MRI Image Reconstruction using Compressive Sensing
Penta Anil Kumar1, Rajeev Sunkara2, Veera Punnaiah Manda3
1Penta Anil Kumar, Assistant Professor, Dept of E.C.E, Gudlavalleru Engineering College, Gudlavalleru, A.P.
2Rajeev Sunkara, Academic Consultant, Dept of ECE, Krishna University of Engineering and Technology, Krishna Dt, A.P.
3Veera Punnaiah Manda, Assistant Professor, Dept of ECE, Gudlavalleru Engineering College, Gudlavalleru, Krishna Dt, A.P.
Manuscript received on 01 March 2019 | Revised Manuscript received on 07 March 2019 | Manuscript published on 30 July 2019 | PP: 5256-5260 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1056078219/19©BEIESP | DOI: 10.35940/ijrte.B1056.078219
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© 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: A large number of diagnostic images which also include the MRIs are generated by the imaging departments of the hospitals for medical and legal reasons. This results in the creation of a huge amount of data in the form of images which are required to be stored for a long period. The primary challenge for the picture archiving and communication systems (PACS) allowing to store the image data and the display and reconstruction of the image for recalling at various sites. Image compression and reconstruction are necessary to cope up with these tasks. Significant efforts have been made in the recent towards the application of compressive sensing techniques for acquiring the data in MRI process. The primary aim of the theory of Compressive Sensing (CS) in signal processing is reducing the quantity of data that is acquired for successfully reconstructing the signals. Decreasing the number of coefficients of the acquired images will result in reduced acquisition time i.e. nothing but the duration for which the images are exposed to the MRI apparatus. This paper aims at using optimization algorithms in designing the scanner of the MR integrated with the CS, which results in the reduction of the scan time of the MRI. From a small set of acquired samples, images of satisfactory quality can be obtained. Various Compressive Sensing based optimization algorithms for reconstructing the MRI images are assessed, and a relative comparison is done for further research in this paper.
Keywords: Compressive Sensing (CS), Magnetic Resonance Imaging (MRI), Image Compression, Image Reconstruction, K-Space.
Scope of the Article: Image Processing and Pattern Recognition