@inproceedings{d058d924bfa44e828d1258cd695aaf3d,
title = "Poster Abstract: The Concept of a Lightweight Ultrasound Tomograph for Brain Scanning Using a Heterogeneous Neural Model",
abstract = "The primary objective of the research is the development of a lightweight and cost-effective headband-style tomographic apparatus capable of non-invasively capturing real-time internal cerebral images. A prototype of an ultrasonic tomograph was engineered, comprising a lightweight cranial band synergized with ultrasonic transducers and the tomographic system. Ultrasonic measurements were transmuted into visualizations via a heterogeneous convolutional neural network (CNN). The Ultrasonic Computed Tomography (USCT) architecture was conceived to facilitate untethered data interchange between the head-worn sensor array and the tomographic machinery.",
keywords = "brain imaging, brain phantom, deep learning, speed of sound imaging, ultrasound tomography",
author = "Grzegorz K{\l}osowski and Tomasz Rymarczyk and Manuchehr Soleimani",
year = "2023",
month = nov,
day = "15",
doi = "10.1145/3625687.3628380",
language = "English",
series = "SenSys 2023 - Proceedings of the 21st ACM Conference on Embedded Networked Sensors Systems",
publisher = "Association for Computing Machinery",
pages = "506--507",
booktitle = "SenSys 2023 - Proceedings of the 21st ACM Conference on Embedded Networked Sensors Systems",
address = "USA United States",
note = "21st ACM Conference on Embedded Networked Sensors Systems, SenSys 2023 ; Conference date: 13-11-2023 Through 15-11-2023",
}