CN103033782A - Parallel magnetic resonance imaging device and parallel magnetic resonance imaging method - Google Patents

Abstract

A parallel magnetic resonance imaging device comprises a plurality of imaging channels, a collection module, an initialization module, an operation module and a reconstruction module, wherein the collection module is used for collecting an undersampling matrix di (i is the serial number of each imaging channel and i > 0) from the plurality of imaging channels in an undersampling method and according to undersampling factors, the initialization module is used for acquiring an initialization image matrix rho and an initialization sensitivity matrix si (i is the serial number of each imaging channel and i > 0) of the imaging channels, the operation module is used for optimization iteration solving of a constraint function by means of the conjugate gradient algorithm according to the undersampling matrix di, the initialization image matrix rho and the initialization sensitivity matrix si to obtain a reconstructed image matrix rho and a sensitivity matrix si, and the reconstruction module is used for reconstructing an image according to the reconstructed image matrix rho and the sensitivity matrix si. When the parallel magnetic resonance imaging device is used for image reconstruction, imaging speed is effectively increased, and signal to noise ratio loss is small.

Description

The method of parallel MR imaging device and imaging thereof

Technical field

The present invention relates to medical skill, relate in particular to the method for a kind of parallel MR imaging device and imaging thereof.

Background technology

MRI has become one of important means of clinical medicine inspection, for clinical medicine provides very valuable diagnostic message.The MRI technology is compared with other Medical Imaging Technology, have the advantages such as radiationless harm, multi-faceted and multiparameter imaging, its inspection to soft tissue is very responsive, not only can show the shape information of human anatomic structure, and can also reflect some Physiology and biochemistry information of tissue.But the image taking speed of MRI is slower, physiological motion in the imaging process in examinee's health all can make image fog, contrast distortion, therefore how can't satisfy the requirement of the fast imagings such as heart dynamic imaging, cerebral function imaging, human motion imaging and cardiovascular and cerebrovascular, high-resolution realization FastMRI has become the key of MRI technical development and application.

In the MRI investigation of nearest three more than ten years, the researcher has proposed a variety of fast imaging methods.Such as the fast imaging sequence that shortens the sampling time according to the MRI principle, such as echo planar imaging imaging (EPI:Echo planar Imaging), screw propeller imaging, the gtadient echo of reading soon and spin-echo sequence etc. fast.Yet in most cases, we are by gathering a small amount of data and unite and adopt these fast imaging sequences to shorten the whole sampling time, then obtaining complete K spatial data by subsequent reconstruction.Parallel MR imaging (Parallel MRI:pMRI), compressed sensing imaging (Compressed Sensing:CS) etc. are typically arranged.Parallel imaging can be divided into again based on parallel (the Generalized autocalibrating partially parallel acquisitio ns:GRAPPA) algorithm and theoretical according to handkerchief Preece Generalized sampling based on susceptibility coding (Sensitivity encoding Sense) imaging of image area processing that obtains of the self-correcting of K spatial manipulation, when we to the sensitivity matrix in the Sense algorithm estimate comparatively accurately the time, in the parallel imaging method, it can obtain better picture quality than GRAPPA.Yet sensitivity is the pre-surface sweeping by some low resolution images or some separation to be obtained, and is difficult to like this obtain accurately sensitivity matrix, therefore is difficult to reconstruct high-quality image.The nonlinear iteration method (Joint estimation of coil sensitivities and image by nonlinear iterative methods:JSENSE) of estimation is united in the people such as Uecker proposition to coil sensitivity and image, the method is to obtain a kind of effective ways that high-quality Sense rebuilds, yet along with the increase of owing to adopt the factor, the signal to noise ratio (S/N ratio) of reconstructed image with traditional Sense method equally descend very fast.Address this problem, have the researcher that some prior imformations have been incorporated among the pMRI as bound term.

CS utilizes the sparse property of image to improve MRI to gather, and the method and pMRI has been combined at present and has accelerated faster the MRI image taking speed.We are divided into three major types to this associated methods: a kind of is to utilize coil sensitivity map to rebuild as the L1 norm restriction of prior imformation, sparseSENSE for example, in sparseSENSE, the coil sensitivity value is rebuild the same with traditional Sense, the data of first the sensitivity matrix utilization being owed to adopt or the prescanned data of separation obtain, and then come reconstructed image in conjunction with the CS sparse constraint, yet, this method is the same with traditional Sense, sensitivity estimate inaccurate the time, larger pseudo-shadow still appears.The second is to add the CS sparse constraint in GRAPPA, and this method is to utilize the priori image data to obtain some weighted values to estimate not adopt data, yet, in the middle of utilizing, entirely adopt data and go not image data of estimated edge, can not estimate very accurately.The third is as sparseSENSE, only image and sensitivity matrix are what to rebuild out simultaneously, according to prior-constrained difference, such can be divided into a variety of methods again, in reconstruction formula, have plenty of use final image sparse property as prior-constrained, have plenty of the coil sensitivity slickness of employing or sparse property as prior-constrained, but that image is made an uproar is larger than loss.

Summary of the invention

In view of this, be necessary to provide imaging device and the formation method thereof that a kind of image acquisition is fast and the signal noise ratio (snr) of image loss is little.

A kind of parallel MR imaging device provided by the invention comprises a plurality of imaging bands, acquisition module, initialization module, computing module and rebuilds module.Wherein: acquisition module is used for each imaging band utilization mode of owing to adopt is owed to adopt matrix d according to owing to adopt factor collection _i(i＞0 is the numbering of imaging band); Initialization module is used for obtaining the initialization sensitivity matrix s of initialisation image matrix ρ and described imaging band _i(i＞0 is the numbering of imaging band); Computing module is used for owing to adopt matrix d according to described _i, initialisation image matrix ρ and initialization sensitivity matrix s _iUtilize conjugate gradient algorithm that constraint function is optimized iterative, obtain reconstructed image matrix ρ and sensitivity matrix s _iRebuilding module is used for according to described reconstructed image matrix ρ and described sensitivity matrix s _iReconstructed image.

The present invention also provides a kind of formation method of parallel MR imaging device, and described parallel MR imaging device comprises a plurality of imaging bands, may further comprise the steps: each imaging band utilization mode of owing to adopt is owed to adopt matrix d according to owing to adopt factor collection _iObtain the initialization sensitivity matrix s of initialisation image matrix ρ and described imaging band _iOwe to adopt matrix d according to described _i, initialisation image matrix ρ and initialization sensitivity matrix s _iUtilize conjugate gradient algorithm that constraint function is optimized iterative, obtain reconstructed image matrix ρ and sensitivity matrix s _iAccording to described reconstructed image matrix ρ and sensitivity matrix reconstructed image s _i

Parallel MR imaging device among the present invention and formation method thereof are owed the mode of adopting collection by utilization and are owed to adopt matrix, and obtain initialisation image matrix ρ and initialization sensitivity matrix s _iAfter utilize conjugate gradient algorithm that constraint function is optimized iterative, to realize the reconstruction to image, effectively raise image taking speed, and the snr loss is little.

Description of drawings

Fig. 1 is the module map of parallel MR imaging device in an embodiment of the present invention;

Fig. 2 is the process flow diagram that utilizes the method for parallel MR imaging device imaging shown in Figure 1 in an embodiment of the present invention;

Fig. 3 is original image;

Fig. 4 A is the image that utilizes after sparseSENSE rebuilds Fig. 3;

Fig. 4 B is the image after utilizing method among the present invention that Fig. 3 is rebuild.

Embodiment

The below describes embodiments of the invention in detail, and the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or the element with identical or similar functions from start to finish.Be exemplary below by the embodiment that is described with reference to the drawings, only be used for explaining the present invention, and can not be interpreted as limitation of the present invention.

In description of the invention, term " interior ", " outward ", " vertically ", " laterally ", " on ", orientation or the position relationship of the indications such as D score, " top ", " end " be based on orientation shown in the drawings or position relationship, only be for convenience of description the present invention rather than require the present invention with specific orientation structure and operation, therefore can not be interpreted as limitation of the present invention.

See also Fig. 1, Figure 1 shows that the module map of parallel MR imaging device 10 in an embodiment of the present invention.

In the present embodiment, parallel MR imaging device 10 comprises: acquisition module 102, initialization module 104, computing module 106, reconstruction module 108, processor 110 and storer 112, module 102, acquisition module 104, judge module 106 and fractionation module 108 are set are stored in the storer 112, processor 110 is used for the modules of execute store 112.

In the present embodiment, parallel MR imaging device 10 also comprises a plurality of imaging band (not shown), and wherein, each imaging band is comprised of coil.

In the present embodiment, acquisition module 102 is used for each imaging band utilization mode of owing to adopt is owed to adopt matrix di (i＞0 is the numbering of imaging band) according to owing to adopt factor collection.In the present embodiment, the size that each passage gathers out owes to adopt matrix di is the same, but out owe to adopt that just full benefit that does not gather among the matrix di be zero.

In the present embodiment, the described mode of adopting of owing refers to center section in the described imaging band is all gathered, and peripheral part interlacing collection or both sides are owed sampling or radially sparse sampling or spiral sparse sampling at random.

Initialization module 104 is used for obtaining the initialization sensitivity matrix s of initialisation image matrix ρ and described imaging band _i(i＞0 is the numbering of imaging band).

In the present embodiment, described initialization sensitivity matrix s _i, described initialisation image matrix ρ number identical with described port number.

In the present embodiment, described initialisation image matrix ρ is 1 matrix entirely for value.

In the present embodiment, initialization sensitivity matrix s _iBe 0 matrix entirely for being worth.

Computing module 106 is used for owing to adopt matrix d according to described _i, initialisation image matrix ρ and initialization sensitivity matrix s _iUtilize conjugate gradient algorithm that constraint function is optimized iterative, obtain reconstructed image matrix ρ and sensitivity matrix s _i

In the present embodiment, described computing module 106 utilizes the solving model of conjugate gradient algorithm $\underset{\mathrm{\ρ},{\hat{s}}_1,\·\·\·,{\hat{s}}_n}{\arg \min }{\left|\right|\left(\begin{array}{c}{d}_1\\ d_2\\ .\\ .\\ .\\ d_n\end{array}\right)-\left(\begin{array}{c}\mathrm{PF\ρ}.M{\hat{s}}_1\\ \mathrm{PF\ρ}.M{\hat{s}}_2\\ .\\ .\\ .\\ \mathrm{PF\ρ}.M{\hat{s}}_n\end{array}\right)\left|\right|}_2+\mathrm{\λ}{\mathrm{\Σ}}_{i=1}^n{|\mathrm{W\ρ}.M{\hat{s}}_i|}_1$ Iterative, wherein, F is that Fourier changes, and P owes to adopt the factor, and W is sparse variation operator, and described M figures son as smoothing operator with Suo Bailiefu, for: $M=(1/{(1+|\left|\stackrel{\→}{k}\right|\left|\right)}^{1/2}).$

In other embodiments of the present invention, described computing module 106 also can utilize the solving model of conjugate gradient algorithm $\underset{\mathrm{\ρ},{\hat{s}}_1,\·\·\·,{\hat{s}}_n}{\arg \min }{\left|\right|\left(\begin{array}{c}{d}_1\\ d_2\\ .\\ .\\ .\\ d_n\end{array}\right)-\left(\begin{array}{c}\mathrm{PF\ρ}.M{\hat{s}}_1\\ \mathrm{PF\ρ}.M{\hat{s}}_2\\ .\\ .\\ .\\ \mathrm{PF\ρ}.M{\hat{s}}_n\end{array}\right)\left|\right|}_2+{\mathrm{\λ}}_1{\mathrm{\Σ}}_{i=1}^n{|\mathrm{W\ρ}.M{\hat{s}}_i|}_1+$ ${\mathrm{\λ}}_2{\mathrm{\Σ}}_{i=1}^n\mathrm{TV}(\mathrm{\ρ}.M{\hat{s}}_i)$ Come iterative.

Rebuilding module 108 is used for according to described reconstructed image matrix ρ and described sensitivity matrix si reconstructed image.

See also Fig. 2, Figure 2 shows that the process flow diagram of the method for utilizing parallel MR imaging device 10 imagings shown in Figure 1 in an embodiment of the present invention.

At step S200, acquisition module 102 owes to adopt matrix to each imaging band utilization mode of owing to adopt according to owing to adopt factor collection.In the present embodiment, the size that each passage gathers out owes to adopt matrix di is the same, but out owe to adopt that just full benefit that does not gather among the matrix di be zero.

In the present embodiment, the described mode of adopting of owing refers to center section in the described imaging band is all gathered, and peripheral part interlacing collection or both sides are owed sampling or radially sparse sampling or spiral sparse sampling at random.

At step S210, initialization module 104 obtains the initialization sensitivity matrix s of initialisation image matrix ρ and described imaging band _i

In the present embodiment, described initialization sensitivity matrix s _i, described initialisation image matrix ρ number identical with described port number.

In the present embodiment, described initialisation image matrix ρ is 1 matrix entirely for value.

In the present embodiment, initialization sensitivity matrix s _iBe 0 matrix entirely for being worth.

At step S220, computing module 106 owes to adopt matrix d according to described _i, initialisation image matrix ρ and initialization sensitivity matrix s _iUtilize conjugate gradient algorithm that constraint function is optimized iterative, obtain reconstructed image matrix ρ and sensitivity matrix s _i

In the present embodiment, described computing module 106 utilizes the solving model of conjugate gradient algorithm $\underset{\mathrm{\ρ},{\hat{s}}_1,\·\·\·,{\hat{s}}_n}{\arg \min }{\left|\right|\left(\begin{array}{c}{d}_1\\ d_2\\ .\\ .\\ .\\ d_n\end{array}\right)-\left(\begin{array}{c}\mathrm{PF\ρ}.M{\hat{s}}_1\\ \mathrm{PF\ρ}.M{\hat{s}}_2\\ .\\ .\\ .\\ \mathrm{PF\ρ}.M{\hat{s}}_n\end{array}\right)\left|\right|}_2+\mathrm{\λ}{\mathrm{\Σ}}_{i=1}^n{|\mathrm{W\ρ}.M{\hat{s}}_i|}_1$ Iterative, wherein, F is that Fourier changes, and P owes to adopt the factor, and W is sparse variation operator, and described M figures son as smoothing operator with Suo Bailiefu, for: $M=(1/{(1+|\left|\stackrel{\→}{k}\right|\left|\right)}^{1/2}).$

In other embodiments of the present invention, described computing module 106 also can utilize the solving model of conjugate gradient algorithm $\underset{\mathrm{\ρ},{\hat{s}}_1,\·\·\·,{\hat{s}}_n}{\arg \min }{\left|\right|\left(\begin{array}{c}{d}_1\\ d_2\\ .\\ .\\ .\\ d_n\end{array}\right)-\left(\begin{array}{c}\mathrm{PF\ρ}.M{\hat{s}}_1\\ \mathrm{PF\ρ}.M{\hat{s}}_2\\ .\\ .\\ .\\ \mathrm{PF\ρ}.M{\hat{s}}_n\end{array}\right)\left|\right|}_2+{\mathrm{\λ}}_1{\mathrm{\Σ}}_{i=1}^n{|\mathrm{W\ρ}.M{\hat{s}}_i|}_1+$ ${\mathrm{\λ}}_2{\mathrm{\Σ}}_{i=1}^n\mathrm{TV}(\mathrm{\ρ}.M{\hat{s}}_i)$ Come iterative.

At step S230, rebuild module 108 according to described reconstructed image matrix ρ and sensitivity matrix reconstructed image s _i

See also Fig. 3, Fig. 4 A and Fig. 4 B, wherein, Figure 3 shows that original image, Fig. 4 A is depicted as the image that utilizes after sparseSENSE rebuilds Fig. 3, the image after the method shown in Fig. 4 B in the invention is rebuild Fig. 3.

In the present embodiment, original image shown in Figure 3 is the Shepp-Logan image of 256 * 256 sizes of Noise.

In Fig. 4 A, adopting successively and owing to adopt factor R is 3,5,7,9, utilizes sparseSENSE original image to be rebuild the reconstructed image that obtains.

In Fig. 4 B, adopting successively and owing to adopt factor R is 3,5,7,9, utilizes the method among the present invention original image to be rebuild the reconstructed image that obtains.

Following table is depicted as the AP value deck watch of reconstructed image:

Owe to adopt factor R	3	5	7	9
					The inventive method	0.045796	0.050110	0.057300	0.061012
sparseSENSE	0.100678	0.114854	0.121454	0.131453

Therefore can find out and utilize image that the sparseSENSE method rebuilds out with respect to the method for utilizing among the present invention, produced larger pseudo-shadow, and the method for parallel MR imaging device 10 imagings provided by the present invention has been carried out smoothing denoising to image, effect is very obvious, comparison by the image A P value in the table, also can find out, the method for parallel MR imaging device 10 imagings that invention provides can reconstruct high signal-to-noise ratio image in the situation fast.

Parallel MR imaging device 10 in the embodiment of the present invention and formation method thereof are owed the mode of adopting collection by utilization and are owed to adopt matrix, and obtain initialisation image matrix ρ and initialization sensitivity matrix s _iAfter utilize conjugate gradient algorithm that constraint function is optimized iterative, to realize the reconstruction to image, effectively raise image taking speed, and the snr loss is little.

Although the present invention is described with reference to current better embodiment; but those skilled in the art will be understood that; above-mentioned better embodiment only is used for illustrating the present invention; be not to limit protection scope of the present invention; any within the spirit and principles in the present invention scope; any modification of doing, equivalence replacement, improvement etc. all should be included within the scope of the present invention.

Claims (12)

1. a parallel MR imaging device comprises a plurality of imaging bands, it is characterized in that, also comprises:

Acquisition module is used for each imaging band utilization mode of owing to adopt is owed to adopt matrix d according to owing to adopt factor collection _i(i＞0 is the numbering of imaging band);

Initialization module is for the initialization sensitivity matrix s that obtains initialisation image matrix ρ and described imaging band _i(i＞0 is the numbering of imaging band);

Computing module is used for owing to adopt matrix d according to described _i, initialisation image matrix ρ and initialization sensitivity matrix s _iUtilize conjugate gradient algorithm that constraint function is optimized iterative, obtain reconstructed image matrix ρ and sensitivity matrix s _i

Rebuild module, be used for according to described reconstructed image matrix ρ and described sensitivity matrix si reconstructed image.

2. parallel MR imaging device as claimed in claim 1, it is characterized in that, the described mode of adopting of owing refers to center section in the described imaging band is all gathered, and peripheral part interlacing collection or both sides are owed sampling or radially sparse sampling or spiral sparse sampling at random.

3. parallel MR imaging device as claimed in claim 1 is characterized in that, described initialization sensitivity matrix s _i, described initialisation image matrix ρ number identical with described port number.

4. parallel MR imaging device as claimed in claim 1 is characterized in that, described initialisation image matrix ρ is 1 matrix entirely for value.

5. parallel MR imaging device as claimed in claim 1 is characterized in that, initialization sensitivity matrix s _iBe 0 matrix entirely for being worth.

6. parallel MR imaging device as claimed in claim 1 is characterized in that, described computing module utilizes the solving model of conjugate gradient algorithm $\underset{\mathrm{\ρ},{\hat{s}}_1,\·\·\·,{\hat{s}}_n}{\arg \min }{\left|\right|\left(\begin{array}{c}{d}_1\\ d_2\\ .\\ .\\ .\\ d_n\end{array}\right)-\left(\begin{array}{c}\mathrm{PF\ρ}.M{\hat{s}}_1\\ \mathrm{PF\ρ}.M{\hat{s}}_2\\ .\\ .\\ .\\ \mathrm{PF\ρ}.M{\hat{s}}_n\end{array}\right)\left|\right|}_2+\mathrm{\λ}{\mathrm{\Σ}}_{i=1}^n{|\mathrm{W\ρ}.M{\hat{s}}_i|}_1$ Iterative, wherein, described M figures son as smoothing operator with Suo Bailiefu, for:

7. the formation method of a parallel MR imaging device, described parallel MR imaging device comprises a plurality of imaging bands, comprising:

Each imaging band utilization mode of owing to adopt is owed to adopt matrix d according to owing to adopt factor collection _i

Obtain the initialization sensitivity matrix s of initialisation image matrix ρ and described imaging band _i

Owe to adopt matrix d according to described _i, described initialisation image matrix ρ and described initialization sensitivity matrix s _iUtilize conjugate gradient algorithm that constraint function is optimized iterative, obtain reconstructed image matrix ρ and sensitivity matrix s _i

According to described reconstructed image matrix ρ and sensitivity matrix reconstructed image s _i

8. method as claimed in claim 7 is characterized in that, the described mode of adopting of owing refers to center section in the described imaging band is all gathered, and peripheral part interlacing collection or both sides are owed sampling or radially sparse sampling or spiral sparse sampling at random.

9. method as claimed in claim 7 is characterized in that, described initialization sensitivity matrix s _i, described initialisation image matrix ρ number identical with described port number.

10. method as claimed in claim 7 is characterized in that, described initialisation image matrix ρ is 1 matrix entirely for value.

11. method as claimed in claim 7 is characterized in that, initialization sensitivity matrix s _iBe 0 matrix entirely for being worth.

12. method as claimed in claim 7 is characterized in that, described computing module utilizes the solving model of conjugate gradient algorithm $\underset{\mathrm{\ρ},{\hat{s}}_1,\·\·\·,{\hat{s}}_n}{\arg \min }{\left|\right|\left(\begin{array}{c}{d}_1\\ d_2\\ .\\ .\\ .\\ d_n\end{array}\right)-\left(\begin{array}{c}\mathrm{PF\ρ}.M{\hat{s}}_1\\ \mathrm{PF\ρ}.M{\hat{s}}_2\\ .\\ .\\ .\\ \mathrm{PF\ρ}.M{\hat{s}}_n\end{array}\right)\left|\right|}_2+\mathrm{\λ}{\mathrm{\Σ}}_{i=1}^n{|\mathrm{W\ρ}.M{\hat{s}}_i|}_1$ Iterative, wherein, described M figures son as smoothing operator with Suo Bailiefu, for: $M=(1/{(1+|\left|\stackrel{\→}{k}\right|\left|\right)}^{1/2}).$

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