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In recent years, singular value decomposition (SVD)-based clutter filters have received widespread attention in ultrasound flow imaging owing to their high performance over traditional clutter filters in suppressing clutter signals. The excellent performance of the SVD clutter filter depends on its adaptive nature. The SVD clutter filter adaptively rejects echoes from slowly moving clutters, allowing visualization of echoes from blood cells. Owing to this property, the SVD filter works well throughout a cardiac cycle. Recently, deep neural networks have been used for a variety of tasks. The adaptive nature of deep neural networks would be beneficial for clutter filtering in ultrasonic blood flow imaging. In the present study, we conducted a preliminary study on clutter filtering using a long short-term memory neural network. Experimental results suggested that the proposed deep-learning clutter filter achieved a comparable performance than SVD one in terms of contrast values.

Performance of Simple Genetic Algorithm Inserting Forced Inheritance Mechanism and Parameters Relaxation 47 3. Regeneration takes the value from the individuals by the percentage to be regenerated. 4. This population is converted to binary representation. 5. The position that will occupy the regenerated ones in the original population is determined without altering the number of individuals. 6. Reinsert the regenerated population into a sector of the original population. 7. The best individual in the regenerated population introduces itself, looking forward not to alter the number of individuals.Following the development of the genetic algorithm, taking the best individuals from the population will pay the selection of those who have been outfitted as parents of the new generations.Bio-Inspired Computational Algorithms and Their Applications 62 impossible; but if the parameters were optimized one at the time, it is then possible to handle its interactions and, for a given problem, the values of the selected parameters are not necessarily the optimal ones, but if they are analyzed uniformly they will generate more significant values.

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