An Optimized Cascaded Stochastic Resonance for the Enhancement of Brain MRI - 04/11/18

Doi : 10.1016/j.irbm.2018.08.002

M. Singh ^a,^b,^{^⁎} , A. Verma ^c, N. Sharma ^a
^a School of Biomedical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi - 221005, India
^b Department of Mechatronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka - 576104, India
^c Department of Radiodiagnosis and Imaging, Institute of Medical Sciences, Banaras Hindu University, Varanasi - 221005, India

^⁎Corresponding author.

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Graphical abstract

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Highlights

•	The algorithm proposes enhancement of MRI data based on cascaded stochastic resonance.
•	Cascading of two DSR models, i.e., modified neuron model and quartic bistable model.
•	MO-PSO adaptively selects the parameters of cascaded stochastic resonance.
•	Fitness functions for MO-PSO are two different non-referential image quality measures.
•	Another image quality measure selects best enhanced image from non-dominant solutions.

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Abstract

Background

The contrast enhancement of Magnetic Resonance Imaging (MRI) data is quite challenging as the noise present in this data also get amplified in this process. Dynamic Stochastic Resonance (DSR) is the technique that utilizes the noise to enhance the contrast of MRI data.

Method

The present study proposes the cascaded stochastic resonance, which exploits the properties of modified potential neuron model and quartic bistable model of DSR. The Multi-objective Particle Swarm Optimization (MOPSO) tunes the DSR parameters associated with the cascading of both the models. The MOPSO produces a set of the solution called Pareto front for the maximization of two image quality measures, i.e., contrast enhancement factor and universal image quality index. Further, the maximization of another image quality measure, i.e., anisotropy helps to obtain the optimum enhanced image from the Pareto fronts solution.

Results

The present study included the simulated and real MRI data. The results show that the proposed method obtained mean contrast enhancement factor, universal image quality index and anisotropy equal to 1.79, 0.78 and 0.021 respectively. These values are better than those obtained for classical bistable DSR and other conventional contrast enhancement techniques. The proposed algorithm has been tested on real MRI data as well and found valuable in the diagnosis of lacunar infarct and mesial temporal sclerosis.