CN105259525B - Dynamic constrasted enhancement magnetic resonance fast imaging method based on the perception of neighborhood share compressed - Google Patents
Dynamic constrasted enhancement magnetic resonance fast imaging method based on the perception of neighborhood share compressed Download PDFInfo
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Abstract
Compressed sensing sampling technique is integrated into the K space data Quick Acquisition and method for reconstructing that neighborhood shares sample track the invention proposes a kind of, for Dynamic constrasted enhancement magnetic resonance fast imaging, random division including the space K, the design of compressed sensing sampling matrix, the acquisition of neighborhood share compressed perception sampling matrix, data acquisition based on neighborhood share compressed cognitive method, five parts of data reconstruction based on neighborhood share compressed cognitive method, the sequence can further increase its scanning speed on the basis of neighborhood is shared, obtain higher imaging time resolution ratio and signal noise ratio (snr) of image, and it ensure that higher picture quality, it is more applicable for clinical dynamic Contrast enhanced magnetic resonance imaging.
Description
Technical field
The present invention is that a kind of neighborhood of integrated compressed sensing (compressed sensing, CS) technology shares (view
Sharing) K space sampling trajectories optimize acquisition method, belong to magnetic resonance medical imaging technology field, are capable of providing the high time point
Internal organs Dynamic constrasted enhancement (DCE-MRI) image of resolution and high s/n ratio, can be used for the medical diagnosis on disease, function of abdominal organs etc.
It can parameter quantitative measurement and prognostic evaluation etc..
Background technique
Chronic kidney disease and liver cancer have become major global public health problem, and in developed country, general population's is chronic
Nephrosis illness rate is up to 6.5%~16%, brings heavy burden to global Health Investment.According to 2012 one " China is slow
The epidemiological survey of property kidney trouble " the results show that the illness rate of chronic kidney disease has been up to 10.8% in China's Adult Groups,
It is expected that existing 1.2 hundred million people of adult Patients with Chronic Kidney Disease in the whole nation.In addition, the onset of liver cancer rate of developed country general population is up to
ASR 2.7~8.6, and disease incidence is improving year by year, mortality is up to ASR 2.5~7.1.Liver cancer has become world's model
The lethal second largest disease of interior male cancer is enclosed, only the newly-increased liver cancer patient 780,000 in the whole world in 2012, dead 740,000, wherein China
Liver cancer number of patients and death toll account for 50%.In abdomen clinical diagnosis, the dynamic contrast based on mr imaging technique
Enhancing inspection is the effective means of diagnosis of kidney disease and liver diseases.Therefore, improve the magnetic resonance of abdomen Dynamic constrasted enhancement at
The temporal resolution and picture quality of picture, to reach more accurate organ function quantitative measurment, for abdominal organs disease
Clinical diagnosis and prognostic evaluation have very important significance.
In abdomen Dynamic constrasted enhancement DCE-MRI imaging, since 3 dimension imaging technology can preferably show entire kidney
The form of the organs such as dirty, liver, and more image informations and perfusion curve are provided, thus by wide clinical application.Currently,
TWIST sequence is mainly differentiated using three-dimensional Fast spoiled gradient echo 3D FSPGR sequence and three-dimensional random trajectory time in clinic
Column carry out the magnetic resonance imaging of abdomen Dynamic constrasted enhancement.
Three-dimensional Fast spoiled gradient echo 3D FSPGR sequence uses small-angle radio frequency pulse excitation signal, then carries out phase
Position coding acquisition signal is finally added in gradient coding direction and disturbs phase gradient for remaining transverse magnetization vector dephasing, with
Shorten the sequence acquisition time.3D FSPGR sequence usually requires integrating parallel imaging technique, realizes faster abdomen dynamic contrast
Enhancing scanning, clinically since abdomen image coverage is big, phase code step number is more, and imaging time resolution ratio is by the very day of one's doom
System.
TWIST sequence shares the three-dimensional pseudorandom track sets of (view sharing) using neighborhood, and K is empty when data acquire
Between central area (A) carry out intensive sampling and outer region (B) and carry out sub-sampling, and in data reconstruction by the adjacent space K number
It is rebuild according to shared (view-shared) is merged, so as to shorten the acquisition time of each frame image.In the three-dimensional space K,
TWIST samples mechanism in phase encoding gradient kyWith layer choosing phase encoding gradient kzUpper execution, is expressed using polar coordinate mode, is adopted
Sample track is with radiation radius krAnd azimuth angle theta (π of 0 < θ < 2) increases gradually to periphery progress, sets a radiation radius
Threshold value kc, in threshold value kcWithin K space-intensive sampling, kcExcept the space K pseudorandom sub-sampling.The space K periphery sub-sampling mode is first
Periphery K area of space B is first randomly divided into the N number of subset B not overlapped each otheri(i=1 ... N), it is first when carrying out data acquisition
First acquiring a K spatial point is fully sampled data set #0, then successively acquires A, B1, A, B2, A, B1... it obtains at number of regions strong point
Obtain #1, #2, #3, #4, #5, #6 ... data set.When rebuilding, by the space #1, #3 and #5 former and later two even numbers K of odd-times acquisition
Data set is merged into current data set, is combined into the complete fully sampled space K and carries out image reconstruction, as shown in Figure 1.Institute
With the image frame per second of TWIST reconstruction image depends on the frame number of region A acquisition.
But it covers on a large scale in order to realize when clinical upper abdomen three-dimensional imaging and obtains enhancing figure high-resolution enough
Picture is normally set up the more number of plies (>=32 layers), larger sampling matrix (phase code ky>=128, frequency coding kx>=128) and it is more
Secondary excitation (Nex >=1), sequence time resolution ratio is between 10~60 seconds, so that the time-intensity curves collected point
Resolution is low, compensates far from the precise measurement for meeting functional parameter, or to sacrifice spatial resolution, causes image empty
Between resolution ratio decline.Clinical 3D FSPGR sequence scanning is fully sampled mode, it usually needs integrating parallel imaging technique
(parallel imaging) carries out picture signal acquisition, however, parallel imaging technique leads to signal noise ratio (snr) of image SNR reduction, and
Accelerate multiple to be typically limited within three times, accelerates speed higher, signal-to-noise ratio is lower, hinders further mentioning for temporal resolution
It is high.On the other hand, TWIST sequence scanning mode is interval sampling K space center region and outer region, is needed in reconstruction process
Adjacent K space data collection is merged, ensure that the higher spatial resolution of image, but makes the reduction of image frame per second, limit
The raising of its temporal resolution is made.It had not only improved temporal resolution so studying one kind but also can guarantee sufficiently high spatial discrimination
The magnetic resonance fast imaging method of rate is the important prerequisite for improving medical diagnosis on disease and functional parameter precise measurement.
Summary of the invention
In view of the above-mentioned problems, compressed sensing technology is integrated into the shared sampling rail of K spatial neighborhood the invention proposes a kind of
The quick sub-sampling method of K space data of (view-shared compressed sensing, VCS), improves abdomen in mark
The temporal resolution of Dynamic constrasted enhancement magnetic resonance imaging, while ensure that higher signal noise ratio (snr) of image and picture quality.
To achieve the above object, the present invention takes following technical scheme: a kind of Dynamic constrasted enhancement magnetic resonance imaging it is fast
The fast three-dimensional space K sub-sampling fill method, random division, the design of CS sampling matrix including the space K, the perception of neighborhood share compressed
The acquisition of sampling matrix, the data based on neighborhood share compressed cognitive method acquire, based on neighborhood share compressed cognitive method
Five parts of data reconstruction.Particular content are as follows: firstly, using the adjacent space K periphery data sharing sample track, a space K
In acquire central area and portion perimeter area data simultaneously, the shared space the K periphery sub-sampling track of neighborhood is each other without weight
Folded, all adjacent sub-sampling K space data combinations may be constructed the complete space K.Secondly, compressed sensing acceleration is adopted
Sample technology is embedded into the shared sample track in the adjacent space K, each space K is filled track and designed really by compression sensing method
Fixed, wherein the central area in the space K retains and does not share, and outer region is randomly divided into 2~5 after the design of compressed sensing sampling matrix
A shared subset, to realize that the neighborhood share compressed of continuous multiple K space sampling trajectories perceives sampling.The perception of neighborhood share compressed
Sampling acquires the shared subset in central area and 1 periphery in the space compressed sensing K in a K spatial sampling.The space K
For data when rebuilding, continuous multiple non-overlapping shared subsets are merged into the current space K, reassemble into and meet compressed sensing sampling
The data set of matrix track is used for further compressed sensing image reconstruction.The compressed sensing for being integrated into the shared sample track of neighborhood adds
Fast multiple can be set to 2~8 times.The design of compressed sensing sampling matrix is generated using non uniformly varying density function, and meets height
Sparse convert of this distribution, compressed sensing can be Fourier transformation, wavelet transformation, finite difference transformation etc..Finally, by neighborhood
The space the K sub-sampling data of shared recombination are through l1The nonlinear algorithm that normal form minimizes is rebuild to obtain image, and reconstruction formula is as follows:
minimizeλ1||ψS||1+λ2TV(S),
S is target image, λ in formula1And λ2Indicate the weight between ψ sparse expression and finite difference sparse expression TV, F table
Show Fourier transform corresponding with sub-sampling, diIndicate VCSiCollected k-space data, ε are generally arranged at ambient noise water
Guarantee the fidelity of reconstruction image under flat.And clinical criteria medical image is rewritten as according to DICOM format.
The present invention has the advantages that the present invention is a kind of abdomen of high time resolution due to taking above technical scheme
Portion's quick three-dimensional Dynamic constrasted enhancement imaging method, the space K filling mode use neighborhood technology of sharing, each K space data
Collect while acquiring central area and portion perimeter number of regions strong point, so that image frame per second remains unchanged；Each shared K of neighborhood
Spatial sampling is integrated with compressed sensing sampling technique, while the neighborhood for realizing data is shared and compressed sensing acquires, further
Shorten data acquisition time；Adjacent K space data collection is merged into reconstruction when neighborhood share compressed cognitive method is rebuild, is schemed
As information is not lost；Neighborhood share compressed cognitive method image reconstruction algorithm, which uses, is based on l1Normal form compressed sensing reconstruction algorithm,
The data that CS algorithm for reconstructing can obtain sub-sampling reliably reconstruct original image.The present invention can be widely applied to abdomen
The magnetic resonance imaging of internal organs Dynamic constrasted enhancement and its clinical application.
Detailed description of the invention
Fig. 1 is the space sequence K TWIST filling track schematic diagram:
Fig. 2 is neighborhood share compressed perception VCS sample mode schematic diagram:
Fig. 3 is neighborhood share compressed cognitive method VCS image reconstruction schematic diagram:
Specific embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings, specific as follows:
It is as shown in Figures 2 and 3 the embodiment of the present invention, random division, the design of CS sampling matrix, neighborhood including the space K
Share compressed perceives the acquisition of sampling matrix, the data acquisition based on neighborhood share compressed cognitive method, based on the shared pressure of neighborhood
Five parts of data reconstruction of contracting cognitive method, when work, first have to generate neighborhood share compressed perception sampling matrix VCS, then
Continuous dynamic K space data is acquired, finally acquired data are rebuild.
Neighborhood share compressed cognitive method first part is the random division in the space K, the k in the three-dimensional space Ky-kzPlane
It is interior, it is classified as central area A and outer region B, wherein region B is randomly divided into N number of subset B not overlapped each otheri(i=1,
2 ... N), as shown in Fig. 2, each subset Bi forms the K space tracking K for sharing (view-shared) for neighborhood in conjunction with Ai
(i=1,2 ... N), i.e., the set (K of white point and its periphery white point in dashed circle1, K2, K3) indicate the three of random division
A K space tracking.
Neighborhood share compressed cognitive method second part is the design of CS sampling matrix, and in the three-dimensional space K, phase is compiled in layer
Code (ky) and layer choosing phase code (kz) phase codes of two dimensions can carry out compression sampling, it is theoretical according to CS, using illiteracy
Special calot's method obtains the two-dimensional observation matrix Φ for meeting independent same distribution Gaussian ProfileCS, the sampling mechanism of the observing matrix meets
Non uniformly varying density function, and it is irrelevant with sparse matrix, in kyAnd kzSub-sampling, frequency coding direction are realized on direction
(kx) by hardware condition limited must be it is fully sampled.As shown in Fig. 2, when work, kyDirection and kzDirection is according to ΦCSSampling matrix
Carry out random coded simultaneously, i.e., in dashed circle the set (CS matrix) of white point and its periphery white point indicate to execute with
Machine coding.According to the phase code after this optimization, K space center region point is intensive sampling, and outer region is general by variable density
Rate density function is Random sparseness sampling outward, realizes the compression sampling of K space data.ΦCSThe practical step executed of phase code
The percentage that number accounts for the space K sum determines the temporal resolution of the imaging, and the space K points are set as 50% in the embodiment,
Realize that 2 times of CS accelerates sampling.
Neighborhood share compressed cognitive method Part III is the acquisition that neighborhood share compressed perceives sampling matrix, such as Fig. 2 institute
Show, firstly, the K space tracking K that neighborhood is sharedi(i=1,2 ... N) illustrate that the adjacent space K outer region executes different rails
Mark filling, then by 2 times of CS sampling matrix ΦCSSame KiMake mathematics and operation, obtains neighborhood share compressed and perceive sampling matrix
VCS(view-shared CS matrix)。VCSi(i=1,2 ... N) respectively indicates the neighborhood share compressed sense of successive image frame
Know sampling matrix.
Neighborhood share compressed cognitive method Part IV is the data acquisition based on neighborhood share compressed cognitive method, is passed through
The neighborhood for being embedded in compressed sensing shares sampling matrix VCS and carries out continuous Dynamic Data Acquiring, obtains Dynamic constrasted enhancement scanning
The original raw data data d in the space Ki(i=1,2 ... N), as shown in Figure 3.
Neighborhood share compressed cognitive method Part V is the data reconstruction based on neighborhood share compressed cognitive method, is such as schemed
Shown in 3, mechanism, the initial data d that will be collected first are sampled according to VCSiCarry out the shared (view- of adjacent data collection
Shared), the reconstruction data for meeting CS sample track are reassembled into, then, the reconstruction data of these recombinations are carried out based on l1Model
The nonlinear algorithm that formula minimizes is rebuild.Reconstruction formula is as follows:
minimizeλ1||ψS||1+λ2TV(S),
S is target image, λ1And λ2Indicate the weight between ψ sparse expression and finite difference sparse expression TV, F indicate with
The corresponding Fourier transform of sub-sampling, diIndicate VCSiCollected k-space data, ε be generally arranged at background noise level it
Get off to guarantee the fidelity of reconstruction image.
Finally, reconstruction image is rewritten as clinical criteria medical image according to DICOM format.
Claims (4)
1. a kind of space the quick three-dimensional K sub-sampling fill method of Dynamic constrasted enhancement magnetic resonance imaging, random by the space K are drawn
Divide, the design of compressed sensing sampling matrix, the acquisition of neighborhood share compressed perception sampling matrix, be based on neighborhood share compressed perception side
The data acquisition of method, the data reconstruction composition based on neighborhood share compressed cognitive method, it is characterised in that: use the adjacent space K
Track is filled in periphery data sharing, acquires central area and portion perimeter area data in a space K simultaneously, what neighborhood was shared
The space K periphery filling track is non-overlapping each other, and it is empty that all adjacent sub-sampling K space datas combinations constitute a complete K
Between, compressed sensing accelerates sampling technique to be embedded into the shared filling track in the adjacent space K, designs K using compression sensing method
Space fill track, wherein the space K central area retain do not share, outer region through compressed sensing sampling matrix design after with
Machine is divided into multiple shared subsets, and the perception sampling of neighborhood share compressed acquires the space compressed sensing K in a K spatial sampling
Subset is shared in central area and 1 periphery, and when rebuilding, continuous multiple non-overlapping shared subsets are merged into K space data
The current space K reassembles into the data set for meeting compressed sensing sampling matrix track, and the design of compressed sensing sampling matrix is using non-equal
Even variable density function generates, finally, by the space the K sub-sampling data of the shared recombination of neighborhood through l1The non-linear calculation that normal form minimizes
Method rebuilds to obtain image.
2. the space K sub-sampling fill method according to claim 1, it is characterised in that: outer region is adopted through compressed sensing
2 ~ 5 shared subsets are randomly divided into after sample matrix design.
3. the space K sub-sampling fill method according to claim 1, it is characterised in that: be integrated into the shared filling rail of neighborhood
It is 2 ~ 8 times that the compressed sensing of mark, which accelerates multiple,.
4. the space K sub-sampling fill method according to claim 1, it is characterised in that: the design of compressed sensing sampling matrix
It is generated using non uniformly varying density function, and meets Gaussian Profile, sparse convert of compressed sensing can be Fourier transformation, small
One of wave conversion, finite difference transformation.
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| CN110213467B (en) * | 2019-05-27 | 2020-10-23 | 中国科学院国家空间科学中心 | Multi-scale modulation compressed sensing imaging system and imaging method thereof |
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