D-ConvNet: Deep learning model for enhancement of brain MR images
| Parent link: | Basic and Clinical Pharmacology and Toxicology Vol. 124, iss. S2.— 2019.— [P. 3-4] |
|---|---|
| מחבר תאגידי: | |
| מחברים אחרים: | , , , |
| סיכום: | 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 |
| שפה: | אנגלית |
| יצא לאור: |
2019
|
| נושאים: | |
| גישה מקוונת: | https://doi.org/10.1111/bcpt.13173 |
| פורמט: | אלקטרוני Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663948 |
| סיכום: | 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 |
|---|---|
| DOI: | 10.1111/bcpt.13173 |