CN108460810A - A kind of parallel MR image method for fast reconstruction of full variation - Google Patents
A kind of parallel MR image method for fast reconstruction of full variation Download PDFInfo
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- CN108460810A CN108460810A CN201810141967.XA CN201810141967A CN108460810A CN 108460810 A CN108460810 A CN 108460810A CN 201810141967 A CN201810141967 A CN 201810141967A CN 108460810 A CN108460810 A CN 108460810A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
- G06T11/008—Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
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- 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]
Abstract
The present invention provides a kind of parallel MR image method for fast reconstruction of full variation, belong to magnetic resonance imaging arts.The present invention is for Bregman division Operator Methods using the slow disadvantage of fixed step size solving speed, it is proposed that non-convex the problem of being difficult to solve of full variation regular terms has been effectively treated in a kind of Bregman splitting algorithms of variable step.The experimental results showed that the innovatory algorithm can not only obtain preferable image recovery effects, and there is good convergence and stability.
Description
Technical field
The present invention relates in image procossing magnetic resonance imaging arts more particularly to a kind of parallel MR figure of full variation
As method for fast reconstruction.
Background technology
Parallel MR imaging (parallel Magnetic Resonance Imaging, pMRI) technology is successfully answered
For in daily clinical trial.PMRI technologies acquire signal using multiple parallel receiving coils, utilize the sensitivity between coil
It spends difference and lack sampling is carried out to K space data, realize the coding of spatial positional information, reduce the step number of phase code, substantially subtract
The short sweep time of data, improve image resume speed.However what parallel MR imaging obtained is each parallel coil
Part visual field (Field of view, FOV) image of the overlapping of generation, to obtain the full FOV of a width without aliasing
Image then needs to carry out image recovery using the good algorithm for reconstructing of robustness.
Full variation (Total Variation, TV) model, is exactly briefly the total of signal absolute gradient in sampling process
With.Different from L1 models, TV models not only can effectively reconstruction image information, can also preferably retain the thin of image border
Save information.Although there is significant advantage, non-differentiability and nonlinear characteristic to keep it more more tired than solving L1 problems for TV regularizations
Difficulty, other existing image recovery methods further include Tikhonov-like regularizations, but this method is more likely to eliminate texture
Details significant advantage, the problem of equally existing be：Non-differentiability and nonlinear characteristic keep it more difficult than solving L1 problems.
Invention content
The technical problem to be solved by the present invention is to：For defect involved in background technology, a kind of full change is proposed
The parallel MR image method for fast reconstruction divided, can retain the marginal information of image, while inhibiting the ladder of smooth region
Effect has better image recovery effects.
The present invention uses following technical scheme to solve above-mentioned technical problem：
A kind of the step of parallel MR image method for fast reconstruction of full variation, the method for fast reconstruction includes：
Step 1 obtains parallel MR image, establishes the blur degradation model of parallel MR image；
Step 2 is weighted blur degradation model total variation constraint, is parallel MR by blur degradation model conversation
The Total Variation of image；
Step 3 carries out variable replacement using auxiliary variable to the Total Variation of parallel MR image, obtains parallel magnetic
The equivalent constraint Optimized model of resonance image；
Step 4, the secondary penalty term that parallel MR image and auxiliary variable are added in equivalent constraint Optimized model, obtain
To the unconstrained optimization model of parallel MR image and auxiliary variable；
Step 5 passes through the unconstrained optimization model solution to parallel MR image and auxiliary variable, acquisition restored map
Picture.
Further, a kind of parallel MR image method for fast reconstruction of full variation proposed by the invention, step 1 are built
The blur degradation model of vertical parallel MR image is；
Wherein Φ is the convex function of non-differentiability, and A is the matrix for describing imaging device, and f is measurement data, and B represents differential calculation
Son, u are the reconstruction image, CNFor image collection.
Further, the parallel MR image method for fast reconstruction of a kind of full variation proposed by the invention, institute in step 2
The Total Variation for stating parallel MR image is：
Wherein Φ is the convex function of non-differentiability, and f is measurement data, and B represents differential operator, and u is reconstruction image, and F is in Fu
Leaf transformation matrix, P are mask, S ∈ CN×NIt is the susceptibility mapping in j-th of channel, CN×NFor image complex matrix.
Further, the parallel MR image method for fast reconstruction of a kind of full variation proposed by the invention, described in step 3
The equivalent constraint Optimized model of parallel MR image is：
Wherein Φ is the convex function of non-differentiability, and A is the matrix for describing imaging device, and f is measurement data, and B represents differential calculation
Son, u are the reconstruction image.
Further, the parallel MR image method for fast reconstruction of a kind of full variation proposed by the invention, described in step 4
Parallel MR image and the unconstrained optimization model of auxiliary variable are：
Wherein Φ is the convex function of non-differentiability, and A is the matrix for describing imaging device, and f is measurement data, and B represents differential calculation
Son, u are the reconstruction image, and w represents auxiliary variable, and b represents Lagrange multiplier, and F is Fourier transform matrix, and P is mask,
S∈CN×NIt is the susceptibility mapping in j-th of channel, ρ is the parameter of positive integer.
Further, the parallel MR image method for fast reconstruction of a kind of full variation proposed by the invention, institute in step 5
It states and includes to the process of unconstrained optimization model solution：
Decomposition is optimized to the unconstrained optimization model, obtains the solving model of parallel MR image and auxiliary respectively
Help the solving model of variable；
The iterative solution formula of parallel MR image is calculated according to the solving model of parallel MR image；
The iterative solution formula of auxiliary variable is calculated according to the solving model of auxiliary variable.
Further, the parallel MR image method for fast reconstruction of a kind of full variation proposed by the invention, the calculating
The step of iterative solution formula of parallel MR image includes：
Derivation is carried out to the solving model of the parallel MR image, then parallel MR image is solved；
Fast Fourier Transform (FFT) and its inverse transformation are carried out to solving result, the iterative solution for obtaining parallel MR image is public
Formula.
Further, the parallel MR image method for fast reconstruction of a kind of full variation proposed by the invention, it is described parallel
The iterative solution formula of magnetic resonance image and auxiliary variable is：
Wherein, Φ () is the convex letter of non-differentiabilityjNumber, δkFor iteration step length, k is iterations, and ρ is punishment parameter, and β is
A positive parameter, u are reconstruction image, and F is Fourier transform matrix, and P is mask, S ∈ CN×NIt is the susceptibility in j-th of channel
Mapping, f is measurement data, and B represents differential operator, and w represents auxiliary variable, and b represents Lagrange multiplier.
Further, the parallel MR image method for fast reconstruction of a kind of full variation proposed by the invention, the iteration
The step-length of solution formula is calculated by following formula：
Wherein,For H () functionjGradient, u are reconstruction image, and F is Fourier transform matrix, and P is mask, S ∈
CN×NIt is the susceptibility mapping in j-th of channel.
The present invention has the following technical effects using above technical scheme is compared with the prior art：
1, the present invention can retain the edge of image by providing a kind of method for fast reconstruction of the full variation of parallel MR
Information, while inhibiting the alias of smooth region, there are better image recovery effects.
2, as the iteration of target function value changes, the receipts of the Bregman splitting algorithms of variable step proposed by the invention
It holds back effect and is better than traditional Bregman splitting algorithms.
Description of the drawings
Fig. 1 is a kind of method for fast reconstruction work flow diagram of the full variation of parallel MR provided by the invention.
Fig. 2 is the comparison of Bregman splitting algorithms (BOSVS), Bregman splitting algorithms and original image of variable step.
Fig. 3 is the oscillogram of the Bregman splitting algorithms of variable step.
Specific implementation mode
Technical scheme of the present invention is described in further detail below in conjunction with the accompanying drawings：
Those skilled in the art of the present technique are it is understood that unless otherwise defined, all terms used herein (including skill
Art term and scientific terminology) there is meaning identical with the general understanding of the those of ordinary skill in fields of the present invention.Also
It should be understood that those terms such as defined in the general dictionary should be understood that with in the context of the prior art
The consistent meaning of meaning, and unless defined as here, will not be explained with the meaning of idealization or too formal.
As shown in Figure 1, in the present embodiment, a kind of method for fast reconstruction of the full variation of parallel MR of the present invention
Include the following steps, specially：
Step 1 obtains parallel MR image, establishes the blur degradation model of parallel MR image, and described obscure is moved back
Changing model is：
Wherein Φ is the convex function of non-differentiability, and A is the matrix for describing imaging device, and f is measurement data, and B represents differential calculation
Son, u are the reconstruction image.
Step 2 is weighted blur degradation model total variation constraint, is parallel MR by blur degradation model conversation
The Total Variation of image, the Total Variation are：
Wherein F is Fourier transform matrix, and P is mask, S ∈ CN×NIt is the susceptibility mapping in j-th of channel.
Step 3 carries out variable replacement using auxiliary variable to the Total Variation of parallel MR image, obtains parallel magnetic
The equivalent constraint Optimized model of the equivalent constraint Optimized model of resonance image, the parallel MR image is：
Step 4, the secondary penalty term that parallel MR image and auxiliary variable are added in equivalent constraint Optimized model, obtain
To the unconstrained optimization model of parallel MR image and auxiliary variable, the unconstrained optimization model is：
Wherein b represents Lagrange multiplier, and ρ is the parameter of positive integer.
Step 5, by the unconstrained optimization model solution to parallel MR image and auxiliary variable, it is total to calculate parallel magnetic
Shake image.
The process to unconstrained optimization model solution includes：
(1) decomposition is optimized to the unconstrained optimization model, obtains the solving model of parallel MR image respectively
With the solving model of auxiliary variable；
(2) the iterative solution formula of parallel MR image is calculated according to the solving model of parallel MR image；
(3) the iterative solution formula of auxiliary variable is calculated according to the solving model of auxiliary variable.
Wherein, the step of iterative solution formula of calculating parallel MR image includes：
(1) derivation is carried out to the solving model of parallel MR image, then parallel MR image is solved；
(2) Fast Fourier Transform (FFT) is carried out to solving result and its inverse transformation, the iteration for obtaining parallel MR image is asked
Solution formula.
Further, the parallel MR image and the iterative solution formula of auxiliary variable are：
Wherein, δkFor iteration step length, k is iterations, and ρ is punishment parameter, and β is a positive parameter, and f is to measure number
According to w represents auxiliary variable.
Further, the step-length of the iterative solution formula is calculated by following formula：
Wherein,For the gradient of H () function.
With reference to shown in figure 2, Fig. 3, wherein Fig. 2 is the Bregman splitting algorithms (BOSVS) of variable step, Bregman division calculations
The comparison of method and original image, Fig. 3 are the oscillograms of the Bregman splitting algorithms of variable step.The experimental results showed that the improvement is calculated
Method can not only obtain preferable image recovery effects, and have good convergence and stability.
The above is only some embodiments of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (9)
1. a kind of parallel MR image method for fast reconstruction of full variation, which is characterized in that the step of the method for fast reconstruction
Suddenly include：
Step 1 obtains parallel MR image, establishes the blur degradation model of parallel MR image；
Step 2 is weighted blur degradation model total variation constraint, is parallel MR image by blur degradation model conversation
Total Variation；
Step 3 carries out variable replacement using auxiliary variable to the Total Variation of parallel MR image, obtains parallel MR
The equivalent constraint Optimized model of image；
Step 4, the secondary penalty term that parallel MR image and auxiliary variable are added in equivalent constraint Optimized model, obtain simultaneously
The unconstrained optimization model of row magnetic resonance image and auxiliary variable；
Step 5 passes through the unconstrained optimization model solution to parallel MR image and auxiliary variable, acquisition restored image.
2. a kind of parallel MR image method for fast reconstruction of full variation according to claim 1, which is characterized in that step
The blur degradation model of the rapid 1 parallel MR image established is；
Wherein Φ is the convex function of non-differentiability, and A is the matrix for describing imaging device, and f is measurement data, and B represents differential operator, u
For the reconstruction image, CNFor image collection.
3. a kind of parallel MR image method for fast reconstruction of full variation according to claim 1, which is characterized in that step
The Total Variation of parallel MR image described in rapid 2 is：
Wherein Φ is the convex function of non-differentiability, and f is measurement data, and B represents differential operator, and u is reconstruction image, and F is that Fourier becomes
Matrix is changed, P is mask, S ∈ CN×NIt is the susceptibility mapping in j-th of channel, CN×NFor image complex matrix.
4. a kind of parallel MR image method for fast reconstruction of full variation according to claim 1, which is characterized in that step
The equivalent constraint Optimized model of the rapid 3 parallel MR image is：
Wherein Φ is the convex function of non-differentiability, and A is the matrix for describing imaging device, and f is measurement data, and B represents differential operator, u
For the reconstruction image.
5. a kind of parallel MR image method for fast reconstruction of full variation according to claim 1, which is characterized in that step
The rapid 4 parallel MR image and the unconstrained optimization model of auxiliary variable are：
Wherein Φ is the convex function of non-differentiability, and A is the matrix for describing imaging device, and f is measurement data, and B represents differential operator, u
For the reconstruction image, w represents auxiliary variable, and b represents Lagrange multiplier, and F is Fourier transform matrix, and P is mask, S ∈
CN×NIt is the susceptibility mapping in j-th of channel, ρ is the parameter of positive integer.
6. a kind of parallel MR image method for fast reconstruction of full variation according to claim 1, which is characterized in that step
Include to the process of unconstrained optimization model solution described in rapid 5：
Decomposition is optimized to the unconstrained optimization model, the solving model and auxiliary for obtaining parallel MR image respectively become
The solving model of amount；
The iterative solution formula of parallel MR image is calculated according to the solving model of parallel MR image；
The iterative solution formula of auxiliary variable is calculated according to the solving model of auxiliary variable.
7. a kind of parallel MR image method for fast reconstruction of full variation according to claim 6, which is characterized in that institute
State calculate parallel MR image iterative solution formula the step of include：
Derivation is carried out to the solving model of the parallel MR image, then parallel MR image is solved；
Fast Fourier Transform (FFT) and its inverse transformation are carried out to solving result, obtain the iterative solution formula of parallel MR image.
8. a kind of parallel MR image method for fast reconstruction of full variation according to claim 7, which is characterized in that institute
The iterative solution formula for stating parallel MR image and auxiliary variable is：
Wherein, Φ () is the convex letter of non-differentiabilityjNumber, δkFor iteration step length, k is iterations, and ρ is punishment parameter, and β is positive
One parameter, u are reconstruction image, and F is Fourier transform matrix, and P is mask, S ∈ CN×NIt is the susceptibility mapping in j-th of channel,
F is measurement data, and B represents differential operator, and w represents auxiliary variable, and b represents Lagrange multiplier.
9. a kind of parallel MR image method for fast reconstruction of full variation according to claim 6 or 8, feature exist
In the step-length of the iterative solution formula is calculated by following formula：
Wherein,For the gradient of H () function, u is reconstruction image, and F is Fourier transform matrix, and P is mask, S ∈ CN×N
It is the susceptibility mapping in j-th of channel.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109920017A (en) * | 2019-01-16 | 2019-06-21 | 昆明理工大学 | The parallel MR imaging reconstructing method of the full variation Lp pseudonorm of joint from consistency based on feature vector |
| CN110118967A (en) * | 2019-06-03 | 2019-08-13 | 电子科技大学 | A kind of scanning radar orientation super-resolution imaging method based on total variation |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012117303A1 (en) * | 2011-03-01 | 2012-09-07 | Koninklijke Philips Electronics N.V. | Accelerated mr thermometry mapping involving an image ratio constrained reconstruction |
| CN103077544A (en) * | 2012-12-28 | 2013-05-01 | 深圳先进技术研究院 | Magnetic resonance parameter matching method and device and medical image processing equipment |
| CN104107044A (en) * | 2014-06-27 | 2014-10-22 | 山东大学（威海） | Compressed sensing magnetic resonance image reconstruction method based on TV norm and L1 norm |
| CN105678822A (en) * | 2016-01-13 | 2016-06-15 | 哈尔滨理工大学 | Three-regular magnetic resonance image reconstruction method based on Split Bregman iteration |
| CN106296668A (en) * | 2016-08-01 | 2017-01-04 | 南京邮电大学 | A kind of global image dividing method of multiresolution analysis |
| CN106651983A (en) * | 2016-12-27 | 2017-05-10 | 四川大学 | Magnetic resonance image reconstruction method and apparatus |
| EP3199968A1 (en) * | 2016-01-27 | 2017-08-02 | Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. | Autofocusing-based correction of b0 fluctuation-induced ghosting |
| CN107507149A (en) * | 2017-08-31 | 2017-12-22 | 深圳市智图医疗技术有限责任公司 | A kind of noise-reduction method and device of Magnetic resonance imaging image |
-
2018
- 2018-02-11 CN CN201810141967.XA patent/CN108460810A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012117303A1 (en) * | 2011-03-01 | 2012-09-07 | Koninklijke Philips Electronics N.V. | Accelerated mr thermometry mapping involving an image ratio constrained reconstruction |
| CN103077544A (en) * | 2012-12-28 | 2013-05-01 | 深圳先进技术研究院 | Magnetic resonance parameter matching method and device and medical image processing equipment |
| CN104107044A (en) * | 2014-06-27 | 2014-10-22 | 山东大学（威海） | Compressed sensing magnetic resonance image reconstruction method based on TV norm and L1 norm |
| CN105678822A (en) * | 2016-01-13 | 2016-06-15 | 哈尔滨理工大学 | Three-regular magnetic resonance image reconstruction method based on Split Bregman iteration |
| EP3199968A1 (en) * | 2016-01-27 | 2017-08-02 | Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. | Autofocusing-based correction of b0 fluctuation-induced ghosting |
| CN106296668A (en) * | 2016-08-01 | 2017-01-04 | 南京邮电大学 | A kind of global image dividing method of multiresolution analysis |
| CN106651983A (en) * | 2016-12-27 | 2017-05-10 | 四川大学 | Magnetic resonance image reconstruction method and apparatus |
| CN107507149A (en) * | 2017-08-31 | 2017-12-22 | 深圳市智图医疗技术有限责任公司 | A kind of noise-reduction method and device of Magnetic resonance imaging image |
Non-Patent Citations (3)
| Title |
|---|
| SHIYOU YAN ET AL: ""Alternating Direction Method of Multipliers with variable stepsize for Partially Parallel MR Image reconstruction"", 《PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE》 * |
| ZHI-PEI LIANG ET AL: ""PARALLEL IMAGING: SOME SIGNAL PROCESSING ISSUES AND SOLUTIONS"", 《SIAM JOURNAL ON SCIENTIFIC COMPUTING》 * |
| 杨俊锋: ""图像处理中全变差正则化数据拟合问题算法回顾"", 《运筹学学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109920017A (en) * | 2019-01-16 | 2019-06-21 | 昆明理工大学 | The parallel MR imaging reconstructing method of the full variation Lp pseudonorm of joint from consistency based on feature vector |
| CN109920017B (en) * | 2019-01-16 | 2022-06-21 | 昆明理工大学 | Parallel magnetic resonance imaging reconstruction method of joint total variation Lp pseudo norm based on self-consistency of feature vector |
| CN110118967A (en) * | 2019-06-03 | 2019-08-13 | 电子科技大学 | A kind of scanning radar orientation super-resolution imaging method based on total variation |
| CN110118967B (en) * | 2019-06-03 | 2021-06-01 | 电子科技大学 | Scanning radar azimuth super-resolution imaging method based on total variation |
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