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Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l1-Norm Approximation.
Int J Biomed Imaging 2018; 2018:7803067IJ

Abstract

Transformed domain sparsity of Magnetic Resonance Imaging (MRI) has recently been used to reduce the acquisition time in conjunction with compressed sensing (CS) theory. Respiratory motion during MR scan results in strong blurring and ghosting artifacts in recovered MR images. To improve the quality of the recovered images, motion needs to be estimated and corrected. In this article, a two-step approach is proposed for the recovery of cardiac MR images in the presence of free breathing motion. In the first step, compressively sampled MR images are recovered by solving an optimization problem using gradient descent algorithm. The L1-norm based regularizer, used in optimization problem, is approximated by a hyperbolic tangent function. In the second step, a block matching algorithm, known as Adaptive Rood Pattern Search (ARPS), is exploited to estimate and correct respiratory motion among the recovered images. The framework is tested for free breathing simulated and in vivo 2D cardiac cine MRI data. Simulation results show improved structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE) with different acceleration factors for the proposed method. Experimental results also provide a comparison between k-t FOCUSS with MEMC and the proposed method.

Authors+Show Affiliations

Electrical Engineering Department, International Islamic University, Islamabad, Islamabad, Pakistan.Electrical Engineering Section, UniKL BMI, Gombak, Selangor, Malaysia.Electrical Engineering Department, Air University Islamabad, Islamabad, Pakistan.Electrical Engineering Section, UniKL BMI, Gombak, Selangor, Malaysia.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29610569

Citation

Bilal, Muhammad, et al. "Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l1-Norm Approximation." International Journal of Biomedical Imaging, vol. 2018, 2018, p. 7803067.
Bilal M, Shah JA, Qureshi IM, et al. Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l1-Norm Approximation. Int J Biomed Imaging. 2018;2018:7803067.
Bilal, M., Shah, J. A., Qureshi, I. M., & Kadir, K. (2018). Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l1-Norm Approximation. International Journal of Biomedical Imaging, 2018, p. 7803067. doi:10.1155/2018/7803067.
Bilal M, et al. Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l1-Norm Approximation. Int J Biomed Imaging. 2018;2018:7803067. PubMed PMID: 29610569.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Respiratory Motion Correction for Compressively Sampled Free Breathing Cardiac MRI Using Smooth l1-Norm Approximation. AU - Bilal,Muhammad, AU - Shah,Jawad Ali, AU - Qureshi,Ijaz M, AU - Kadir,Kushsairy, Y1 - 2018/01/23/ PY - 2017/09/20/received PY - 2017/11/19/revised PY - 2017/12/21/accepted PY - 2018/4/4/entrez PY - 2018/4/4/pubmed PY - 2018/4/4/medline SP - 7803067 EP - 7803067 JF - International journal of biomedical imaging JO - Int J Biomed Imaging VL - 2018 N2 - Transformed domain sparsity of Magnetic Resonance Imaging (MRI) has recently been used to reduce the acquisition time in conjunction with compressed sensing (CS) theory. Respiratory motion during MR scan results in strong blurring and ghosting artifacts in recovered MR images. To improve the quality of the recovered images, motion needs to be estimated and corrected. In this article, a two-step approach is proposed for the recovery of cardiac MR images in the presence of free breathing motion. In the first step, compressively sampled MR images are recovered by solving an optimization problem using gradient descent algorithm. The L1-norm based regularizer, used in optimization problem, is approximated by a hyperbolic tangent function. In the second step, a block matching algorithm, known as Adaptive Rood Pattern Search (ARPS), is exploited to estimate and correct respiratory motion among the recovered images. The framework is tested for free breathing simulated and in vivo 2D cardiac cine MRI data. Simulation results show improved structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE) with different acceleration factors for the proposed method. Experimental results also provide a comparison between k-t FOCUSS with MEMC and the proposed method. SN - 1687-4188 UR - https://www.unboundmedicine.com/medline/citation/29610569/Respiratory_Motion_Correction_for_Compressively_Sampled_Free_Breathing_Cardiac_MRI_Using_Smooth_l1_Norm_Approximation_ L2 - https://dx.doi.org/10.1155/2018/7803067 DB - PRIME DP - Unbound Medicine ER -