CN105467339A - Quick multilayer magnetic resonance imaging method and device - Google Patents
Quick multilayer magnetic resonance imaging method and device Download PDFInfo
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- CN105467339A CN105467339A CN201511030684.0A CN201511030684A CN105467339A CN 105467339 A CN105467339 A CN 105467339A CN 201511030684 A CN201511030684 A CN 201511030684A CN 105467339 A CN105467339 A CN 105467339A
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Abstract
The invention discloses a quick multilayer magnetic resonance imaging method. The method comprises steps: calculation of a sampling template is carried out, and the sampling template which gives consideration to requirements of both parallel imaging and compressive sensing imaging is generated; data are acquired according to the sampling template; the sampling data are rebuilt, wherein the rebuilding comprises compressive sensing rebuilding and parallel imaging rebuilding. The invention also discloses a quick multilayer magnetic resonance imaging device. According to the multilayer imaging sampling mode and the corresponding rebuilding method, an original image of a high quality can be rebuilt when only few samples need to be acquired, the acquisition line number in k space is reduced, the scanning time is reduced, and quick multilayer imaging can be realized.
Description
Technical field
The application relates to magnetic resonance imaging arts, is specifically related to a kind of Quick multi-layer MR imaging method and device.
Background technology
In order to improve magnetic resonance image (MRI) picking rate, parallel imaging technique is widely used in magnetic resonance imaging.This technology mainly utilizes the spatial sensitivities difference of single receiving coil in phased-array coil to carry out encodes spatial information, is reduced to as necessary phase encoding step number, obtains sweep velocity faster.Traditional reconstruction algorithm mainly contains SENSE (sensitivityencoding), GRAPPA (Generalizedautocalibratingpartiallyparallelacquisitions) etc.
It similarly is the another kind of fast imaging method utilizing coil sensitivities information that multilayer is excited into.Multilayer is excited into picture and excites multi-layer image at every turn, obtains the image of aliasing, and the difference of recycling interlayer coil sensitivities, by the image of aliasing separately, obtains multi-layer image.
Summary of the invention
The application provides a kind of Quick multi-layer MR imaging method, comprising: sample template calculates, and generates the sample template taking into account parallel imaging and compressed sensing imaging requirements; According to sample template image data; Described sampled data is rebuild, comprises and sequentially carry out compressed sensing reconstruction and parallel imaging reconstruction.
Above-mentionedly to comprise according to sampling masterplate image data: the multilayer excitation pulse adopting many plane phase to be shifted carrys out imaging, and the excitation pulse of adjacent layer has the phase differential of PI, the adjacent layer image shift FOV/2 after imaging.
Above-mentioned sample template calculates, and generates the sample template taking into account parallel imaging and compressed sensing imaging requirements and comprises: carry out variable density sampling evenly falling on the basis of adopting, form final sample template.
Above-mentioned reconstruction described compressed sensing comprises: according to reconstruction formula
rebuild the K space data gathered, wherein x is the image rebuild, and y is the k-space data collected, F
pfor the fourier descriptor of lack sampling, Ψ is sparse transformation, and λ is bound term coefficient.
Above-mentioned parallel imaging of stating is rebuild and is comprised: the view data obtained after rebuilding described compressed sensing to K space through inverse fourier transform, is rebuild the data concurrent reconstruction algorithm after described conversion, and is separated by multi-layer image.
According to the second aspect of the application, a kind of Quick multi-layer MR imaging apparatus is provided, comprises: template generation module, calculate for sample template, generate the sample template taking into account parallel imaging and compressed sensing imaging requirements; Sampling module, for according to described sample template image data; Rebuild module, for rebuilding described sampled data, comprising and sequentially carrying out compressed sensing reconstruction and parallel imaging reconstruction.
The multilayer excitation pulse of above-mentioned sampling module also for adopting many plane phase to be shifted carrys out imaging, and the excitation pulse of adjacent layer has the phase differential of PI, the adjacent layer image shift FOV/2 after imaging.
Above-mentioned template generation module also for carrying out variable density sampling evenly falling on the basis of adopting, forms final sample template.
Above-mentioned reconstruction module is also for according to reconstruction formula
rebuild the K space data gathered, wherein x is the image rebuild, and y is the k-space data collected, F
pfor the fourier descriptor of lack sampling, Ψ is sparse transformation, and λ is bound term coefficient.
Above-mentioned reconstruction module also for the view data that obtains after rebuilding described compressed sensing through inverse fourier transform to K space, the data concurrent reconstruction algorithm after described conversion is rebuild, and multi-layer image is separated.
Owing to have employed above technical scheme, the beneficial effect that the application is possessed is:
In the embodiment of the application, due to compressed sensing lack sampling and corresponding method for reconstructing, only need gather a small amount of sample and can reconstruct original image by high-quality, reduce the gathering line number of k-space, reduce sweep time, can Quick multi-layer imaging.
Accompanying drawing explanation
Fig. 1 is the process flow diagram according to the application's method embodiment;
Fig. 2 is the sampling masterplate according to the application's method embodiment;
Fig. 3 is the structural representation according to the application's device embodiment.
Embodiment
By reference to the accompanying drawings the present invention is described in further detail below by embodiment.
Embodiment one:
Fig. 1 is the process flow diagram according to the application's method embodiment, comprising:
Step 102: sample template calculates, generates the sample template taking into account parallel imaging and compressed sensing imaging requirements.A kind of embodiment, sampling masterplate is calculated as follows: entirely adopt along frequency coding direction (kx) direction, first evenly owe to adopt (accelerating multiple=R1) according to parallel imaging mode along phase-encoding direction, namely often R1 is capable gathers a phase encoding line; Then the basis of adopting carries out variable density sampling (accelerating multiple=R2) evenly falling, i.e. the line number=R2 of the line number/should gather of actual acquisition, variable density sampling pattern meets the requirement of compressed sensing imaging theory.Through calculating, obtain the phase encoding line position that finally need gather, i.e. sample template, as shown in Figure 2, wherein solid dot is the data point gathered, and hollow dots is the data do not gathered in parallel imaging, and plus sige is the data do not gathered in compressed sensing imaging.
Step 104: according to sample template image data.Adopt the multilayer excitation pulse of POMP (the many plane phase displacements of PhaseoffsetMultiplanar) technology to carry out imaging, the excitation pulse of adjacent layer has the phase differential of PI, the adjacent layer image shift FOV/2 after imaging.
Step 106: rebuild sampled data, comprises and sequentially carries out compressed sensing reconstruction and parallel imaging reconstruction.A kind of implementation, this step comprises further:
A, directly to rebuild owing to adopt data by compressed sensing, reconstructing overlapping multilayer and exciting image.
A kind of embodiment, adopts compressed sensing method for reconstructing, and directly rebuild the K space data gathered, reconstruction formula is as follows:
wherein x is the image rebuild, and y is the k-space data collected, F
pfor the fourier descriptor of lack sampling, Ψ is sparse transformation, and λ is bound term coefficient, adopts Nonlinear conjugate gradient descent algorithm to solve.
B, utilize SENSE method by the image of overlap separately, form two width images.
A kind of embodiment, is designated as the view data that obtains in A to K space through inverse fourier transform
will
rebuild with concurrent reconstruction algorithm (SENSE), obtain final reconstructed results.POMP pulse is adopted during owing to exciting, more abundant to the coil sensitivities Information Pull of interlayer, the noise in picture centre region can be reduced.
Embodiment two:
Fig. 3 is the structural representation according to the application's device embodiment, comprising: template generation module, sampling module and reconstruction module.
Template generation module, calculates for sample template, generates the sample template taking into account parallel imaging and compressed sensing imaging requirements.A kind of embodiment, carries out variable density sampling evenly falling on the basis of adopting, forms final sample template.Entirely sample along frequency coding direction, along phase-encoding direction according to parallel imaging mode uniform subsampling, carry out stochastic sampling according to variable density sampling pattern to the data collected, variable density sampling pattern meets the requirement of compressed sensing imaging theory.
Sampling module, for according to described sample template image data.A kind of embodiment, the multilayer excitation pulse adopting many plane phase to be shifted carrys out imaging, and the excitation pulse of adjacent layer has the phase differential of PI, the adjacent layer image shift FOV/2 after imaging.
Rebuild module, for rebuilding sampled data, comprising and sequentially carrying out compressed sensing reconstruction and parallel imaging reconstruction.A kind of embodiment, according to reconstruction formula
rebuild the K space data gathered, wherein x is the image rebuild, and y is the k-space data collected, F
pfor the fourier descriptor of lack sampling, Ψ is sparse transformation, and λ is bound term coefficient.The view data obtained after rebuilding compressed sensing to K space through inverse fourier transform, is rebuild the data concurrent reconstruction algorithm after described conversion, and is separated by multi-layer image.
Compressive sensing theory utilizes the openness of signal, only need gather a small amount of sample and can reconstruct raw data by high-quality.The application utilizes this theory, can reconstruct original image from the k-space of owing to adopt, thus reduces the gathering line number of k-space, reduces sweep time, reaches the object of fast imaging.
Above content is in conjunction with concrete embodiment further description made for the present invention, can not assert that specific embodiment of the invention is confined to these explanations.For general technical staff of the technical field of the invention, without departing from the inventive concept of the premise, some simple deduction or replace can also be made.
Claims (10)
1. a Quick multi-layer MR imaging method, is characterized in that, comprising:
Sample template calculates, and generates the sample template taking into account parallel imaging and compressed sensing imaging requirements;
According to described sample template image data;
Described sampled data is rebuild, comprises and sequentially carry out compressed sensing reconstruction and parallel imaging reconstruction.
2. the method for claim 1, is characterized in that, describedly comprises according to described sampling masterplate image data:
The multilayer excitation pulse adopting many plane phase to be shifted carrys out imaging, and the excitation pulse of adjacent layer has the phase differential of PI, the adjacent layer image shift FOV/2 after imaging.
3. the method for claim 1, is characterized in that, described sample template calculates, and generates the sample template taking into account parallel imaging and compressed sensing imaging requirements, comprising:
Carry out variable density sampling evenly falling on the basis of adopting, form final sample template.
4. the method for claim 1, is characterized in that, described compressed sensing is rebuild and comprised:
According to reconstruction formula
rebuild the K space data gathered, wherein x is the image rebuild, and y is the k-space data collected, F
pfor the fourier descriptor of lack sampling, Ψ is sparse transformation, and λ is bound term coefficient.
5. the method for claim 1, is characterized in that, described parallel imaging is rebuild and comprised:
The view data obtained after rebuilding described compressed sensing to K space through inverse fourier transform, is rebuild the data concurrent reconstruction algorithm after described conversion, and is separated by multi-layer image.
6. a Quick multi-layer MR imaging apparatus, is characterized in that, comprising:
Template generation module, calculates for sample template, generates the sample template taking into account parallel imaging and compressed sensing imaging requirements;
Sampling module, for according to described sample template image data;
Rebuild module, for rebuilding described sampled data, comprising and sequentially carrying out compressed sensing reconstruction and parallel imaging reconstruction.
7. device as claimed in claim 6, it is characterized in that, the multilayer excitation pulse of described sampling module also for adopting many plane phase to be shifted carrys out imaging, and the excitation pulse of adjacent layer has the phase differential of PI, the adjacent layer image shift FOV/2 after imaging.
8. device as claimed in claim 6, is characterized in that, described template generation module also for carrying out variable density sampling evenly falling on the basis of adopting, forms final sample template.
9. device as claimed in claim 6, is characterized in that, described reconstruction module is also for according to reconstruction formula
rebuild the K space data gathered, wherein x is the image rebuild, and y is the k-space data collected, F
pfor the fourier descriptor of lack sampling, Ψ is sparse transformation, and λ is bound term coefficient.
10. device as claimed in claim 6, it is characterized in that, described reconstruction module also for the view data that obtains after rebuilding described compressed sensing through inverse fourier transform to K space, the data concurrent reconstruction algorithm after described conversion is rebuild, and multi-layer image is separated.
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| CN106093814A (en) * | 2016-06-02 | 2016-11-09 | 浙江理工大学 | A kind of cardiac magnetic resonance imaging method based on multiple dimensioned low-rank model |
| CN106339982A (en) * | 2016-08-24 | 2017-01-18 | 深圳先进技术研究院 | Fast magnetic resonance heart real-time cine imaging method and fast magnetic resonance heart real-time cine imaging system |
| CN106597336A (en) * | 2016-11-29 | 2017-04-26 | 广东工业大学 | Scanning trajectory design method for MRI (magnetic resonance imaging) and device thereof |
| CN106997034A (en) * | 2017-04-25 | 2017-08-01 | 清华大学 | Based on the magnetic resonance diffusion imaging method that reconstruction is integrated by example of Gauss model |
| CN108564542A (en) * | 2018-04-04 | 2018-09-21 | 中国科学院长春光学精密机械与物理研究所 | A kind of control method of parallelly compressed perception imaging system |
| CN108825205A (en) * | 2018-04-09 | 2018-11-16 | 中国石油大学(北京) | Downhole NMR spectroscopic signal compressed sensing acquisition method and device |
| CN113534032A (en) * | 2020-04-14 | 2021-10-22 | 上海联影医疗科技股份有限公司 | Magnetic resonance imaging method and system |
| US11327132B2 (en) | 2017-06-29 | 2022-05-10 | Shanghai United Imaging Healthcare Co., Ltd. | System and method for magnetic resonance imaging acceleration |
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| CN106093814A (en) * | 2016-06-02 | 2016-11-09 | 浙江理工大学 | A kind of cardiac magnetic resonance imaging method based on multiple dimensioned low-rank model |
| CN106339982A (en) * | 2016-08-24 | 2017-01-18 | 深圳先进技术研究院 | Fast magnetic resonance heart real-time cine imaging method and fast magnetic resonance heart real-time cine imaging system |
| CN106339982B (en) * | 2016-08-24 | 2019-12-24 | 深圳先进技术研究院 | Rapid magnetic resonance heart real-time film imaging method and system |
| CN106597336A (en) * | 2016-11-29 | 2017-04-26 | 广东工业大学 | Scanning trajectory design method for MRI (magnetic resonance imaging) and device thereof |
| CN106997034A (en) * | 2017-04-25 | 2017-08-01 | 清华大学 | Based on the magnetic resonance diffusion imaging method that reconstruction is integrated by example of Gauss model |
| US11327132B2 (en) | 2017-06-29 | 2022-05-10 | Shanghai United Imaging Healthcare Co., Ltd. | System and method for magnetic resonance imaging acceleration |
| CN108564542A (en) * | 2018-04-04 | 2018-09-21 | 中国科学院长春光学精密机械与物理研究所 | A kind of control method of parallelly compressed perception imaging system |
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| CN108825205A (en) * | 2018-04-09 | 2018-11-16 | 中国石油大学(北京) | Downhole NMR spectroscopic signal compressed sensing acquisition method and device |
| CN108825205B (en) * | 2018-04-09 | 2020-09-22 | 中国石油大学(北京) | Method and device for compressed sensing acquisition of underground nuclear magnetic resonance spectrum signals |
| CN113534032A (en) * | 2020-04-14 | 2021-10-22 | 上海联影医疗科技股份有限公司 | Magnetic resonance imaging method and system |
| CN113534032B (en) * | 2020-04-14 | 2023-01-31 | 上海联影医疗科技股份有限公司 | Magnetic resonance imaging method and system |
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Application publication date: 20160406 |