D-ConvNet: Deep learning model for enhancement of brain MR images

D-ConvNet: Deep learning model for enhancement of brain MR images

Dettagli Bibliografici
Parent link:Basic and Clinical Pharmacology and Toxicology
Vol. 124, iss. S2.— 2019.— [P. 3-4]
Ente Autore: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Научно-образовательный центр "Автоматизация и информационные технологии"
Altri autori: Srinivasan К. Kathiravan, Sharma V. Vishal, Dzhayakodi (Jayakody) Arachshiladzh D. N. K. Dushanta Nalin Kumara, Vincent D. R. Durai Raj
Riassunto:Title screen
In clinical and medical imaging, the magnetic resonance (MR) images obtained, generally do not have a very high resolution because of the several factors like patient's comfort, scanning time, scanning equipment limitations, long sampling times, and so on. However, the MR imaging is always favored by physicians as one of the most trusted modes for clinical pathology, disease diagnosis, and treatment. Therefore, the enhancement of low-resolution MR image to a high-resolution MR image is critical for precise and effective clinical diagnosis. Furthermore, single-image super-resolution is an inverse problem because of its ill-posed characteristics. This problem can be surpassed by using deep learning models such as deep convolutional neural networks (D-ConvNet).
Режим доступа: по договору с организацией-держателем ресурса
Lingua:inglese
Pubblicazione: 2019
Soggetti:
электронный ресурс
труды учёных ТПУ
МРТ
диагностика
патологии
заболевания
Accesso online:https://doi.org/10.1111/bcpt.13173
Natura: Elettronico Capitolo di libro
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663948

Documenti analoghi

Documenti analoghi