Resolution Enhancement of Brain MRI Images Using Deep Learning
One of the most widely used imaging techniques in medicine is magnetic resonance imaging (MRI). It is a tool that doctors use to comprehend human anatomy and carry out more accurate analyses. In the study of brain anatomy, image processing super resolution technology has become important to overcome...
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MDPI AG
2024-01-01
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| author | Minakshi Roy Biraj Upadhyaya Jyoti Rai Kalpana Sharma |
| author_facet | Minakshi Roy Biraj Upadhyaya Jyoti Rai Kalpana Sharma |
| author_sort | Minakshi Roy |
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| description | One of the most widely used imaging techniques in medicine is magnetic resonance imaging (MRI). It is a tool that doctors use to comprehend human anatomy and carry out more accurate analyses. In the study of brain anatomy, image processing super resolution technology has become important to overcome physical restrictions due to image deterioration caused by hardware constraints, lengthier scanning periods, and artefacts. Super resolution is an approach to raise an image’s resolution while improving the image’s quality from a low-resolution (LR) image to a higher-resolution (HR) image. The study provides an overview of deep learning techniques for creating super-resolution (SR) MRI brain images. A widely used deep learning (DL) technique, accessible brain MRI dataset, and quantity evaluation matrices have been presented, mostly used for image super resolution. Factors affecting hardware constraints and artifacts, including magnetic field homogeneity, gradient nonlinearity, radiofrequency (RF) coil sensitivity, signal-to-noise ratio (SNR), and gradient coil performance, have been taken into account. This research focuses mostly on brain MRI images as a contribution to the medical industry for super resolution. |
| format | Article |
| id | doaj-art-033bcab4f4fa483c90e97a06139efebf |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-033bcab4f4fa483c90e97a06139efebf2025-08-20T02:11:05ZengMDPI AGEngineering Proceedings2673-45912024-01-0159115810.3390/engproc2023059158Resolution Enhancement of Brain MRI Images Using Deep LearningMinakshi Roy0Biraj Upadhyaya1Jyoti Rai2Kalpana Sharma3Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok 737136, IndiaDepartment of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok 737136, IndiaDepartment of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok 737136, IndiaDepartment of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok 737136, IndiaOne of the most widely used imaging techniques in medicine is magnetic resonance imaging (MRI). It is a tool that doctors use to comprehend human anatomy and carry out more accurate analyses. In the study of brain anatomy, image processing super resolution technology has become important to overcome physical restrictions due to image deterioration caused by hardware constraints, lengthier scanning periods, and artefacts. Super resolution is an approach to raise an image’s resolution while improving the image’s quality from a low-resolution (LR) image to a higher-resolution (HR) image. The study provides an overview of deep learning techniques for creating super-resolution (SR) MRI brain images. A widely used deep learning (DL) technique, accessible brain MRI dataset, and quantity evaluation matrices have been presented, mostly used for image super resolution. Factors affecting hardware constraints and artifacts, including magnetic field homogeneity, gradient nonlinearity, radiofrequency (RF) coil sensitivity, signal-to-noise ratio (SNR), and gradient coil performance, have been taken into account. This research focuses mostly on brain MRI images as a contribution to the medical industry for super resolution.https://www.mdpi.com/2673-4591/59/1/158super resolutionMRI imagesresolution enhancementdeep learning |
| spellingShingle | Minakshi Roy Biraj Upadhyaya Jyoti Rai Kalpana Sharma Resolution Enhancement of Brain MRI Images Using Deep Learning Engineering Proceedings super resolution MRI images resolution enhancement deep learning |
| title | Resolution Enhancement of Brain MRI Images Using Deep Learning |
| title_full | Resolution Enhancement of Brain MRI Images Using Deep Learning |
| title_fullStr | Resolution Enhancement of Brain MRI Images Using Deep Learning |
| title_full_unstemmed | Resolution Enhancement of Brain MRI Images Using Deep Learning |
| title_short | Resolution Enhancement of Brain MRI Images Using Deep Learning |
| title_sort | resolution enhancement of brain mri images using deep learning |
| topic | super resolution MRI images resolution enhancement deep learning |
| url | https://www.mdpi.com/2673-4591/59/1/158 |
| work_keys_str_mv | AT minakshiroy resolutionenhancementofbrainmriimagesusingdeeplearning AT birajupadhyaya resolutionenhancementofbrainmriimagesusingdeeplearning AT jyotirai resolutionenhancementofbrainmriimagesusingdeeplearning AT kalpanasharma resolutionenhancementofbrainmriimagesusingdeeplearning |