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  1. Free, publicly-accessible full text available June 1, 2025
  2. Abstract

    Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated microbubbles, resulting in prolonged imaging time to obtain detailed microvascular maps. Here, we introduce LOcalization with Context Awareness (LOCA)-ULM, a deep learning-based microbubble simulation and localization pipeline designed to enhance localization performance in high microbubble concentrations. In silico, LOCA-ULM enhanced microbubble detection accuracy to 97.8% and reduced the missing rate to 23.8%, outperforming conventional and deep learning-based localization methods up to 17.4% in accuracy and 37.6% in missing rate reduction. In in vivo rat brain imaging, LOCA-ULM revealed dense cerebrovascular networks and spatially adjacent microvessels undetected by conventional ULM. We further demonstrate the superior localization performance of LOCA-ULM in functional ULM (fULM) where LOCA-ULM significantly increased the functional imaging sensitivity of fULM to hemodynamic responses invoked by whisker stimulations in the rat brain.

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  3. Free, publicly-accessible full text available August 1, 2024
  4. null (Ed.)
    Stochastic Programming (SP) is used in disaster management, supply chain design, and other complex problems. Many of the real-world problems that SP is applied to produce large-size models. It is important but challenging that they are optimized quickly and efficiently. Existing optimization algorithms are limited in capability of solving these larger problems. Sample Average Approximation (SAA) method is a common approach for solving large scale SP problems by using the Monte Carlo simulation. This paper focuses on applying clustering algorithms to the data before the random sample is selected for the SAA algorithm. Once clustered, a sample is randomly selected from each of the clusters instead of from the entire dataset. This project looks to analyze five clustering techniques compared to each other and compared to the original SAA algorithm in order to see if clustering improves both the speed and the optimal solution of the SAA method for solving stochastic optimization problems. 
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