An Algorithm for Improving Simulated Brain Photoacoustic Signal
Subject Areas : Multimedia Processing, Communications Systems, Intelligent Systems
1 - Assistant Professor, Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran.
Keywords: Photoacoustic imaging, Compensation, Vector space similarity, M-MVA algorithm.,
Abstract :
Abstract
Photoacoustic imaging has been investigated for non-invasive detection of molecular, structural, and functional abnormalities in biological tissues. One of the important applications of this technology is brain imaging. However, skull’s acoustic effects reduce the effectiveness of this method. This paper presents an algorithm based on vector space similarity model to compensate for the skull’s effects. Numerical simulations show that using the genetic algorithm to estimate the optimal weight assigned to each feature vector improves compensation accuracy and reduces the normalized standard deviation error.
Introduction: Photoacoustic imaging works based on acoustic detection of optical absorption from tissue chromophores. Since the skull is the main dispersive barrier for the propagation of acoustic waves, it distorts the amplitude and phase of the received signal. This paper introduces a novel approach based on vector space similarity model for compensating the effects of the skull on the received brain photoacoustic signal.
Method: The necessary data is generated through a ray-tracing-based simulator. The extremum information of the signal in the time-frequency domain is used as feature vectors and their similarities are computed using a weighted sum, in which the optimal weight for each feature vector is determined through the genetic algorithm.
Results: The results indicate that the skull causes significant distortions to both the amplitude and phase of the received signal. Moreover, the results from the compensation algorithm indicate that utilizing the weighted sum of feature vector similarities improves the accuracy of compensation compared to the mean feature vector similarity criterion.
Discussion: The paper presents an effective method for compensating the skull induced distortions on brain photoacoustic signals. By employing vector space similarity model and optimizing feature vector weights, the proposed approach enhances compensation and reduces the error in the received signal, which can improve the accuracy of photoacoustic brain imaging.
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