Compressively Sampled MR Image Reconstruction Using POCS with g-Factor as Regularization Parameter


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Resumo

Compressed sensing (CS) is an effective method to reduce k-space sampling for accelerated MRI data acquisition and reconstruction. Iterative-shrinkage algorithms provide an efficient numerical technique to minimize mixed ll − l2 norm minimization problems. These algorithms utilize a regularization parameter to introduce sparsity in the solution for CS recovery problem. This paper introduces a new method based on geometry factor (g-Factor) as an adaptive regularization parameter. For this purpose, Projection onto Convex Sets (POCS) algorithm is modified to include regularization term in the form of g-Factor and a priori constraint (data consistency) for image reconstruction from the highly under-sampled data. The performance of the proposed algorithm is verified using simulated and actual MRI data. The results show that g-Factor as a regularization parameter provides better image reconstruction from the highly under-sampled data as compared to a fixed regularization parameter in POCS.

Sobre autores

Muhammad Kaleem

COMSATS Institute of Information Technology

Autor responsável pela correspondência
Email: kaleem.arfeen@gmail.com
Paquistão, Islamabad

Mahmood Qureshi

COMSATS Institute of Information Technology

Email: kaleem.arfeen@gmail.com
Paquistão, Islamabad

Hammad Omer

COMSATS Institute of Information Technology

Email: kaleem.arfeen@gmail.com
Paquistão, Islamabad

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