Variable Density Sampling Trajectories for Compressive Sensed MRI
Kavitha S1, B. Aziz Musthafa2
1Kavitha S*, Department, of Electonics and Communication, NMAMIT Nitte, affiliated VTU Belagavi, Udupi, India.
2Dr. B. Aziz Musthafa, Department of Computer Science, BIT Mangalore, Mangalore, India. Email:
Manuscript received on December 16, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 1264-1267 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8712019320/2020©BEIESP | DOI: 10.35940/ijitee.C8712.019320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Diagnosis of diseases require high resolution images of human body parts. Magnetic Resonance Imaging (MRI) is a popular technology commonly used for this purpose. In addition to having several benefits, this technology has few shortcomings also. One of them is its high scanning time. In MRI acquisition of image is based on the principle of traditional sampling theorem. The novel sampling theory called as Compressive Sensing (CS) which allows the reconstruction of sparse signals from undersampled data. The application of CS onto MRI will drastically reduce the acquisition time and hence scanning time. In this manuscript analysis and application of CS on to MRI is demonstrated. Simulations are carried out using Variable Density Sampling trajectories (VDS). Then a comparative study is made in terms of Signal to Noise Ratio (SNR) and execution time based on the result obtained.
Keywords: Magnetic Resonance Imaging, Compressive Sensing, Variable Density Sampling, Signal to Noise Ratio, execution time.
Scope of the Article: Signal and Speech Processing