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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. 

Ultrasound computed tomography (USCT) is one of the advanced imaging techniques used in structural health monitoring (SHM) and medical imaging due to its relatively low-cost, rapid data acquisition process. The time-domain full waveform inversion (TDFWI) method, an iterative inversion approach, has shown great promise in USCT. However, such an iterative process can be very time-consuming and computationally expensive but can be greatly accelerated by integrating an AI-based approach (e.g., convolution neural network (CNN)). Once trained, the CNN model takes low-iteration TDFWI images as input and instantaneously predicts material property distribution within the scanned region. Nevertheless, the quality of the reconstruction with the current CNN degrades with the increased complexity of material distributions. Another challenge is the availability of enough experimental data and, in some cases, even synthetic surrogate data. To alleviate these issues, this paper details a systematic study of the enhancement effect of a 2D CNN (U-Net) by improving the quality with limited training data. To achieve this, different augmentation schemes (flipping and mixing existing data) were implemented to increase the amount and complexity of the training datasets without generating a substantial number of samples. The objective was to evaluate the enhancement effect of these augmentation techniques on the performance of the U-Net model at FWI iterations. A thousand numerically built samples with acoustic material properties are used to construct multiple datasets from different FWI iterations. A parallelized, high-performance computing (HPC) based framework has been created to rapidly generate the training data. The prediction results were compared against the ground truth images using standard matrices, such as the structural similarity index measure (SSIM) and average mean square error (MSE). The results show that the increased number of samples from augmentations improves shape imaging of the complex regions even with a low iteration FWI training data. 

Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating “avatars” (herein defined as an extension of “digital twins”) of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.