Dynamic Susceptibility Contrast Perfusion Quantification using Spread Bases Function
B Rajeswari1, P Abdul Khayum2

1B Rajeswari, Research Scholar, JNTU, Anantapur (A.P), India.
2P Abdul Khayum, Professor, GPREC, Kurnool (A.P), India.
Manuscript received on 20 February 2019 | Revised Manuscript received on 11 March 2019 | Manuscript Published on 08 June 2019 | PP: 854-857 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E11690275S419/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Dynamic Susceptibility Contrast (DSC) perfusion Magnetic Resonance (MR) imaging of the brain provides tissue perfusion characterization. This characterization can be done by recovering scalar parameters like cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) and also tissue impulse response function. Scattering effect of bolus causes not only the information to reflect tissue perfusion and also provide macro vascular properties. The possibilities of obtaining disperse response functions and parameters can be done by performing deconvolution. The proposed method of Spread Bases Function (SBF) used to denote the response function in the presence of scattering for effective parameter estimation. The simulated results show that SBF deconvolution gives better performance than oSVD in the effective estimation of perfusion parameter, irrespective of the occurrence of scattering. Furthermore, the SBF method recovers response functions effectively that carry out with both healthy and pathological conditions, and offers the benefit of making no suspicions about the nature of scattering at different levels of perfusion. The simulated results are implemented on the digital head phantom.
Keywords: Perfusion, Dynamic Susceptibility Contrast, Spread Bases Function.
Scope of the Article: Software Defined Networking and Network Function Virtualization