The Efficiency of Applying Compressed Sampling and Multi-Resolution Into Ultrasound Tomography
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Abstract
Introduction: This publication is the product of research developed within the research lines of the Smart Sensing, Signal Processing, and Applications (3SPA) research group throughout 2018, which supports the work of a doctor’s degree at VNU University of Engineering & Technology, Vietnam.
Problem: The limitations of diagnostic ultrasound techniques using echo information has motivated the study of new imaging models in order to create additional quantitative ultrasound information in multi-model imaging devices. A promising solution is to use image sound contrast because it is capable of detecting changes in diseased tissue structures. Ultrasound tomography shows speed-of-sound changes in the propagation medium of sound waves. This technique is primarily used for imaging cancer-causing cells in womens’ breasts. The Distorted Born Iterative Method (DBIM), based on the first-order Born approximation, is an efficient diffraction tomography approach. The compressed sensing technique is utilized for DBIM to obtain the high-quality ultrasound image, although the image reconstruction process is quite long.
Objective: The objective of the research is to propose an combined method for the efficient ultrasound tomography.
Methodology: In this paper, we proposed an approach to enhance the imaging quality and to reduce the imaging time by applying the compressed sensing technique along with the multi-resolution technique for the DBIM.
Results: The simulation results indicate that the imaging time is reduced by 33% and the imaging quality is improved by 83%.
Conclusion: This project seeks to propose an improvement in ultrasound tomography. The simulated results confirmed the realibility of the propsed method.
Originality: Through this research, a combined method of compressed sensing and multiple resolution are formulated for the first time in ultrasound tomography.
Limitations: The lack of experiments to confirm the proposed method.
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