Streamlining Clinical Applicability of Ultrasound Localization Microscopy via Advanced Reconstruction Models

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The ULMARM, or Ultrasound Localization Microscopy with Advanced Reconstruction Models, seeks to improve ultrasound imaging by addressing key challenges in Ultrasound Localization Microscopy (ULM). Traditional ULM methods require long acquisition times and ultra high frame rates to track microbubbles (MBs) accurately, stretching the capabilities of current clinical ultrasound systems. ULMARM aims to solve these challenges by developing advanced reconstruction techniques and integrating deep learning (DL) models. The goal is to relax frame rate demands while maintaining high-quality super-resolved imaging and accurate motion tracking, making ULM more feasible for clinical applications. In ULMARM, I propose recasting ULM as a mathematical inverse problem to achieve super-resolution in both space and time jointly. By treating the relationship between MB movement and ultrasound frames as a generative forward model, I will infer the most probable MB trajectories from observations using a Bayesian posterior maximization approach. To tackle inherent challenges, I will utilize advanced deep generative models and strong data-driven priors to streamline the inference process. The ULMARM project is organized into six work packages (WPs). WP1 focuses on new sparse coding methods to relax frame rate while preserving image quality. WP2 develops mathematical models for direct reconstruction, including motion compensation for improved accuracy. WP3 uses DL to reconstruct missing data, improving both spatial and temporal resolution. WP4 extends these methods to 3D ULM, tackling challenges like high volume rates and out-of-plane motion. WP5 focuses on evaluating these techniques with experimental and clinical datasets. WP6 handles project management and training. The MSCA Fellowship will provide crucial resources and collaboration opportunities, helping me advance my research in ULMARM, establish innovative techniques to improve ULM’s clinical use, and career development.