US 11,885,863 B2Granted

Simultaneous use of joint and individual information terms in multicontrast compressive sensing reconstruction

AssigneeAselsan Elektronik Sanayi Ve Ticaret Anonim Sirketi

InventorsEmre Kopanoglu, Alper Gungor, Huseyin Emre Guven, Tolga Cukur

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Abstract

A method to reconstruct images from raw MRI data, and recover an image by solving an optimization problem. Hence, the method is an image reconstruction system for image recovery contrary to similar methods, the method reconstructs all images using both joint and individual objective functions. The method includes: acquiring with the MRI system, multiple images under an influence of different contrast mechanisms, wherein the different contrast mechanisms belong to a same anatomy, solving an optimization problem with an optimization algorithm using both joint and individual objective functions simultaneously for each of multiple image contrasts or a subset of the multiple image contrasts, to reconstruct the multiple images from acquired data.

Claims12

Dependent claims

A method for reconstructing images of a magnetic resonance imaging (MRI) system comprising the steps of;

acquiring with the MRI system, multiple images under an influence of different contrast mechanisms, wherein the different contrast mechanisms belong to a same anatomy,
solving an optimization problem with an optimization algorithm using both joint and individual objective functions simultaneously for each of multiple image contrasts or a subset of the multiple image contrasts, to reconstruct the multiple images from acquired data,
wherein the optimization problem solved with analysis based methods is as follows;

A
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-
γ
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x
^
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α
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CTV
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(
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+
β
⁢
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GSp
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(
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+
∑
i
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γ
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TV
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+
∑
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Sp
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where Ai denotes a transformation matrix, wherein the transformation matrix maps samples of an image to the samples in a data domain, and the data domain comprises a Fourier domain, {circumflex over (x)}i denotes a reconstructed image, and ŷi denotes associated acquired data for each of the multiple image contrasts;
CTV, GSp, TV, and Sp denote a Color Total Variation, a Group Sparsity (12-1-norm), an individual Total Variation and an individual Sparsity (11-norm), respectively, and the Color Total Variation, the Group Sparsity, the individual Total Variation and the individual Sparsity are defined as:
CTV(|x|)=√{square root over (ΣnΣki=1k((∇1|x(i)[n]|)2+(∇2|x(i)[n]|)2),)}
∥x∥2,1=Σn√{square root over (Σi=1k|x(i)[n]|2)},
TV(|x(i)∥)=Σn√{square root over ((∇1|x(i)[n]|)2+(∇2|x(i)[n]|)2)},
∥x(i)∥1=Σn|x(i)[n]|,
wherein functions CTV and GSp are applied jointly on all the multiple image contrasts, or the subset of the multiple image contrasts, TV and Sp functions are applied on each of the multiple image contrasts individually.

81 more elements

A method for reconstructing images of a magnetic resonance imaging (MRI) system comprising the steps of;

acquiring with the MRI system, multiple images under an influence of different contrast mechanisms, wherein the different contrast mechanisms belong to a same anatomy,
solving an optimization problem with an optimization algorithm using both joint and individual objective functions simultaneously for each of multiple image contrasts or a subset of the multiple image contrasts, to reconstruct the multiple images from acquired data,
wherein the optimization problem solved with a synthesis based method is formulated as below with an approach considering a signal, a linear combination of basis atoms of a dictionary;
⁢
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CTV
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β
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GSp
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∑
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Sp
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where Ai denotes a transformation matrix, wherein the transformation matrix maps samples of an image to the samples in a data domain, and the data domain comprises a Fourier domain, {circumflex over (x)}i denotes a reconstructed image, and yi denotes associated acquired data for each of the multiple image contrasts;
CTV, GSp, TV, and Sp denote a Color Total Variation, a Group Sparsity (12-1-norm), an individual Total Variation and an individual Sparsity (11-norm), respectively, and the Color Total Variation, the Group Sparsity, the individual Total Variation and the individual Sparsity are defined as:
CTV(|x|)=Σn√{square root over (Σki=1k((∇1|x(i)[n]|)2+(∇2|x(i)[n]|)2),)}
∥x∥2,1=Σn√{square root over (Σi=1k|x(i)[n]|2)},
TV(|x(i)∥)=Σn√{square root over ((∇1|x(i)[n]|)2+(∇2|x(i)[n]|)2)},
∥x(i)∥1=Σn|x(i)[n]|.

116 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/047,728Filed 18 Apr 2018
Published: US 2021/0116528 A122 Apr 2021
Granted: 30 Jan 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification8 codes

G01R 33/5608
Image enhancement or correction, e.g. subtraction or averaging techniques , e.g. improvement of signal-to-noise ratio and resolution: data…
G01R 33/5611
By reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences: parallel magnetic resonance imaging, e.g.…
G06T 5/50
Image enhancement or restoration: using two or more images, e.g. averaging or subtraction
G06T 12/20
Tomographic reconstruction from projections: inverse problem, i.e. transformations from projection space into object space