Cited 4 times in
Synthesis of T2-weighted images from proton density images using a generative adversarial network in a temporomandibular joint magnetic resonance imaging protocol
DC Field | Value | Language |
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dc.contributor.author | 이채나 | - |
dc.contributor.author | 전국진 | - |
dc.contributor.author | 최윤주 | - |
dc.contributor.author | 한상선 | - |
dc.date.accessioned | 2023-03-10T01:37:14Z | - |
dc.date.available | 2023-03-10T01:37:14Z | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 2233-7822 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/193262 | - |
dc.description.abstract | Purpose: This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint (TMJ) magnetic resonance imaging (MRI) protocol. Materials and methods: From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen's ĸ coefficient. Results: The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement (ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement (ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion. Conclusion: The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Korean Academy of Oral and Maxillofacial Radiology | - |
dc.relation.isPartOf | IMAGING SCIENCE IN DENTISTRY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Synthesis of T2-weighted images from proton density images using a generative adversarial network in a temporomandibular joint magnetic resonance imaging protocol | - |
dc.type | Article | - |
dc.contributor.college | College of Dentistry (치과대학) | - |
dc.contributor.department | Dept. of Oral and Maxillofacial Radiology (영상치의학교실) | - |
dc.contributor.googleauthor | Chena Lee | - |
dc.contributor.googleauthor | Eun-Gyu Ha | - |
dc.contributor.googleauthor | Yoon Joo Choi | - |
dc.contributor.googleauthor | Kug Jin Jeon | - |
dc.contributor.googleauthor | Sang-Sun Han | - |
dc.identifier.doi | 10.5624/isd.20220125 | - |
dc.contributor.localId | A05388 | - |
dc.contributor.localId | A03503 | - |
dc.contributor.localId | A05734 | - |
dc.contributor.localId | A04283 | - |
dc.relation.journalcode | J01032 | - |
dc.identifier.eissn | 2233-7830 | - |
dc.identifier.pmid | 36605858 | - |
dc.subject.keyword | Artificial Intelligence | - |
dc.subject.keyword | Computer Neural Network | - |
dc.subject.keyword | Deep Learning | - |
dc.subject.keyword | Magnetic Resonance Imaging | - |
dc.subject.keyword | Temporomandibular Joint Disorders | - |
dc.contributor.alternativeName | Lee, Chena | - |
dc.contributor.affiliatedAuthor | 이채나 | - |
dc.contributor.affiliatedAuthor | 전국진 | - |
dc.contributor.affiliatedAuthor | 최윤주 | - |
dc.contributor.affiliatedAuthor | 한상선 | - |
dc.citation.volume | 52 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 393 | - |
dc.citation.endPage | 398 | - |
dc.identifier.bibliographicCitation | IMAGING SCIENCE IN DENTISTRY, Vol.52(4) : 393-398, 2022-12 | - |
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