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MR imaging reconstruction using a modified descent‐type alternating direction method
Author(s) -
Chen Hao,
Tao Jinxu,
Sun Yuli,
Qiu Bensheng,
Ye Zhongfu
Publication year - 2016
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22156
Subject(s) - augmented lagrangian method , descent (aeronautics) , descent direction , regularization (linguistics) , gradient descent , compressed sensing , total variation denoising , computer science , algorithm , norm (philosophy) , multiplier (economics) , iterative reconstruction , mathematics , mathematical optimization , artificial intelligence , image (mathematics) , physics , meteorology , artificial neural network , political science , law , economics , macroeconomics
In the magnetic resonance imaging (MRI) field, total variation (TV) which is theℓ 1 ‐norm of the gradient‐magnitude images (GMI) is widely used as the regularization in the compressive sensing (CS) based reconstruction algorithm. Based on the classic augmented Lagrangian multiplier method, we propose a modified descent‐type alternating direction method (ADM) for solving the TV regularized reconstruction problems in the following sense: an iteration result generated by the ADM is utilized to generate a descent direction; an appropriate step size along this descent direction is identified; and the penalty parameters are updated. The proposed algorithm effectively combines alternating direction technique with the descent‐type method. Extensive results demonstrate that the proposed algorithm, is competitive with, and often outperforms, other state‐of‐the‐art solvers in the field.