Ультрасонография черепа и скальпа у детей: обзор

INTRODUCTION: An important task of modern pediatrics is to ensure radiation safety of diagnostic examinations, especially in young children. One of the options for reducing radiation exposure at the stages of screening diagnostics and dynamic monitoring is a wider use of ultrasound.OBJECTIVE: To analyze the data of domestic and foreign literature on the possibilities of ultrasound examination of the cranial vault bones, cranial sutures and scalp in children.MATERIALS AND METHODS: The literature search was performed in open Russian and English databases Medline, PubMed, Web of Science, RSCI, eLIBRARY using keywords and phrases: «skull ultrasound», «scalp ultrasound», «cranial sutures ultrasound», «point of care ultrasound», «pediatric POCUS» without limitation of retrospective depth.RESULTS: Based on the literature data and our own long-term experience in the use of cranial ultrasonography in clinical practice, the indications and examination technique, as well as the key ultrasound signs of the most frequent types of pathology are described. Prospects of scalp and skull ultrasonography within PoCUS, FAST, including the use of portable sonoscopes based on smartphones and tablets are outlined.CONCLUSION: Ultrasound of the skull and scalp is a quick, simple, affordable, harmless method of screening and monitoring the most frequent types of pathologies of the cranial vault bones, cranial sutures, and soft tissues of the scalp in children (for example, fractures, synostoses, neoplasms).ВВЕДЕНИЕ: Важная задача современной педиатрии — обеспечение лучевой безопасности диагностических исследований, особенно у детей младших возрастных групп. Одним из вариантов снижения лучевой нагрузки на этапах скрининг-диагностики и динамического наблюдения является более широкое применение ультрасонографии.ЦЕЛЬ: Проанализировать данные отечественной и зарубежной литературы, посвященной возможностям ультразвукового исследования костей свода черепа, черепных швов и скальпа у детей.МАТЕРИАЛЫ И МЕТОДЫ: Поиск литературы осуществлялся в открытых информационных базах на русском и английском языке Medline, PubMed, Web of Science, РИНЦ, еLIBRARY по ключевым словам и словосочетаниям: «ультрасонография черепа», «ультрасонография скальпа», «ультрасонография черепных швов», «skull ultrasound», «scalp ultrasound», «cranial sutures ultrasound», «point of care ultrasound», «pediatric POCUS» без ограничения глубины ретроспекции.РЕЗУЛЬТАТЫ: На основании данных литературы и собственного многолетнего опыта применения ультрасонографии черепа в клинической практике описаны показания и методика проведения исследования, а также ключевые ультразвуковые признаки наиболее частых видов патологии. Обозначены перспективы ультрасонографии скальпа и  черепа в  рамках PoCUS, FAST, в том числе с применением портативных соноскопов, основанных на смартфонах и планшетах.ЗАКЛЮЧЕНИЕ: Ультрасонография черепа и  скальпа  — быстрый, простой, доступный, безвредный метод скрининга и мониторинга наиболее частых видов патологии костей свода черепа, черепных швов и мягких тканей волосистой части головы у детей (например, переломов, синостозов, новообразований)

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Automated estimation of link quality for Lora: A remote sensing approach

Many research and industrial communities are betting on LoRa to provide reliable, long-range communication for the Internet of Things. This new radio technology, however, provides widely heterogeneous coverage; a LoRa link may span hundreds of meters or tens of kilometers, depending on the surrounding environment. This high variability is not captured by popular channel models for LoRa, and on-site measurementsÐa common alternativeÐare impractical due to the large geographical areas involved. We propose a novel, automated approach to estimate the coverage of LoRa gateways prior to deployment and without on-site measurements. We achieve this goal by combining free, readily-available multispectral images from remote sensing with the right channel model. Our processing toolchain automatically classifies the type of environment (e.g., buildings, trees, or open fields) traversed by a link, with high accuracy (&gt;90%) and spatial resolution (10×10m2). We use this information to explain the attenuation observed in experiments. As signal attenuation is not well captured by popular channel models, we focus on the Okumura-Hata empirical model, hitherto largely unexplored for LoRa, and show that i) it yields estimates very close to our observations, and ii) we can use our toolchain to automatically select and configure its parameters. A validation on 8,000+ samples from a real dataset shows that our automated approach predicts the expected signal power within a ∼10dBm error, against the 20ś40dBm of popular channel models.</p

Automated estimation of link quality for Lora: A remote sensing approach

Many research and industrial communities are betting on LoRa to provide reliable, long-range communication for the Internet of Things. This new radio technology, however, provides widely heterogeneous coverage; a LoRa link may span hundreds of meters or tens of kilometers, depending on the surrounding environment. This high variability is not captured by popular channel models for LoRa, and on-site measurementsÐa common alternativeÐare impractical due to the large geographical areas involved. We propose a novel, automated approach to estimate the coverage of LoRa gateways prior to deployment and without on-site measurements. We achieve this goal by combining free, readily-available multispectral images from remote sensing with the right channel model. Our processing toolchain automatically classifies the type of environment (e.g., buildings, trees, or open fields) traversed by a link, with high accuracy (&gt;90%) and spatial resolution (10×10m2). We use this information to explain the attenuation observed in experiments. As signal attenuation is not well captured by popular channel models, we focus on the Okumura-Hata empirical model, hitherto largely unexplored for LoRa, and show that i) it yields estimates very close to our observations, and ii) we can use our toolchain to automatically select and configure its parameters. A validation on 8,000+ samples from a real dataset shows that our automated approach predicts the expected signal power within a ∼10dBm error, against the 20ś40dBm of popular channel models.Embedded and Networked System