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

3D Super-Resolution Ultrasound with Adaptive Weight-Based Beamforming

Super-resolution ultrasound (SRUS) imaging through localising and tracking sparse microbubbles has been shown to reveal microvascular structure and flow beyond the wave diffraction limit. Most SRUS studies use standard delay and sum (DAS) beamforming, where large main lobe and significant side lobes make separation and localisation of densely distributed bubbles challenging, particularly in 3D due to the typically small aperture of matrix array probes. This study aims to improve 3D SRUS by implementing a low-cost 3D coherence beamformer based on channel signal variance, as well as two other adaptive weight-based coherence beamformers: nonlinear beamforming with p-th root compression and coherence factor. The 3D coherence beamformers, together with DAS, are compared in computer simulation, on a microflow phantom, and in vivo. Simulation results demonstrate that the adaptive weight-based beamformers can significantly narrow the main lobe and suppress the side lobes for modest computational cost. Significantly improved 3D SR images of microflow phantom and a rabbit kidney are obtained through the adaptive weight-based beamformers. The proposed variance-based beamformer performs best in simulations and experiments.Comment: Ultrasound localisation microscopy (ULM), super-resolution, contrast-enhanced ultrasound, 3D beamformin

Ultrafast 3-D Super Resolution Ultrasound using Row-Column Array specific Coherence-based Beamforming and Rolling Acoustic Sub-aperture Processing: In Vitro, In Vivo and Clinical Study

The row-column addressed array is an emerging probe for ultrafast 3-D ultrasound imaging. It achieves this with far fewer independent electronic channels and a wider field of view than traditional 2-D matrix arrays, of the same channel count, making it a good candidate for clinical translation. However, the image quality of row-column arrays is generally poor, particularly when investigating tissue. Ultrasound localisation microscopy allows for the production of super-resolution images even when the initial image resolution is not high. Unfortunately, the row-column probe can suffer from imaging artefacts that can degrade the quality of super-resolution images as `secondary' lobes from bright microbubbles can be mistaken as microbubble events, particularly when operated using plane wave imaging. These false events move through the image in a physiologically realistic way so can be challenging to remove via tracking, leading to the production of 'false vessels'. Here, a new type of rolling window image reconstruction procedure was developed, which integrated a row-column array-specific coherence-based beamforming technique with acoustic sub-aperture processing for the purposes of reducing `secondary' lobe artefacts, noise and increasing the effective frame rate. Using an {\it{in vitro}} cross tube, it was found that the procedure reduced the percentage of `false' locations from $\sim$26\% to $\sim$15\% compared to traditional orthogonal plane wave compounding. Additionally, it was found that the noise could be reduced by $\sim$7 dB and that the effective frame rate could be increased to over 4000 fps. Subsequently, {\it{in vivo}} ultrasound localisation microscopy was used to produce images non-invasively of a rabbit kidney and a human thyroid

4D ultrafast blood flow imaging comparison: vector Doppler, transverse oscillation and speckle tracking

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