusion tensor and for diffusion tensor imaging |
| US20100308821A1 (en) * | 2007-10-25 | 2010-12-09 | Cyril Poupon | Real-Time Magnetic Resonance Diffusion Imaging |
Non-Patent Citations (5)
| Title |
|---|
| 《Magnetic Resonance in Medicine》 20041231 David S. Tuch Q-Ball Imaging 第1358-1372页 1-9 第52卷, 第6期 * |
| CHRISTOPHER L WELSH ET AL: "STRATEGIES FOR UNDERSAMPLING AND RECONSTRUCTING MR DTI DATA", 《2011 8TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING:FROM NANO TO MACRO PROCEEDINGS》 * |
| CHRISTOPHER L WELSH ET AL: "STRATEGIES FOR UNDERSAMPLING AND RECONSTRUCTING MR DTI DATA", 《2011 8TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING:FROM NANO TO MACRO PROCEEDINGS》, 2 April 2011 (2011-04-02) * |
| T.E.J. BEHRENS ET AL: "Characterization and Propagation of Uncertainty in Diffusion-Weighted MR Imaging", 《MAGNETIC RESONANCE IN MEDICINE》 * |
| T.E.J. BEHRENS ET AL: "Characterization and Propagation of Uncertainty in Diffusion-Weighted MR Imaging", 《MAGNETIC RESONANCE IN MEDICINE》, vol. 50, no. 5, 31 December 2003 (2003-12-31) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103919552A (en) * | 2013-01-14 | 2014-07-16 | 张剑戈 | Water dispersion movement visualizing method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102334992B (en) | 2014-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103477238B (en) | Compressed sensing MR image reconstruction using constraint from prior acquisition | |
| Chen et al. | High-resolution 3D MR Fingerprinting using parallel imaging and deep learning | |
| Tuch et al. | Diffusion MRI of complex neural architecture | |
| Sotiropoulos et al. | Advances in diffusion MRI acquisition and processing in the Human Connectome Project | |
| Jones | Tractography gone wild: probabilistic fibre tracking using the wild bootstrap with diffusion tensor MRI | |
| Summers et al. | A preliminary study of the effects of trigger timing on diffusion tensor imaging of the human spinal cord | |
| CN102309328B (en) | Diffusion-tensor imaging method and system | |
| CN106997034B (en) | Based on the magnetic resonance diffusion imaging method rebuild using Gauss model as example integration | |
| US8842896B2 (en) | Imaging of moving objects | |
| CN103262082B (en) | Magnetic resonance examination system with preferred settings based on data mining | |
| US7034531B1 (en) | Diffusion MRI using spherical shell sampling | |
| CN106574954A (en) | Parallel mr imaging with nyquist ghost correction for epi | |
| CN105283125B (en) | The MR of susceptibility contrast with enhancing is imaged | |
| WO2013098060A1 (en) | Mri with dixon-type water/fat separation and prior knowledge about inhomogeneity of the main magnetic field | |
| Landman et al. | Resolution of crossing fibers with constrained compressed sensing using traditional diffusion tensor MRI | |
| WO2017132180A1 (en) | System and method for optimized diffusion--weighted imaging | |
| CN104067137A (en) | Mr imaging with suppression of flow artefacts | |
| WO2018187764A1 (en) | Isotropic generalized diffusion tensor mri | |
| CN116774121A (en) | Magnetic resonance imaging system and method | |
| CN105051564A (en) | A method for improved k-space sampling in compressed sensing MRI | |
| Rathi et al. | Diffusion propagator estimation from sparse measurements in a tractography framework | |
| CN103282790B (en) | Quick double-contrast degree MR imaging | |
| Gilbert et al. | Mapping complex myoarchitecture in the bovine tongue with diffusion-spectrum magnetic resonance imaging | |
| Li et al. | Mapping brain anatomical connectivity using diffusion magnetic resonance imaging: Structural connectivity of the human brain | |
| CN103048632B (en) | A kind of quick fine angular resolution diffusion imaging method based on compressed sensing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20170210 Address after: 201807 Shanghai city Jiading District Industrial Zone Jiading Road No. 2258 Patentee after: Shanghai United Imaging Healthcare Co., Ltd. Address before: 1068 No. 518055 Guangdong city in Shenzhen Province, Nanshan District City Xili University School Avenue Patentee before: Shenzhen Institutes of Advanced Technology, Chinese Academy of Science |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20181012 Address after: 518055 Nanshan District, Xili, Shenzhen University, Shenzhen, Guangdong, 1068 Patentee after: Shenzhen Institutes of Advanced Technology, Chinese Academy of Science Address before: 201807 No. 2258 Chengbei Road, Jiading Industrial Zone, Jiading District, Shanghai. Patentee before: Shanghai United Imaging Healthcare Co., Ltd. |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20190730 Address after: 201807 Shanghai City, north of the city of Jiading District Road No. 2258 Patentee after: Shanghai United Imaging Healthcare Co., Ltd. Address before: 1068 No. 518055 Guangdong city in Shenzhen Province, Nanshan District City Xili Road School of Shenzhen University Patentee before: Shenzhen Institutes of Advanced Technology, Chinese Academy of Science |
|
| CP01 | Change in the name or title of a patent holder | ||
| CP01 | Change in the name or title of a patent holder |
Address after: 201807 Shanghai City, north of the city of Jiading District Road No. 2258 Patentee after: Shanghai Lianying Medical Technology Co., Ltd Address before: 201807 Shanghai City, north of the city of Jiading District Road No. 2258 Patentee before: SHANGHAI UNITED IMAGING HEALTHCARE Co.,Ltd. |