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Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm.
J Magn Reson 2018; 286:91-98JM

Abstract

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k-space. This paper introduces an improved iterative algorithm based on p-thresholding technique for CS-MRI image reconstruction. The use of p-thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p-thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p-thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.

Authors+Show Affiliations

Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad, Pakistan.Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad, Pakistan. Electronic address: kaleem.arfeen@gmail.com.Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad, Pakistan.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29223565

Citation

Elahi, Sana, et al. "Compressively Sampled MR Image Reconstruction Using Generalized Thresholding Iterative Algorithm." Journal of Magnetic Resonance (San Diego, Calif. : 1997), vol. 286, 2018, pp. 91-98.
Elahi S, Kaleem M, Omer H. Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm. J Magn Reson. 2018;286:91-98.
Elahi, S., Kaleem, M., & Omer, H. (2018). Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm. Journal of Magnetic Resonance (San Diego, Calif. : 1997), 286, pp. 91-98. doi:10.1016/j.jmr.2017.11.008.
Elahi S, Kaleem M, Omer H. Compressively Sampled MR Image Reconstruction Using Generalized Thresholding Iterative Algorithm. J Magn Reson. 2018;286:91-98. PubMed PMID: 29223565.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm. AU - Elahi,Sana, AU - Kaleem,Muhammad, AU - Omer,Hammad, Y1 - 2017/11/21/ PY - 2017/03/16/received PY - 2017/10/19/revised PY - 2017/11/17/accepted PY - 2017/12/11/pubmed PY - 2017/12/11/medline PY - 2017/12/11/entrez KW - Compressed sensing KW - Iterative shrinkage algorithm KW - MRI KW - Non-linear reconstruction SP - 91 EP - 98 JF - Journal of magnetic resonance (San Diego, Calif. : 1997) JO - J. Magn. Reson. VL - 286 N2 - Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k-space. This paper introduces an improved iterative algorithm based on p-thresholding technique for CS-MRI image reconstruction. The use of p-thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p-thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p-thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors. SN - 1096-0856 UR - https://www.unboundmedicine.com/medline/citation/29223565/Compressively_sampled_MR_image_reconstruction_using_generalized_thresholding_iterative_algorithm_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1090-7807(17)30282-3 DB - PRIME DP - Unbound Medicine ER -