Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia
This study aimed to implement a deep learning-based super-resolution (SR) technique that can assist in the diagnosis and surgery of trigeminal neuralgia (TN) using magnetic resonance imaging (MRI). Experimental methods applied SR to MRI data examined using five techniques, including T2-weighted imag...
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MDPI AG
2024-03-01
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| Online Access: | https://www.mdpi.com/2075-1729/14/3/355 |
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| author | Jun Ho Hwang Chang Kyu Park Seok Bin Kang Man Kyu Choi Won Hee Lee |
| author_facet | Jun Ho Hwang Chang Kyu Park Seok Bin Kang Man Kyu Choi Won Hee Lee |
| author_sort | Jun Ho Hwang |
| collection | DOAJ |
| description | This study aimed to implement a deep learning-based super-resolution (SR) technique that can assist in the diagnosis and surgery of trigeminal neuralgia (TN) using magnetic resonance imaging (MRI). Experimental methods applied SR to MRI data examined using five techniques, including T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), contrast-enhancement T1WI (CE-T1WI), T2WI turbo spin–echo series volume isotropic turbo spin–echo acquisition (VISTA), and proton density (PD), in patients diagnosed with TN. The image quality was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). High-quality reconstructed MRI images were assessed using the Leksell coordinate system in gamma knife radiosurgery (GKRS). The results showed that the PSNR and SSIM values achieved by SR were higher than those obtained by image postprocessing techniques, and the coordinates of the images reconstructed in the gamma plan showed no differences from those of the original images. Consequently, SR demonstrated remarkable effects in improving the image quality without discrepancies in the coordinate system, confirming its potential as a useful tool for the diagnosis and surgery of TN. |
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| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-04-24T18:04:26Z |
| publishDate | 2024-03-01 |
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| spelling | doaj.art-132f70bee83b415f83f4ba9a91ad49542024-03-27T13:51:13ZengMDPI AGLife2075-17292024-03-0114335510.3390/life14030355Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal NeuralgiaJun Ho Hwang0Chang Kyu Park1Seok Bin Kang2Man Kyu Choi3Won Hee Lee4Department of Neurosurgery, Kyung Hee University Medical Center, Seoul 02447, Republic of KoreaDepartment of Neurosurgery, Kyung Hee University Medical Center, Seoul 02447, Republic of KoreaDepartment of Urology, National Police Hospital, Seoul 05715, Republic of KoreaDepartment of Neurosurgery, Kyung Hee University Medical Center, Seoul 02447, Republic of KoreaDepartment of Neurosurgery, School of Medicine, Inje University Busan Paik Hospital, 75 Bokji-ro, Busanjin-gu, Busan 47392, Republic of KoreaThis study aimed to implement a deep learning-based super-resolution (SR) technique that can assist in the diagnosis and surgery of trigeminal neuralgia (TN) using magnetic resonance imaging (MRI). Experimental methods applied SR to MRI data examined using five techniques, including T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), contrast-enhancement T1WI (CE-T1WI), T2WI turbo spin–echo series volume isotropic turbo spin–echo acquisition (VISTA), and proton density (PD), in patients diagnosed with TN. The image quality was evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). High-quality reconstructed MRI images were assessed using the Leksell coordinate system in gamma knife radiosurgery (GKRS). The results showed that the PSNR and SSIM values achieved by SR were higher than those obtained by image postprocessing techniques, and the coordinates of the images reconstructed in the gamma plan showed no differences from those of the original images. Consequently, SR demonstrated remarkable effects in improving the image quality without discrepancies in the coordinate system, confirming its potential as a useful tool for the diagnosis and surgery of TN.https://www.mdpi.com/2075-1729/14/3/355artificial intelligence (AI)deep learningsuper resolution (SR)magnetic resonance imaging (MRI)trigeminal neuralgia (TN) |
| spellingShingle | Jun Ho Hwang Chang Kyu Park Seok Bin Kang Man Kyu Choi Won Hee Lee Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia Life artificial intelligence (AI) deep learning super resolution (SR) magnetic resonance imaging (MRI) trigeminal neuralgia (TN) |
| title | Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia |
| title_full | Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia |
| title_fullStr | Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia |
| title_full_unstemmed | Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia |
| title_short | Deep Learning Super-Resolution Technique Based on Magnetic Resonance Imaging for Application of Image-Guided Diagnosis and Surgery of Trigeminal Neuralgia |
| title_sort | deep learning super resolution technique based on magnetic resonance imaging for application of image guided diagnosis and surgery of trigeminal neuralgia |
| topic | artificial intelligence (AI) deep learning super resolution (SR) magnetic resonance imaging (MRI) trigeminal neuralgia (TN) |
| url | https://www.mdpi.com/2075-1729/14/3/355 |
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