Author(s) : Nazrin V A, Anju George

Volume & Issue : VOLUME 2 / 2017 , ISSUE 2

Page(s) : 47-54


Magnetic Resonance Imaging (MRI) is an emerging technique not only due to its reliability but also as it do not cause any impair-ment to human body. Nevertheless MRI has high scan time and cost due to its complex reco-nstruction procedure. The above mentioned factors are dependent on the number of meas-urements taken for reconstruction. Compressive Sensing (CS) is a method by which the image/signal can be reconstructed even if the signal is under sampled. Sparsity and Incoh-erence is an important aspect for reconstruction. Orthogonal Matching Pursuit (OMP) is one of the iterative reconstruction algorithm which implements effective reconstruction from fewer samples. Also it ensures that a coefficient is only taken once resulting in lesser iteration. However the computational complexity per iteration is very high for OMP algorithm. Implementing OMP algorithm in MRI reduces the scan time as the number of iteration is less. However due to the increased complexity the cost can’t be reduced.


Compressive Sensing, Incoherence, OMP, Sparsity


[1]Hassan Rabah,abbes Amira,basant Kumar Mohanty, Somaya Almaadeed, And Pramod Kumar Meher, “FPGA Implementation Of Orthogonal Mat-ching Pursuit For Compressive Sensing Recon-struction”, IEEE Transactions On Very Large Scale Integration (VLSI) Systems, VOL. 23, No. 10, October 2015B. Zhang, C. Sconyers, C. Byington, R. Patrick, M. Orchard, and G. Vachtsevanos, “A probabilistic fault detection approach: Application to bearing fault detection,” IEEE Trans. Ind. Electron., Vol. 58, no. 5,pp. 2011_8, May 2011.
[2]Suetens, Paul. Fundamentals of medical imaging. Cambridge university press, 2009.
[3]Duarte, Marco F., et al. "Single-pixel imaging via compressive sampling."IEEE Signal Processing Magazine 25.2 (2008): 83.
[4]Olivier Taeymans, Dominique G. Penninck, Rac-hel M. Peters, ’comparison Between Clinical, Ultra-sound, Ct, Mri, And Pathology Findings In Dogs Presented For Suspected Thyroid Carcinoma”, IEEE J, vet rediol ultrasound,Vol 54,No1, pp 61-70
[5]Jan Aelterman, Hiˆep Quang-Luong, Bart-Goos-sens, AleksandraPizˇurica, Wilfried Philips ,”Au-gmented Lagrangian based reconstruction of non-uniformly sub-Nyquist sampled MRI data”, ELSEIVER J, 2011, Signal Processing ,Vol 91, pp 2731-2742
[6]Israa Tawfic∗, Sema Kayhan ,” Compressed sensing of ECG signal for wireless system with new fast iterative method “, Elseiver J, 2015, computers and programs in biomedicine(122), pp 437-449
[7]Marco F. Duarte, And Yonina C. Eldar,” Struc-tured Compressed Sensing:from Theory To Appli-cations”, IEEE Transactions On Signal Processing, Vol. 59, No. 9, September 2011

[8]Lin Bai, Patrick Maechler, Michael Muehlber-ghuber, And Hubert Kaeslin, “High-speed Compr-essed Sensing Reconstruction On Fpga Using Omp And Amp” IEEE J,2012
[9]Mei Feng And Felix Krahmer,” An Rip-based Approach To Quantization For Compressed Sensing”, IEEE signal processing letters, vol. 21, no. 11, november 2014
[10]Emamnuel J. Candès,” Compressive sampling” Proceedings of the International Congressof Mathe-maticians, Madrid, Spain, 2006 © 2006 European Mathematical Society
[11]Richard Baraniuk, Rice University.“Compr-essive Sensing”, Lecture Notes in IEEE Signal Proce-ssing Magazine Volume 24, July 2007
[12]Simon Foucart, Holger Rauhut,” A Mathem-atical Introduction to Compressive Sensing”, text-book
[13]Pinto, Sergio S., et al. "Compressive Sensing Hardware in 1-D Signals."Tecciencia 10.19 (2015): 5-10.
[14]Quan, Yinghui, et al. "FPGA Implementation of real-time Compressive Sensing with partial Fourier Dictionary." International Journal of Antennas and Propagation 2016 (2016).
[15]Septimus, Avi, and Raphael Steinberg. "Comp-ressive sampling hardware reconstructi on." Proce-edings of 2010 IEEE International Symposium on Circuits and Systems. IEEE, 2010. [16]Dymarski, Przemyslaw, Nicolas Moreau, and Gaël Richard. "Greedy sparse decompositions: a comparative study." EURASIP Journal on Advances in Signal Processing 2011.1 (2011): 1. [17]Ren, Fengbo, et al. "A single-precision compre-ssive sensing signal reconstruction engine on FPGAs." 2013 23rd International Conference on Field programmmable Logic and Applications. IEEE, 2013. [18]Kulkarni, Amey, and Tinoosh Mohsenin. "Accelerating compressive sensing reconstruction OMP algorithm with CPU, GPU, FPGA and domain specific many-core." 2015 IEEE International Symp-osium on Circuits and Systems (ISCAS). IEEE, 2015.

[19]Y. Jianbo, “Local and nonlocal preserving proje-ction for bearing defect classification and perfor-mance assessment,” IEEE Trans. Ind. Electron., Vol. 59, no. 5, pp. 2363_76, May 2012.
[20]M. D. Prieto, G. Cirrincione, A. G Espinosa, J. A. Ortega, and H. Henao, “Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks,”IEEE Trans. Ind. Electron., Vol. 60, no. 8, pp. 3398_407, Aug. 2013.