CN115236576A - Rapid multi-contrast magnetic resonance diffusion imaging and reconstruction method and system - Google Patents
Rapid multi-contrast magnetic resonance diffusion imaging and reconstruction method and system Download PDFInfo
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Abstract
The invention provides a rapid multi-contrast magnetic resonance diffusion imaging and reconstruction method and a system, comprising the following steps: using a Keyhole plane echo sequence to undersampling k-space data of a plurality of diffusion weighted images through a plurality of receiving coils; constructing an objective function comprising a multi-coil image fidelity term and an image regularization term based on coil-space-contrast domain tensor Low rank (Low-RankTensorConstraint); and minimizing the objective function through an optimization algorithm, reconstructing diffusion images of all receiving coils and acquiring related quantitative parameter images according to a specific mode of contrast change. According to the invention, the undersampling of k space is realized through a keyhole-EPI sequence, the resolution of multi-contrast diffusion imaging is improved, and the echo time is shortened, so that the deformation artifact of an image is improved, and the imaging time is reduced; the method avoids the step of solving the coil sensitivity map, reduces the image artifacts caused by variable density EPI imaging, and improves the robustness and accuracy of the reconstruction result.
Description
Technical Field
The invention relates to the field of magnetic resonance diffusion imaging, in particular to a rapid multi-contrast magnetic resonance diffusion imaging and reconstruction method and system.
Background
The change of the diffusion capacity of water molecules in human tissues can reflect the composition of physiological tissues and pathological changes. Magnetic resonance Diffusion imaging (Diffusion MRI) is an imaging technique that enables non-invasive examination of the ability of living tissue to diffuse. Is widely applied to the quantitative research of micro-vascular perfusion and diffusion effect living bodies, and has important functions in the early diagnosis of diseases such as acute stroke, liver diseases, cancers and the like and the research of neuroscience. The computation of quantitative images of the Diffusion effect relies on a plurality of magnetic resonance Diffusion Weighted Images (DWI) with different b-values, directions, diffusion times, etc. parameters. Magnetic resonance diffusion imaging requires a long scan time due to the need to obtain multiple images of different contrast. Meanwhile, due to the addition of the diffusion gradient, the signal attenuation speed is high, the acquired resolution is limited, and the distortion of the image is large.
There are two main types of existing mri methods.
The first type is a single shot echo planar sequence. The method finishes data acquisition of the whole K space after one-time excitation, has short scanning time and is robust to motion. But because of the longer echo chain, the resolution is low, the signal-to-noise ratio is low, and the image distortion is large.
The second type is a multi-shot echo-planar sequence. The method adopts a multi-excitation strategy, a part of data of the K space is acquired by excitation each time, the data acquired by the multi-excitation is combined into the complete K space, and higher spatial resolution and image quality can be realized. However, the diffusion coding gradient causes a phase change between different excitations and, in addition, there may be motion between excitations; combining these data directly would lead to image artifacts. In addition, for diffusion quantitative imaging applications such as ADC, DTI, DKI, IVIM, etc., multiple images of multiple shots need to be acquired to achieve high resolution, and the scan time becomes long.
Patent document (application No. 201210388038.1) discloses a fast diffusion magnetic resonance imaging and reconstruction method, comprising the steps of: (S1) carrying out signal acquisition on a measured target in N diffusion weighting directions through a multi-channel coil; (S2) combining the obtained complementary K-space data to obtain fully sampled K-space data K C (ii) a (S3) based on the image of the different diffusion weighting direction corresponding to the K space data and the fully sampled K space data K C Correspond toIs subjected to a preliminary reconstruction to obtain a preliminary image and (S4) is subjected to a regularized reconstruction based on said preliminary image and iterated until convergence to obtain the desired final image (I) 1 ，…，I N ). According to the method, a common echo planar imaging sequence instead of a keyhole echo planar imaging sequence is used for data acquisition, the acquired diffusion images need to be combined in a K space, and meanwhile, coil sensitivity parameters need to be solved, so that reconstruction errors caused by combination are difficult to avoid, and inaccuracy caused by inaccurate estimation of the coil sensitivity parameters to reconstruction cannot be avoided.
Patent document CN103675737a (application number: CN 201310659202.2) discloses a diffusion magnetic resonance imaging and reconstruction method. The method comprises the following steps: s1, using a plurality of channel coils, and acquiring signals of a target to be detected in a multi-excitation diffusion imaging mode to acquire k-space data; s2, calculating a coil sensitivity map, and performing iterative initialization; and S3, performing iterative reconstruction on the required diffusion image based on a POCS algorithm according to the acquired k-space data, the calculated coil sensitivity map and the initialization parameters. The invention adopts a multi-excitation strategy, needs to firstly merge K space of the collected multi-coil diffusion images, and can not reduce imaging time in multi-contrast diffusion imaging by changing contrast.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a rapid multi-contrast magnetic resonance diffusion imaging and reconstruction method and system, which can effectively improve the resolution, reduce the deformation artifact and improve the scanning efficiency.
The invention provides a rapid multi-contrast magnetic resonance diffusion imaging and reconstruction method, which comprises the following steps:
step S1: undersampling k-space data of a plurality of variable contrast diffusion weighted images using a keyhole planar echo sequence through a plurality of receive coils;
step S2: according to the acquisition result, constructing a target function comprising a multi-coil image fidelity term and an image regular term based on coil, space and contrast domain tensor low-rank property;
and step S3: and minimizing the objective function through an optimization algorithm, reconstructing diffusion images of all receiving coils and acquiring related quantitative parameter images according to a specific mode of contrast change.
Preferably, in the step S1:
the number of the receiving coils is more than or equal to 1;
the variable contrast includes: the contrast parameters between different TRs of the plane echo sequence can be arbitrarily varied, including but not limited to, arbitrary variations in b-value, diffusion encoding direction, and diffusion time;
for a specific one of the contrast parameters, the arbitrary variation is that the contrast parameters between different TRs remain unchanged or are different;
the keyhole-EPI sequence was acquired using variable density k-space:
at undersampling rate R in the central k-space region C Carrying out intensive collection;
at undersampling rate R in the surrounding k-space region P Uniformly shifting and undersampling so that adjacent TRs acquire cyclically shifted phase encoding lines;
the variable density k-space acquisition of the keyhole-EPI sequence is performed by:
determining a gradient momentum across a single phase encoding row; calculating other required blip gradient momentum spanning different phase coding rows according to the calculated result; carrying out time sequence arrangement on the required blip gradient corresponding to the momentum according to a preset sampling track; and acquiring k-space data by adopting a bipolar EPI gradient according to the calculation result.
Preferably, in the step S2:
the image to be solved is a diffusion weighted image with variable contrast under multiple coils, but not a diffusion weighted image after the multiple coils are combined;
the objective function does not comprise coil sensitivity, and the coil sensitivity does not need to be solved first or the self-correction of parallel imaging is not needed to be solved;
image fidelity terms based on coil, space and contrast domain tensor low rank make constraints on the variation range of an image to be solved by utilizing tensor low rank of a multi-coil variable contrast diffusion weighted image, wherein the image fidelity terms comprise the constraints based on tensor low rank in a global space and the constraints based on local space tensor low rank;
the objective function is:
y represents the preprocessed multidimensional K space data, D represents an undersampling operator, F represents a Fourier transform operator, x represents a multi-coil multi-contrast image to be reconstructed, alpha is a regularization coefficient, and B represents i x is a coil, space and contrast three-dimensional tensor taking the ith pixel as a center, R is a rank-solving operator of the three-dimensional tensor based on Tucker decomposition or PARAFAC decomposition, and N is v For a predetermined value, e.g. using a global low rank constraint, then N v =1, if local low rank constraint is used, then N v May be equal to the number of pixels.
Preferably, in the step S3:
the objective function can be solved through any optimization algorithm, including a gradient descent method, a conjugate gradient method, a quasi-Newton method, an alternative multiplier method, a near-end gradient method, a projection gradient method and a coordinate descent method, so as to reconstruct diffusion images of all receiving coils and obtain related quantitative parameter images according to specific application scenes.
Preferably, the imaging method can be used for diffusion imaging application scenes and comprises the following steps: diffusion weighted imaging under multiple excitations, apparent diffusion coefficient quantitative imaging, diffusion tensor imaging, diffusion kurtosis imaging and in-voxel incoherent motion imaging.
According to the invention, a fast multi-contrast magnetic resonance diffusion imaging and reconstruction system is provided, which comprises:
a module M1: undersampling k-space data of a plurality of variable contrast diffusion weighted images by using a keyhole plane echo sequence through a plurality of receiving coils;
a module M2: according to the acquisition result, constructing a target function comprising a multi-coil image fidelity term and an image regular term based on coil, space and contrast domain tensor low-rank property;
a module M3: and minimizing the objective function through an optimization algorithm, reconstructing diffusion images of all receiving coils and acquiring related quantitative parameter images according to a specific mode of contrast change.
Preferably, in said module M1:
the number of the receiving coils is more than or equal to 1;
the variable contrast includes: the contrast parameters between different TRs of the plane echo sequence can be arbitrarily varied, including but not limited to, arbitrary variations in b-value, diffusion encoding direction, and diffusion time;
for a specific contrast parameter, the arbitrary variation is that the contrast parameters between different TRs remain unchanged or are different;
the keyhole-EPI sequence was acquired using variable density k-space:
at undersampling rate R in the central k-space region C Carrying out intensive collection;
at undersampling rate R in the surrounding k-space region P Uniformly shifting and undersampling so that adjacent TRs acquire the phase coding rows which are circularly shifted;
the variable density k-space acquisition of the keyhole-EPI sequence is performed by:
determining a gradient momentum across a single phase encoding row; calculating other required blip gradient momentum spanning different phase coding rows according to the calculated result; carrying out time sequence arrangement on the required blip gradient corresponding to the momentum according to a preset sampling track; and acquiring k-space data by adopting a bipolar EPI gradient according to the calculation result.
Preferably, in said module M2:
the image to be solved is a variable contrast diffusion weighted image under multiple coils, but not a diffusion weighted image after multiple coils are combined;
the objective function does not comprise coil sensitivity, and the objective function is solved without solving the coil sensitivity or carrying out self-correction of parallel imaging;
image fidelity terms based on coil, space and contrast domain tensor low rank make constraints on the variation range of an image to be solved by utilizing tensor low rank of a multi-coil variable contrast diffusion weighted image, wherein the constraints comprise constraints based on tensor low rank in a global space and constraints based on local space tensor low rank.
The objective function is:
y represents the preprocessed multidimensional K space data, D represents an undersampling operator, F represents a Fourier transform operator, x represents a multi-coil multi-contrast image to be reconstructed, alpha is a regularization coefficient, and B represents i x is a coil, space and contrast three-dimensional tensor taking the ith pixel as a center, R is a rank-solving operator of the three-dimensional tensor based on Tucker decomposition or PARAFAC decomposition, and N is v For a predetermined value, N if a global low rank constraint is used v =1, if a local low rank constraint is used, then N v May be equal to the number of pixels.
Preferably, in said module M3:
the objective function can be solved through any optimization algorithm, including a gradient descent method, a conjugate gradient method, a quasi-Newton method, an alternative multiplier method, a near-end gradient method, a projection gradient method and a coordinate descent method, so as to reconstruct diffusion images of all receiving coils and obtain related quantitative parameter images according to specific application scenes.
Preferably, the imaging method can be used for diffusion imaging application scenes and comprises the following steps: diffusion weighted imaging under multiple excitation, apparent diffusion coefficient quantitative imaging, diffusion tensor imaging, diffusion kurtosis imaging and incoherent motion imaging in a voxel.
Compared with the prior art, the invention has the following beneficial effects:
1. compared with the single-shot plane echo imaging technology, the method has the advantages that the acquisition time of an EPI echo chain is reduced by a Keyole plane echo sequence imaging method, and higher resolution, higher signal-to-noise ratio and smaller image distortion can be realized;
2. compared with the multiple excitation plane echo imaging technology, the method has the advantages that the contrast change is introduced in the multiple excitation process, multiple diffusion weighted images with different contrasts and corresponding quantitative images are obtained in a shorter imaging time, the step of solving a coil sensitivity map is avoided by using the low-rank tensor constraint of a coil-space-contrast domain, the image artifact caused by variable-density EPI imaging is reduced, and the robustness and the accuracy of a reconstruction result are improved.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of a diffusion magnetic resonance imaging and reconstruction method of an embodiment of the present application;
FIG. 2 is a schematic diagram of a keyhole planar echo sequence according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a Keyhole planar echo sequence trajectory in accordance with an embodiment of the present application;
FIG. 4 is a flow chart of an iterative reconstruction method in a diffusion magnetic resonance imaging and reconstruction method in accordance with an embodiment of the present application;
FIG. 5 is a graphical representation comparing the results of a retrospective experimental reconstruction of an embodiment of the present application with those of the prior art;
fig. 6 is a diagram comparing the reconstruction results of a prospective experiment of the present application with those of the prior art.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example 1:
according to the invention, a fast multi-contrast magnetic resonance diffusion imaging and reconstruction method is provided, as shown in fig. 1-6, comprising:
step S1: undersampling k-space data of a plurality of variable contrast diffusion weighted images by using a keyhole plane echo sequence through a plurality of receiving coils;
specifically, in the step S1:
the number of the receiving coils is more than or equal to 1;
the variable contrast includes: the contrast parameters between different TRs of a planar echo sequence can be varied arbitrarily, including but not limited to, arbitrary variations in b-values, diffusion encoding directions, and diffusion times;
for a specific one of the contrast parameters, the arbitrary variation is that the contrast parameters between different TRs remain unchanged or are different;
the keyhole-EPI sequence was acquired using variable density k-space:
at undersampling rate R in the central k-space region C Carrying out intensive collection;
at undersampling rate R in the surrounding k-space region P Uniformly shifting and undersampling so that adjacent TRs acquire cyclically shifted phase encoding lines;
the variable density k-space acquisition of the keyhole-EPI sequence is performed by:
determining a gradient momentum across a single phase encoding row; calculating other required blip gradient momentum spanning different phase coding rows according to the calculated result; carrying out time sequence arrangement on the required blip gradient corresponding to the momentum according to a preset sampling track; and acquiring k-space data by adopting a bipolar EPI gradient according to the calculation result.
Step S2: according to the acquisition result, constructing a target function comprising a multi-coil image fidelity term and an image regular term based on coil, space and contrast domain tensor low-rank property;
specifically, in the step S2:
the image to be solved is a variable contrast diffusion weighted image under multiple coils, but not a diffusion weighted image after multiple coils are combined;
the objective function does not comprise coil sensitivity, and the objective function is solved without solving the coil sensitivity or carrying out self-correction of parallel imaging;
the image fidelity terms based on the coil, space and contrast domain tensor low rank property make constraints on the variation range of an image to be solved by utilizing the tensor low rank property of the multi-coil variable contrast diffusion weighted image, wherein the constraints comprise the constraints based on the tensor low rank property in the global space and the constraints based on the local space tensor low rank property;
the objective function is:
y represents the preprocessed multidimensional K space data, D represents an undersampling operator, F represents a Fourier transform operator, x represents a multi-coil multi-contrast image to be reconstructed, alpha is a regularization coefficient, and B represents i x is a coil, space and contrast three-dimensional tensor taking the ith pixel as a center, R is a rank-solving operator of the three-dimensional tensor based on Tucker decomposition or PARAFAC decomposition, and N is v For a predetermined value, N if a global low rank constraint is used v =1, if a local low rank constraint is used, then N v May be equal to the number of pixels.
And step S3: and minimizing the objective function through an optimization algorithm, reconstructing diffusion images of all receiving coils and acquiring related quantitative parameter images according to a specific mode of contrast change.
Specifically, in the step S3:
the objective function can be solved through any optimization algorithm, including a gradient descent method, a conjugate gradient method, a quasi-Newton method, an alternative multiplier method, a near-end gradient method, a projection gradient method and a coordinate descent method, so as to reconstruct diffusion images of all receiving coils and obtain related quantitative parameter images according to specific application scenes.
In particular, the imaging method can be used in a diffusion imaging application scenario including: diffusion weighted imaging under multiple excitation, apparent diffusion coefficient quantitative imaging, diffusion tensor imaging, diffusion kurtosis imaging and incoherent motion imaging in a voxel.
According to the invention, the rapid multi-contrast magnetic resonance diffusion imaging and reconstruction system comprises:
a module M1: undersampling k-space data of a plurality of variable contrast diffusion weighted images by using a keyhole plane echo sequence through a plurality of receiving coils;
specifically, in the module M1:
the number of the receiving coils is more than or equal to 1;
the variable contrast includes: the contrast parameters between different TRs of the plane echo sequence can be arbitrarily varied, including but not limited to, arbitrary variations in b-value, diffusion encoding direction, and diffusion time;
for a specific one of the contrast parameters, the arbitrary variation is that the contrast parameters between different TRs remain unchanged or are different;
the keyhole-EPI sequence was acquired using variable density k-space:
at undersampling rate R in the central k-space region C Carrying out intensive collection;
at undersampling rate R in the surrounding k-space region P Uniformly shifting and undersampling so that adjacent TRs acquire cyclically shifted phase encoding lines;
the variable density k-space acquisition of the keyhole-EPI sequence is performed by:
determining a gradient momentum across a single phase encoding row; calculating other required blip gradient momentum spanning different phase coding rows according to the calculated result; carrying out time sequence arrangement on the required blip gradient corresponding to the momentum according to a preset sampling track; and acquiring k-space data by adopting a bipolar EPI gradient according to the calculation result.
A module M2: according to the acquisition result, constructing a target function comprising a multi-coil image fidelity term and an image regular term based on coil, space and contrast domain tensor low-rank property;
in particular, in said module M2:
the image to be solved is a variable contrast diffusion weighted image under multiple coils, but not a diffusion weighted image after multiple coils are combined;
the objective function does not comprise coil sensitivity, and the objective function is solved without solving the coil sensitivity or carrying out self-correction of parallel imaging;
image fidelity terms based on coil, space and contrast domain tensor low rank make constraints on the variation range of an image to be solved by utilizing tensor low rank of a multi-coil variable contrast diffusion weighted image, wherein the image fidelity terms comprise the constraints based on tensor low rank in a global space and the constraints based on local space tensor low rank;
the objective function is:
y represents the preprocessed multidimensional K space data, D represents an undersampling operator, F represents a Fourier transform operator, x represents a multi-coil multi-contrast image to be reconstructed, alpha is a regularization coefficient, and B represents i x is a coil, space and contrast three-dimensional tensor taking the ith pixel as a center, R is a rank-solving operator of the three-dimensional tensor based on Tucker decomposition or PARAFAC decomposition, and N is v For a predetermined value, e.g. using a global low rank constraint, then N v =1, if local low rank constraint is used, then N v May be equal to the number of pixels.
A module M3: and minimizing the objective function through an optimization algorithm, reconstructing diffusion images of all receiving coils and acquiring related quantitative parameter images according to a specific mode of contrast change.
In particular, in said module M3:
the objective function can be solved through any optimization algorithm, including a gradient descent method, a conjugate gradient method, a quasi-Newton method, an alternative multiplier method, a near-end gradient method, a projection gradient method and a coordinate descent method, so as to reconstruct diffusion images of all receiving coils and obtain related quantitative parameter images according to specific application scenes.
In particular, the imaging method can be used in a diffusion imaging application scenario including: diffusion weighted imaging under multiple excitation, apparent diffusion coefficient quantitative imaging, diffusion tensor imaging, diffusion kurtosis imaging and incoherent motion imaging in a voxel.
Example 2:
example 2 is a preferred example of example 1, and the present invention will be described in more detail.
The invention provides a rapid multi-contrast magnetic resonance diffusion imaging and reconstruction method, which comprises the following steps:
step S1: undersampling k-space data of a plurality of variable contrast diffusion weighted images using a Keyole plane echo (Keyole-EPI) sequence by a plurality of receive coils;
step S2: constructing a target function comprising a multi-coil image fidelity term and an image regular term based on coil-space-contrast domain tensor low-rank property;
and step S3: and minimizing the objective function through an optimization algorithm, reconstructing diffusion images of all receiving coils and acquiring related quantitative parameter images according to a specific mode of contrast change.
Preferably, in the step S1:
the number of the receiving coils is more than or equal to 1;
the variable contrast includes: the contrast parameters between different TRs of the plane echo sequence can be arbitrarily varied, including but not limited to, arbitrary variations in b-value, diffusion encoding direction, and diffusion time;
the arbitrary variation is such that the contrast parameters between different TRs remain the same or differ for a particular one of the contrast parameters.
Preferably, the keyhole-EPI sequence is acquired using variable density k-space:
at undersampling rate R in the central k-space region C Carrying out intensive collection;
at undersampling rate R in the surrounding k-space region P Uniformly shifting and undersampling so that adjacent TRs acquire cyclically shifted phase encoding lines;
the variable density k-space acquisition of the keyhole-EPI sequence is performed by:
determining a gradient momentum across a single phase encoding row; calculating other required blip gradient momentum spanning different phase coding rows according to the calculated result; carrying out time sequence arrangement on the required blip gradient corresponding to the momentum according to a preset sampling track; from the above calculations, bipolarEPI gradients are used to acquire k-space data.
Preferably, in the step S2:
the image to be solved is a variable contrast diffusion weighted image under multiple coils, but not a diffusion weighted image after multiple coils are combined.
The coil sensitivity is not included in the objective function, and the objective function is solved without solving the coil sensitivity first or performing self-correction of parallel imaging.
Image fidelity terms based on coil-space-contrast domain tensor low rank make constraints on the variation range of an image to be solved by utilizing tensor low rank of a multi-coil variable contrast diffusion weighted image, wherein the constraints comprise constraints based on tensor low rank in a global space and constraints based on local space tensor low rank.
Specifically, according to an embodiment of the present invention, when the local tensor low rank is used as the low rank constraint, the constructed objective function can be described as:
y represents the preprocessed multidimensional K space data, D represents an undersampling operator, F represents a Fourier transform operator, x represents a multi-coil multi-contrast image to be reconstructed, alpha is a regularization coefficient, and B represents i x is a coil-space-contrast three-dimensional tensor taking the ith pixel as a center, R is a rank-solving operator of the three-dimensional tensor based on Tucker decomposition or PARAFAC decomposition, N v For a predetermined value, e.g. N using a global low rank constraint v =1, e.g. using local low rank constraint then N v May be equal to the number of pixels.
According to the rapid multi-contrast magnetic resonance diffusion imaging and reconstruction method, the coil sensitive image parameters can be prevented from being solved, the direct combination of K spaces with different phases is avoided, the reconstructed image quality is improved, and higher resolution and higher acquisition efficiency are allowed.
According to one embodiment of the invention, the objective function may be rewritten as the following formula by constructing the auxiliary variables:
s.t B i x＝M i
M i ＝g i × l A i × 2 B i × 3 C i ，j＝1，2，…,N v
and y represents the multi-dimensional K space data obtained after preprocessing, D represents an undersampling operator, F represents a Fourier transform operator, and x represents a multi-coil multi-contrast image to be reconstructed. α is a regularization coefficient, B i x is a coil-space-contrast three-dimensional tensor taking the ith pixel as a center, R is a rank-solving operator of the three-dimensional tensor based on the Tucker decomposition or PARAFAC decomposition, N v For a predetermined value, e.g. N using a global low rank constraint v =1, e.g. using local low rank constraint then N v May be equal to the number of pixels. M i Is an auxiliary variable of Tensor type, obtained by PARAFAC or Tucker decomposition, M i ＝g i × 1 A i × 2 B i × 3 C i ，i＝1，2，…，N v
According to one embodiment of the present invention, the objective function may be rewritten to an augmented Lagrangian form, as follows:
wherein, y represents the multidimensional K space data obtained after preprocessing, D represents the undersampling operator, and F tableShowing a fourier transform operator and x representing a multi-coil multi-contrast image to be reconstructed. α is a regularization coefficient, B i x is a coil-space-contrast three-dimensional tensor taking the ith pixel as a center, R is a rank-solving operator of the three-dimensional tensor based on Tucker decomposition or PARAFAC decomposition, N v For a predetermined value, e.g. using a global low rank constraint, then N v =1, e.g. using local low rank constraint then N v May be equal to the number of pixels. Re is a real number taking operator. M is a group of i Is an auxiliary variable of Tensor type, obtained by PARAFAC or Tucker decomposition, M i ＝g i × 1 A i × 2 B i × 3 C i ，i＝1，2，…，N v Mu is the augmented Lagrange coefficient, lambda i Is a lagrange multiplier.
Preferably, in the step S3:
the objective function can be solved by any optimization algorithm, including Gradient Descent (Gradient Description), conjugate Gradient (conjugate gradients), quasi Newton (quasi-Newton), alternative multiplier (alternative multipliers), near-end Gradient (near Gradient), projection Gradient (projected Gradient), coordinate Descent (coordinated Descent) and other algorithms, so as to reconstruct the diffusion image of all receiving coils and obtain the related quantitative parameter image according to the specific application scenario.
The Imaging method can be used for various Diffusion Imaging related application scenes such as Diffusion weighted Imaging under multiple excitation, application Diffusion Coefficient (ADC) quantitative Imaging, diffusion Tensor Imaging (Diffusion Tensor Imaging), diffusion Kurtosis Imaging (Diffusion Kurtosis Imaging), in-voxel incoherent motion Imaging (IVIM) and the like.
The obtaining of the relevant quantitative parametric image representation according to the application scenario: when quantitative parameter images need to be obtained, the parameter images and the diffusion images are final results; and when the quantitative parameter image does not need to be acquired, the reconstructed diffusion image is the final result.
Specifically, in one embodiment of the present invention, the objective function is iteratively minimized by an alternative multiplier method, which is calculated as follows:
s31 initialization: before formal iterative reconstruction, a diffusion image to be solved needs to be initialized, namely an initial value of the image is determined 0 。
S32, solving the subproblems respectively: in the ADMM, the sub-problems only including the diffusion image variable, the auxiliary variable and the lagrange multiplier variable to be solved need to be solved respectively, and the steps S321 to S323 describe the details of solving the above-mentioned corresponding sub-problems respectively.
Step S321: solving for x k Existence of analytic solution
Where H denotes the conjugate transpose.
Step S322 of solving
The sub-targets are as follows
Preferably, the PARAFAC or Tucker decomposition of the tensor is first performed
The singular value soft threshold method is then used:
step S323 of solving
S33, after completing the above steps, automatically updating the coil image of each channel, and performing loop iteration, and stopping iteration when one of the following conditions is reached: (1) the iteration times are more than 50; (2) Infinite norm of gradient less than 1 x 10 -10 (ii) a (3) Step size less than 1 x 10 -12 . And judging whether convergence is achieved or the maximum iteration number is reached. If the iteration stop condition is not satisfied, the process re-enters the step S32 to continue the iteration. And if the iteration stop condition is met, terminating the iteration to obtain the magnetic resonance diffusion weighted image.
And S34, solving diffusion quantitative imaging such as ADC, DTI, DKI, IVIM and the like according to the acquired diffusion weighting parameter types and actual requirements, wherein if the quantitative image does not need to be solved, the magnetic resonance diffusion weighting image is the final result.
Example 3:
example 3 is a preferred example of example 1, and the present invention will be described in more detail.
The application aims to provide a fast multi-contrast magnetic resonance diffusion imaging and reconstruction method.
The general steps of the fast multi-contrast magnetic resonance diffusion imaging and reconstruction method provided by the present application are shown in fig. 1, and include: step S1: undersampling k-space data of a plurality of variable contrast diffusion weighted images using a Keyole plane echo (Keyole-EPI) sequence by a plurality of receive coils; step S2: constructing an objective function comprising a multi-coil image fidelity term and an image regularization term based on the coil-space-contrast domain tensor low-rank property; and step S3: and minimizing the objective function through an optimization algorithm, reconstructing diffusion images of all receiving coils and acquiring related quantitative parameter images according to a specific mode of contrast change.
In the following description of the fast high-resolution diffusion magnetic resonance imaging and reconstruction Method according to the embodiment of the present application, a coil-space-contrast domain local Tensor Low Rank constraint (LLRT) is used in combination with a data fidelity term to form an objective function, and an Alternating Direction Multiplier Method (ADMM) is preferably used as an iterative reconstruction algorithm to reconstruct a multi-contrast diffusion weighted image and obtain an ADC image.
The above steps are described in detail with reference to 2,3,4.
(S101) acquiring k space through a keyhole plane echo sequence under any receiving coil quantity; fig. 3 is a schematic diagram of the variable contrast keyhole planar echo sequence. In FIG. 3, the momentum of the unit phase code K space is M u The momentum of the Blip gradient in the central K space is M u The momentum of the Blip gradient in the surrounding K space is R p M u ,R p The surrounding K space undersampling rate. Adjacent TRs differ by R in the phase encode direction, the momentum of the Prephase gradient c M u /2. Realizing dense acquisition in a central k-space region and undersampling R in a surrounding k-space region P And (5) uniformly shifting and collecting. The contrast parameters such as b value, diffusion coding direction, diffusion time and the like between different TRs can be changed at will. In the present embodiment, TR1 and TR2 in fig. 2 have different diffusion contrast parameters. In fig. 4, a more detailed embodiment is given. Different TRs have continuously changing b values.
And (S102) carrying out data preprocessing on the acquired K space data, and carrying out Nyquist ghost and trapezoidal sampling correction. Any known method can be used for nyquist ghost correction and trapezoidal sampling correction. In this embodiment, the processing is performed using an integrated method of the acquisition machine uMR.
(S103) constructing an objective equation by combining the coil-space-contrast domain tensor low-rank regularization term and the data fidelity term. In the embodiment, the Tensor Low Rank property (LLRT) of the image to be obtained in the coil-space-contrast domain is preferably combined with the data fidelity term to form an objective function, and an alternating direction multiplier method is selected as an iterative reconstruction algorithm to obtain the required diffusion image.
It should be particularly noted that the invention does not need to acquire coil sensitivity parameters during reconstruction;
specifically, the objective function may be described as:
wherein y represents the preprocessed multidimensional K space data, D represents an undersampling operator, F represents a Fourier transform operator, x represents a multi-coil multi-contrast image to be reconstructed, alpha is a regularization coefficient, and B i x is a coil-space-contrast three-dimensional tensor taking the ith pixel as a center, R is a rank-solving operator of the three-dimensional tensor based on Tucker decomposition or PARAFAC decomposition, N v For a predetermined value, e.g. using a global low rank constraint, then N v =1, e.g. using local low rank constraint then N v May be equal to the number of pixels. According to the rapid multi-contrast magnetic resonance diffusion imaging and reconstruction method, the coil sensitive image parameters can be prevented from being solved, the direct combination of K spaces with different phases is avoided, the reconstructed image quality is improved, and higher resolution and higher acquisition efficiency are allowed.
(S104) in the embodiment of the application, a required diffusion image is acquired based on an alternating direction multiplier method as an iterative reconstruction algorithm. Before using the above method, an auxiliary variable is constructed and an objective function is rewritten as follows.
s.t B i x＝M i
M i ＝g i × 1 A i × 2 B i × 3 C i ，i＝1，2，…，N v
Wherein y represents the multidimensional K space data obtained after preprocessing, D represents an undersampling operator, F represents a Fourier transform operator, and x represents a multi-coil multi-contrast image to be reconstructed. α is a regularization coefficient, B i x is a coil-space-contrast three-dimensional tensor taking the ith pixel as a center, R is a rank-solving operator of the three-dimensional tensor based on the Tucker decomposition or PARAFAC decomposition, N v For a predetermined value, e.g. using a global low rank constraint, then N v =1, e.g. using local low rank constraint then N v May be equal to the number of pixels. M i Is an auxiliary variable of Tensor type, obtained by PARAFAC or Tucker decomposition, M i ＝g i × 1 A i × 2 B i × 3 C i ，i＝1，2，…，N v
According to one embodiment of the present invention, the objective function may be rewritten as an augmented Lagrangian form as follows:
and y represents the multi-dimensional K space data obtained after preprocessing, D represents an undersampling operator, F represents a Fourier transform operator, and x represents a multi-coil multi-contrast image to be reconstructed. α is a regularization coefficient, B i x is a coil-space-contrast three-dimensional tensor taking the ith pixel as a center, R is a rank-solving operator of the three-dimensional tensor based on Tucker decomposition or PARAFAC decomposition, N v For a predetermined value, e.g. using a global low rank constraint, then N v =1, e.g. using local low rank constraint then N v May be equal to the number of pixels. Re is a real number taking operator. M i Is an auxiliary variable of Tensor type, obtained by PARAFAC or Tucker decomposition, M i ＝g i × 1 A i × 2 B i × 3 C i ，i＝1，2，…，N v Mu is the augmented Lagrange coefficient, lambda i Is a lagrange multiplier.
(S105) in the formalBefore iterative reconstruction, a diffusion image to be solved needs to be initialized, namely an initial value of the image is determined, in the embodiment of the invention, the initial value can be set to be 0, and for convenience of description, the initial point x is selected as x in a formula 0 。
(S106) solving the sub-problems respectively. In the ADMM, sub-problems only including a diffusion image variable to be solved, an auxiliary variable and a lagrange multiplier variable need to be solved, and the following sub-steps describe the details of solving the above-mentioned corresponding sub-problems respectively.
Solving for x k Existence of an analytic solution
Where H denotes the conjugate transpose.
Solving for
The sub-targets are as follows
Preferably, the PARAFAC or Tucker decomposition of the tensor is performed first
Then using singular value soft thresholding:
solving for
(S107) after the steps are completed, automatically updating the coil image of each channel, and performing loop iteration until one of the following conditions is reached and the iteration is stopped: (1) the iteration times are more than 50; (2) Infinite norm of gradient less than 1 x 10 -10 (ii) a (3) Step size less than 1 x 10 -12 . And judging whether convergence or the maximum iteration number is reached.
(S108) if the iteration stop condition is not met, re-entering the step S106 to continue the iteration.
(S109) solving diffusion quantitative imaging such as ADC, DTI, DKI, IVIM and the like according to the collected diffusion weighting parameter types and actual requirements, wherein if the quantitative image does not need to be solved, the magnetic resonance diffusion weighting image is the final result.
The foregoing discloses one embodiment to realize the different structures of the present application; the described embodiments are only some embodiments of the present application and not all embodiments. Various configurations of the present invention can be realized by other methods and combinations.
The following examples are carried out using the present invention and the advantages of the present invention can be more embodied in this example.
A multi-b-value magnetic resonance diffusion imaging control experiment of the head was performed on a healthy subject using a variable contrast multi-shot planar echo sequence and a variable contrast keyhole planar echo sequence imaging method according to an embodiment of the present application. Oral and written informed consent was given prior to the experiment. The scanning device is the combined image medical uMR T magnetic resonance. Using a 32-channel head coil from a b value of 0-1000s/mm 2 At 50s/mm 2 DWI fully sampled K-space data for 21 different b-values at intervals, 3 per b-valueA diffusion direction. Other sequence scan parameters are:
field of view/image size/TR/TE/bandwidth/number of repetitions/flip angle =240 × 240mm 2 128X 128/3000ms/132.3ms/1790 Hz/1/90. Then densely sampling with central k-space region and surrounding k-space region at undersampling rate R P =4; the central k-space region is densely sampled and the surrounding k-space regions are undersampled at an undersampling rate R P =8, scan prospective undersampled data. Other sequence scan parameters are: field of view/image size/TR/TE/bandwidth/number of repetitions/flip angle =240 × 240mm 2 /240×240/3000ms/100.4ms/1790Hz/1/90°。
FIG. 5 shows the peripheral k-space undersampling rate R at low resolution P Retrospective experiments comparing =4 and 8 reconstruct the results. The comparison methods are named EPI + SPA-LLR and kEPI + LLR, and the result of reconstruction according to the example is kEPI + LLRT (variable contrast keyhole-EPI). As can be seen from the figure, kEPI + LLRT shows better images (closer to the golden standard) than kEPI + LLR and EPI + SPA-LLR, and particularly EPI + SPA-LLR has larger error in partial area.
FIG. 6 shows the surrounding k-space undersampling rate R at high resolution P And when =4 and 8, compared prospective experimental reconstruction results and corresponding diffusion parameter images. The comparison method is named as EPI + SPA-LLR and kEPI + LLR; the result of reconstruction according to the example is kEPI + LLRT. The EPI + SPA-LLR has the highest intensity residual artifact in the image global. The kEPI + LLRT constructed according to the implementation accurately reflects the details of the image, has no residual artifacts, and has a higher signal-to-noise ratio.
Therefore, the invention realizes the rapid high-resolution magnetic resonance diffusion imaging and reconstruction, the K space is collected by using the keyhole plane echo sequence with the randomly changed contrast, and the diffusion weighted image is reconstructed in the image dimension, the contrast dimension and the low-rank property of the coil dimension, so that the phase and motion artifacts caused by directly combining the K space are avoided, and the coil sensitivity parameter is avoided being solved. The robustness is improved, higher resolution and higher signal-to-noise ratio are realized, and the imaging time can be obviously reduced in the applications of diffusion quantitative imaging and the like. The method achieves balance on the resolution and the acquisition efficiency and has important significance in clinical application.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the apparatus, and the modules thereof provided by the present invention may be considered as a hardware component, and the modules included in the system, the apparatus, and the modules for implementing various programs may also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A fast multi-contrast magnetic resonance diffusion imaging and reconstruction method, comprising:
step S1: undersampling k-space data of a plurality of variable contrast diffusion weighted images by using a keyhole plane echo sequence through a plurality of receiving coils;
step S2: according to the acquisition result, constructing an objective function comprising a multi-coil image fidelity term and an image regular term based on coil, space and contrast domain tensor Low rank (Low-RankTensorConstraint);
and step S3: and minimizing the objective function through an optimization algorithm, reconstructing diffusion images of all receiving coils and acquiring related quantitative parameter images according to a specific mode of contrast change.
2. The fast multi-contrast magnetic resonance diffusion imaging and reconstruction method according to claim 1, wherein in the step S1:
the number of the receiving coils is more than or equal to 1;
the variable contrast includes: the contrast parameters between different TRs of the plane echo sequence can be arbitrarily varied, including but not limited to, arbitrary variations in b-value, diffusion encoding direction, and diffusion time;
for a specific one of the contrast parameters, the arbitrary variation is that the contrast parameters between different TRs remain unchanged or are different;
the keyhole-EPI sequence was acquired using variable density k-space:
at undersampling rate R in the central k-space region C Carrying out intensive collection;
at undersampling rate R in the surrounding k-space region P Uniformly shifting and undersampling so that adjacent TRs acquire cyclically shifted phase encoding lines;
the variable density k-space acquisition of the keyhole-EPI sequence is performed by:
determining a gradient momentum across a single phase encoding row; calculating other required blip gradient momentum spanning different phase coding rows according to the calculated result; carrying out time sequence arrangement on the required blip gradient corresponding to the momentum according to a preset sampling track; and acquiring k-space data by adopting a bipolar EPI gradient according to the calculation result.
3. The fast multi-contrast magnetic resonance diffusion imaging and reconstruction method according to claim 1, characterized in that in said step S2:
the image to be solved is a variable contrast diffusion weighted image under multiple coils, but not a diffusion weighted image after multiple coils are combined;
the objective function does not comprise coil sensitivity, and the objective function is solved without solving the coil sensitivity or carrying out self-correction of parallel imaging;
image fidelity terms based on coil, space and contrast domain tensor low rank make constraints on the variation range of an image to be solved by utilizing tensor low rank of a multi-coil variable contrast diffusion weighted image, wherein the image fidelity terms comprise constraints based on tensor low rank in a global space (GloballoylLow-RankTensorConstraint) and constraints based on local space tensor low rank (LocallyLow-RankTensorConstraint);
the objective function is:
y represents the preprocessed multidimensional K space data, D represents an undersampling operator, F represents a Fourier transform operator, x represents a multi-coil multi-contrast image to be reconstructed, alpha is a regularization coefficient, and B represents i x is a coil, space and contrast three-dimensional tensor taking the ith pixel as a center, R is a rank-solving operator of the three-dimensional tensor based on Tucker decomposition or PARAFAC decomposition, N v For a predetermined value, e.g. using a global low rank constraint, then N v =1, if a local low rank constraint is used, then N v May be equal to the number of pixels.
4. The fast multi-contrast magnetic resonance diffusion imaging and reconstruction method according to claim 1, characterized in that in said step S3:
the objective function can be solved through any optimization algorithm, including a gradient descent method, a conjugate gradient method, a quasi-Newton method, an alternative multiplier method, a near-end gradient method, a projection gradient method and a coordinate descent method, so as to reconstruct diffusion images of all receiving coils and obtain related quantitative parameter images according to specific application scenes.
5. The fast multi-contrast magnetic resonance diffusion imaging and reconstruction method of claim 1, characterized in that: the imaging method can be used for diffusion imaging application scenes and comprises the following steps: diffusion weighted imaging under multiple excitation, apparent diffusion coefficient quantitative imaging, diffusion tensor imaging, diffusion kurtosis imaging and incoherent motion imaging in a voxel.
6. A rapid multi-contrast magnetic resonance diffusion imaging and reconstruction system, comprising:
a module M1: undersampling k-space data of a plurality of variable contrast diffusion weighted images using a keyhole planar echo sequence through a plurality of receive coils;
a module M2: according to the acquisition result, constructing a target function comprising a multi-coil image fidelity term and an image regular term based on coil, space and contrast domain tensor low-rank property;
a module M3: and minimizing the objective function through an optimization algorithm, reconstructing diffusion images of all receiving coils and acquiring related quantitative parameter images according to a specific mode of contrast change.
7. The fast multi-contrast magnetic resonance diffusion imaging and reconstruction system according to claim 1, wherein in the module M1:
the number of the receiving coils is more than or equal to 1;
the variable contrast includes: the contrast parameters between different TRs of the plane echo sequence can be arbitrarily varied, including but not limited to, arbitrary variations in b-value, diffusion encoding direction, and diffusion time;
for a specific one of the contrast parameters, the arbitrary variation is that the contrast parameters between different TRs remain unchanged or are different;
the keyhole-EPI sequence was acquired using variable density k-space as follows:
at undersampling rate R in the central k-space region C Carrying out intensive collection;
at undersampling rate R in the surrounding k-space region P Uniformly shifting and undersampling so that adjacent TRs acquire cyclically shifted phase encoding lines;
the variable density k-space acquisition of the keyhole-EPI sequence is performed by:
determining a gradient momentum across a single phase encoding row; calculating other required blip gradient momentum spanning different phase coding rows according to the calculated result; carrying out time sequence arrangement on the required blip gradient corresponding to the momentum according to a preset sampling track; and acquiring k-space data by adopting a bipolar EPI gradient according to the calculation result.
8. The fast multi-contrast magnetic resonance diffusion imaging and reconstruction system of claim 1, wherein in the module M2:
the image to be solved is a variable contrast diffusion weighted image under multiple coils, but not a diffusion weighted image after multiple coils are combined;
the objective function does not comprise coil sensitivity, and the objective function is solved without solving the coil sensitivity or carrying out self-correction of parallel imaging;
image fidelity terms based on coil, space and contrast domain tensor low rank make constraints on the variation range of an image to be solved by utilizing tensor low rank of a multi-coil variable contrast diffusion weighted image, wherein the image fidelity terms comprise the constraints based on tensor low rank in a global space and the constraints based on local space tensor low rank;
the objective function is:
wherein y represents the preprocessed multidimensional K space data, D represents an undersampling operator, F represents a Fourier transform operator, x represents a multi-coil multi-contrast image to be reconstructed, alpha is a regularization coefficient, and B i x is a coil, space and contrast three-dimensional tensor taking the ith pixel as a center, R is a rank-solving operator of the three-dimensional tensor based on Tucker decomposition or PARAFAC decomposition, and N is v For a predetermined value, e.g. using a global low rank constraint, then N v =1, if a local low rank constraint is used, then N v May be equal to the number of pixels.
9. The fast multi-contrast magnetic resonance diffusion imaging and reconstruction system of claim 1, wherein in the module M3:
the objective function can be solved through any optimization algorithm, including a gradient descent method, a conjugate gradient method, a quasi-Newton method, an alternative multiplier method, a near-end gradient method, a projection gradient method and a coordinate descent method, so as to reconstruct diffusion images of all receiving coils and obtain related quantitative parameter images according to specific application scenes.
10. The fast multi-contrast magnetic resonance diffusion imaging and reconstruction system of claim 1, characterized in that: the imaging method can be used for diffusion imaging application scenes and comprises the following steps: diffusion weighted imaging under multiple excitations, apparent diffusion coefficient quantitative imaging, diffusion tensor imaging, diffusion kurtosis imaging and in-voxel incoherent motion imaging.
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