BUbble Flow Field: a Simulation Framework for Evaluating Ultrasound Localization Microscopy Algorithms

Ultrasound contrast enhanced imaging has seen widespread uptake in research and clinical diagnostic imaging. This includes applications such as vector flow imaging, functional ultrasound and super-resolution Ultrasound Localization Microscopy (ULM). All of these require testing and validation during development of new algorithms with ground truth data. In this work we present a comprehensive simulation platform BUbble Flow Field (BUFF) that generates contrast enhanced ultrasound images in vascular tree geometries with realistic flow characteristics and validation algorithms for ULM. BUFF allows complex micro-vascular network generation of random and user-defined vascular networks. Blood flow is simulated with a fast Computational Fluid Dynamics (CFD) solver and allows arbitrary input and output positions and custom pressures. The acoustic field simulation is combined with non-linear Microbubble (MB) dynamics and simulates a range of point spread functions based on user-defined MB characteristics. The validation combines both binary and quantitative metrics. BFF's capacity to generate and validate user-defined networks is demonstrated through its implementation in the Ultrasound Localisation and TRacking Algorithms for Super Resolution (ULTRA-SR) Challenge at the International Ultrasonics Symposium (IUS) 2022 of the Institute of Electrical and Electronics Engineers (IEEE). The ability to produce ULM images, and the availability of a ground truth in localisation and tracking enables objective and quantitative evaluation of the large number of localisation and tracking algorithms developed in the field. BUFF can also benefit deep learning based methods by automatically generating datasets for training. BUFF is a fully comprehensive simulation platform for testing and validation of novel ULM techniques and is open source.Comment: 10 Pages, 9 Figure

Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures, but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues.Comment: 10 pages, 9 figure

Differentiation of Vascular Characteristics Using Contrast-Enhanced Ultrasound Imaging

Ultrasound contrast imaging has been used to assess tumour growth and regression by assessing the flow through the macro- and micro-vasculature. Our aim was to differentiate the blood kinetics of vessels such as veins, arteries and microvasculature within the limits of the spatial resolution of contrast-enhanced ultrasound imaging. The highly vascularised ovine ovary was used as a biological model. Perfusion of the ovary with SonoVue was recorded with a Philips iU22 scanner in contrast imaging mode. One ewe was treated with prostaglandin to induce vascular regression. Time-intensity curves (TIC) for different regions of interest were obtained, a lognormal model was fitted and flow parameters calculated. Parametric maps of the whole imaging plane were generated for 2 × 2 pixel regions of interest. Further analysis of TICs from selected locations helped specify parameters associated with differentiation into four categories of vessels (arteries, veins, medium-sized vessels and micro-vessels). Time-dependent parameters were associated with large veins, whereas intensity-dependent parameters were associated with large arteries. Further development may enable automation of the technique as an efficient way of monitoring vessel distributions in a clinical setting using currently available scanners.Agência financiadora Medical Research Council UK (MRC) 30800896 Science & Technology Facilities Council (STFC) ST/M007804/1 British Heart Foundation PG/10/021/28254 Life Sciences Discovery Fund 3292512 United States Department of Defense CA160415/PRCRPinfo:eu-repo/semantics/publishedVersio

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

Ultrasound Signal Processing: From Models to Deep Learning

Medical ultrasound imaging relies heavily on high-quality signal processing algorithms to provide reliable and interpretable image reconstructions. Hand-crafted reconstruction methods, often based on approximations of the underlying measurement model, are useful in practice, but notoriously fall behind in terms of image quality. More sophisticated solutions, based on statistical modelling, careful parameter tuning, or through increased model complexity, can be sensitive to different environments. Recently, deep learning based methods have gained popularity, which are optimized in a data-driven fashion. These model-agnostic methods often rely on generic model structures, and require vast training data to converge to a robust solution. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge. These model-based solutions yield high robustness, and require less trainable parameters and training data than conventional neural networks. In this work we provide an overview of these methods from the recent literature, and discuss a wide variety of ultrasound applications. We aim to inspire the reader to further research in this area, and to address the opportunities within the field of ultrasound signal processing. We conclude with a future perspective on these model-based deep learning techniques for medical ultrasound applications