US 11,668,777 B2
Adaptive joint sparse coding-based parallel magnetic resonance imaging method and apparatus and computer readable medium
Shanshan Wang, Guangdong (CN); Dong Liang, Guangdong (CN); Sha Tan, Guangdong (CN); Xin Liu, Guangdong (CN); and Hairong Zheng, Guangdong (CN)
Assigned to SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY, Guangdong (CN)
Appl. No. 16/760,956
Filed by SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY, Guangdong (CN)
PCT Filed Dec. 1, 2017, PCT No. PCT/CN2017/114172
§ 371(c)(1), (2) Date May 1, 2020,
PCT Pub. No. WO2019/104702, PCT Pub. Date Jun. 6, 2019.
Prior Publication US 2020/0256942 A1, Aug. 13, 2020
Int. Cl. G01R 33/561 (2006.01); G01R 33/56 (2006.01); G06T 11/00 (2006.01)
CPC G01R 33/5611 (2013.01) [G01R 33/5608 (2013.01); G06T 11/008 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20056 (2013.01); G06T 2207/20081 (2013.01)] 18 Claims
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1. A parallel magnetic resonance imaging method based on adaptive joint sparse codes, applied by a parallel magnetic resonance imaging apparatus, comprising:
step a: constructing a calibration-free parallel magnetic resonance imaging model based on joint sparse codes, that is, a reconstructed model, wherein the model is defined as:

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wherein V denotes a reconstructed image, D∈cM×P denotes an overcomplete (P>>M) dictionary, θ denotes an objective function, X=(Xl,1|Xl,2 . . . Xl,j . . . ,|Xl,j)∈cp×j denotes a sparse matrix, Xl,j∈cP denotes an l-th extracted patch from a j-th channel image, FM denotes a Fourier transformation, vj denotes an image of each channel, V denotes a matrix formed by images vj of all channels, fj denotes K-space data, Rl∈cM×N denotes a patch extraction matrix, M denotes an atom size, N denotes a vectorized size of the image, P denotes a number of atoms, a subscript F denotes the Fourier transformation, λ denotes a data fitting term weight, β denotes a joint sparse regularization weight, and ∥Xl∥2,1 denotes a joint sparse term, and
wherein

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 wherein Xlp,j2, denotes a p-th pixel for the l-th extracted patch from the j-th channel image;
step b: temporarily fixing X, and solving the dictionary D by using a gradient descent method:

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wherein γdenotes a learning rate, and k denotes a number of iterations, and
wherein

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 applies, wherein the formula

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 denotes a reciprocal of the reconstructed objective function to the dictionary D and H denotes conjugate transpose, and thus the following formula is obtained:
Dk+1=Dk−γ(DX−RlV)XH  (3);
step c: updating joint sparse coefficient X;
step d: updating vj by using the following formula:

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step e: updating the K-space data by using the following formula:
fjk+1=fjk+fj−FMvjk+1  (5),
wherein fjk denotes an updated value of a k-th iteration of the j-th channel;
step f: performing an inverse Fourier transformation on the K-space data and re-updating vj to obtain the following formula:
vjk+1=FHfjk+1  (6); and
step g: obtaining an updated image based on the images vj of the all channels.