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Space situational awareness systems primarily focus on detecting and tracking space objects, providing crucial positional data. However, understanding the complex space domain requires characterising satellites, often involving estimation of bus and solar panel sizes. While inverse synthetic aperture radar allows satellite visualisation, developing deep learning models for substructure segmentation in inverse synthetic aperture radar images is challenging due to the high costs and hardware requirements. The authors present a framework addressing the scarcity of inverse synthetic aperture radar data through synthetic training data. The authors approach utilises a few‐shot domain adaptation technique, leveraging thousands of rapidly simulated low‐fidelity inverse synthetic aperture radar images and a small set of inverse synthetic aperture radar images from the target domain. The authors validate their framework by simulating a real‐case scenario, fine‐tuning a deep learning‐based segmentation model using four inverse synthetic aperture radar images generated through the backprojection algorithm from simulated raw radar data (simulated at the analogue‐to‐digital converter level) as the target domain. The authors results demonstrate the effectiveness of the proposed framework, significantly improving inverse synthetic aperture radar image segmentation across diverse domains. This enhancement enables accurate characterisation of satellite bus and solar panel sizes as well as their orientation, even when the images are sourced from different domains. [ABSTRACT FROM AUTHOR]

Background: T1 mapping and extracellular volume (ECV) have the potential to guide patient care and serve as surrogate end-points in clinical trials, but measurements differ between cardiovascular magnetic resonance (CMR) scanners and pulse sequences. To help deliver T1 mapping to global clinical care, we developed a phantom-based quality assurance (QA) system for verification of measurement stability over time at individual sites, with further aims of generalization of results across sites, vendor systems, software versions and imaging sequences. We thus created T1MES: The T1 Mapping and ECV Standardization Program. Methods: A design collaboration consisting of a specialist MRI small-medium enterprise, clinicians, physicists and national metrology institutes was formed. A phantom was designed covering clinically relevant ranges of T1 and T2 in blood and myocardium, pre and post-contrast, for 1.5 T and 3 T. Reproducible mass manufacture was established. The device received regulatory clearance by the Food and Drug Administration (FDA) and Conformité Européene (CE) marking. Results: The T1MES phantom is an agarose gel-based phantom using nickel chloride as the paramagnetic relaxation modifier. It was reproducibly specified and mass-produced with a rigorously repeatable process. Each phantom contains nine differently-doped agarose gel tubes embedded in a gel/beads matrix. Phantoms were free of air bubbles and susceptibility artifacts at both field strengths and T1 maps were free from off-resonance artifacts. The incorporation of high-density polyethylene beads in the main gel fill was effective at flattening the B1 field. T1 and T2 values measured in T1MES showed coefficients of variation of 1 % or less between repeat scans indicating good short-term reproducibility. Temperature dependency experiments confirmed that over the range 15-30 °C the short-T1 tubes were more stable with temperature than the long-T1 tubes. A batch of 69 phantoms was mass-produced with random sampling of ten of these showing coefficients of variations for T1 of 0.64 ± 0.45 % and 0.49 ± 0.34 % at 1.5 T and 3 T respectively. Conclusion: The T1MES program has developed a T1 mapping phantom to CE/FDA manufacturing standards. An initial 69 phantoms with a multi-vendor user manual are now being scanned fortnightly in centers worldwide. Future results will explore T1 mapping sequences, platform performance, stability and the potential for standardization. [ABSTRACT FROM AUTHOR]

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F 1 -score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility.

Background: T 1 mapping and extracellular volume (ECV) have the potential to guide patient care and serve as surrogate end-points in clinical trials, but measurements differ between cardiovascular magnetic resonance (CMR) scanners and pulse sequences. To help deliver T 1 mapping to global clinical care, we developed a phantom-based quality assurance (QA) system for verification of measurement stability over time at individual sites, with further aims of generalization of results across sites, vendor systems, software versions and imaging sequences. We thus created T1MES: The T1 Mapping and ECV Standardization Program., Methods: A design collaboration consisting of a specialist MRI small-medium enterprise, clinicians, physicists and national metrology institutes was formed. A phantom was designed covering clinically relevant ranges of T 1 and T 2 in blood and myocardium, pre and post-contrast, for 1.5 T and 3 T. Reproducible mass manufacture was established. The device received regulatory clearance by the Food and Drug Administration (FDA) and Conformité Européene (CE) marking., Results: The T1MES phantom is an agarose gel-based phantom using nickel chloride as the paramagnetic relaxation modifier. It was reproducibly specified and mass-produced with a rigorously repeatable process. Each phantom contains nine differently-doped agarose gel tubes embedded in a gel/beads matrix. Phantoms were free of air bubbles and susceptibility artifacts at both field strengths and T 1 maps were free from off-resonance artifacts. The incorporation of high-density polyethylene beads in the main gel fill was effective at flattening the B 1 field. T 1 and T 2 values measured in T1MES showed coefficients of variation of 1 % or less between repeat scans indicating good short-term reproducibility. Temperature dependency experiments confirmed that over the range 15-30 °C the short-T 1 tubes were more stable with temperature than the long-T 1 tubes. A batch of 69 phantoms was mass-produced with random sampling of ten of these showing coefficients of variations for T 1 of 0.64 ± 0.45 % and 0.49 ± 0.34 % at 1.5 T and 3 T respectively., Conclusion: The T1MES program has developed a T 1 mapping phantom to CE/FDA manufacturing standards. An initial 69 phantoms with a multi-vendor user manual are now being scanned fortnightly in centers worldwide. Future results will explore T 1 mapping sequences, platform performance, stability and the potential for standardization.