D-ConvNet: Deep learning model for enhancement of brain MR images; Basic and Clinical Pharmacology and Toxicology; Vol. 124, iss. S2
| Parent link: | Basic and Clinical Pharmacology and Toxicology Vol. 124, iss. S2.— 2019.— [P. 3-4] |
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| Korporativní autor: | |
| Další autoři: | , , , |
| Shrnutí: | 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 |
| Jazyk: | angličtina |
| Vydáno: |
2019
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| Témata: | |
| On-line přístup: | https://doi.org/10.1111/bcpt.13173 |
| Médium: | Elektronický zdroj Kapitola |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663948 |
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| 200 | 1 | |a D-ConvNet: Deep learning model for enhancement of brain MR images |f К. Srinivasan, V. Sharma, D. N. K. Dzhayakodi (Jayakody) Arachshiladzh, D. R. Vincent | |
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| 300 | |a Title screen | ||
| 330 | |a 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). | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Basic and Clinical Pharmacology and Toxicology | ||
| 463 | |t Vol. 124, iss. S2 |v [P. 3-4] |d 2019 | ||
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