ULTRA-SR Challenge: Assessment of Ultrasound Localization and TRacking Algorithms for Super-Resolution Imaging.

With the widespread interest and uptake of super-resolution ultrasound (SRUS) through localization and tracking of microbubbles, also known as ultrasound localization microscopy (ULM), many localization and tracking algorithms have been developed. ULM can image many centimeters into tissue in-vivo and track microvascular flow non-invasively with sub-diffraction resolution. In a significant community effort, we organized a challenge, Ultrasound Localization and TRacking Algorithms for Super-Resolution (ULTRA-SR). The aims of this paper are threefold: to describe the challenge organization, data generation, and winning algorithms; to present the metrics and methods for evaluating challenge entrants; and to report results and findings of the evaluation. Realistic ultrasound datasets containing microvascular flow for different clinical ultrasound frequencies were simulated, using vascular flow physics, acoustic field simulation and nonlinear bubble dynamics simulation. Based on these datasets, 38 submissions from 24 research groups were evaluated against ground truth using an evaluation framework with six metrics, three for localization and three for tracking. In-vivo mouse brain and human lymph node data were also provided, and performance assessed by an expert panel. Winning algorithms are described and discussed. The publicly available data with ground truth and the defined metrics for both localization and tracking present a valuable resource for researchers to benchmark algorithms and software, identify optimized methods/software for their data, and provide insight into the current limits of the field. In conclusion, Ultra-SR challenge has provided benchmarking data and tools as well as direct comparison and insights for a number of the state-of-the art localization and tracking algorithms.

Dehazing Ultrasound using Diffusion Models.

Echocardiography has been a prominent tool for the diagnosis of cardiac disease. However, these diagnoses can be heavily impeded by poor image quality. Acoustic clutter emerges due to multipath reflections imposed by layers of skin, subcutaneous fat, and intercostal muscle between the transducer and heart. As a result, haze and other noise artifacts pose a real challenge to cardiac ultrasound imaging. In many cases, especially with difficult-to-image patients such as patients with obesity, a diagnosis from B-Mode ultrasound imaging is effectively rendered unusable, forcing sonographers to resort to contrast-enhanced ultrasound examinations or refer patients to other imaging modalities. Tissue harmonic imaging has been a popular approach to combat haze, but in severe cases is still heavily impacted by haze. Alternatively, denoising algorithms are typically unable to remove highly structured and correlated noise, such as haze. It remains a challenge to accurately describe the statistical properties of structured haze, and develop an inference method to subsequently remove it. Diffusion models have emerged as powerful generative models and have shown their effectiveness in a variety of inverse problems. In this work, we present a joint posterior sampling framework that combines two separate diffusion models to model the distribution of both clean ultrasound and haze in an unsupervised manner. Furthermore, we demonstrate techniques for effectively training diffusion models on radio-frequency ultrasound data and highlight the advantages over image data. Experiments on both in-vitro and in-vivo cardiac datasets show that the proposed dehazing method effectively removes haze while preserving signals from weakly reflected tissue.

Expressing a moth abcc2 gene in transgenic Drosophila causes susceptibility to Bt Cry1Ac without requiring a cadherin-like protein receptor.

Bt toxins ingested by insect pests can bind to midgut receptors and cause death, although several steps in this process remain unclear. Multiple Bt toxin receptors have been identified in Lepidoptera, including a cadherin-like protein (CaLP), which is central to several models explaining Bt toxins' mode of action. Mutations in the Plutella xylostella ATP-dependent binding cassette transporter C2 (Px-abcc2), rather than CaLP, are genetically linked with Bt Cry1Ac resistance. Here we expressed Px-abcc2 in Drosophila and performed larval bioassays to determine whether this protein acts as an effective Bt receptor. Cry1Ac had no effect on larvae expressing Px-abcc2 in salivary glands, yet larvae expressing Px-abcc2 in the midgut were highly susceptible to both Cry1Ac protoxin and trypsin activated toxin. Furthermore, the CaLP orthologue has been lost from the Drosophila genome, making this a useful system for investigating the role of CaLP peptides from Manduca sexta (CR12-MPED), which are known to act as Bt synergists in larval feeding assays. Drosophila larvae expressing Px-ABCC2 in the midgut were fed LD50 concentrations of Cry1Ac toxin or protoxin, plus purified CR12-MPED cloned from M. sexta or P. xylostella. The M. sexta CR12-MPED protein acted synergistically with Cry1Ac protoxin and activated toxin significantly more effectively than the P. xylostella peptide. This work demonstrates ABCC2 is the major functional Cry1Ac receptor for P. xylostella and the importance of CaLP proteins in Bt mode of action may vary between different lepidopteran species.