DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Dong Kyun | ko |
dc.contributor.author | Lee, So-Yeon | ko |
dc.contributor.author | Lee, Jinyoung | ko |
dc.contributor.author | Huh, Yeon Jong | ko |
dc.contributor.author | Lee, Seungeun | ko |
dc.contributor.author | Lee, Sungwon | ko |
dc.contributor.author | Jung, Joon-Yong | ko |
dc.contributor.author | Lee, Hyun-Soo | ko |
dc.contributor.author | Benkert, Thomas | ko |
dc.contributor.author | Park, Sung-Hong | ko |
dc.date.accessioned | 2024-07-01T09:00:18Z | - |
dc.date.available | 2024-07-01T09:00:18Z | - |
dc.date.created | 2024-06-25 | - |
dc.date.created | 2024-06-25 | - |
dc.date.issued | 2024-01 | - |
dc.identifier.citation | MAGNETIC RESONANCE IMAGING, v.105, pp.82 - 91 | - |
dc.identifier.issn | 0730-725X | - |
dc.identifier.uri | http://hdl.handle.net/10203/320095 | - |
dc.description.abstract | Purpose: To assess the feasibility of deep learning (DL)-based k-space-to-image reconstruction and super resolution for whole-spine diffusion-weighted imaging (DWI). Method: This retrospective study included 97 consecutive patients with hematologic and/or oncologic diseases who underwent DL-processed whole-spine MRI from July 2022 to March 2023. For each patient, conventional (CONV) axial single-shot echo-planar DWI (b = 50, 800 s/mm2) was performed, followed by DL reconstruction and super resolution processing. The presence of malignant lesions and qualitative (overall image quality and diagnostic confidence) and quantitative (nonuniformity [NU], lesion contrast, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and ADC values) parameters were assessed for DL and CONV DWI. Results: Ultimately, 67 patients (mean age, 63.0 years; 35 females) were analyzed. The proportions of vertebrae with malignant lesions for both protocols were not significantly different (P: [0.55-0.99]). The overall image quality and diagnostic confidence scores were higher for DL DWI (all P <= 0.002) than CONV DWI. The NU, lesion contrast, SNR, and CNR of each vertebral segment (P <= 0.04) but not the NU of the sacral segment (P = 0.51) showed significant differences between protocols. For DL DWI, the NU was lower, and lesion contrast, SNR, and CNR were higher than those of CONV DWI (median values of all segments; 19.8 vs. 22.2, 5.4 vs. 4.3, 7.3 vs. 5.5, and 0.8 vs. 0.7). Mean ADC values of the lesions did not significantly differ between the protocols (P: [0.16-0.89]). Conclusions: DL reconstruction can improve the image quality of whole-spine diffusion imaging. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.title | Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI | - |
dc.type | Article | - |
dc.identifier.wosid | 001165211800001 | - |
dc.identifier.scopusid | 2-s2.0-85176765919 | - |
dc.type.rims | ART | - |
dc.citation.volume | 105 | - |
dc.citation.beginningpage | 82 | - |
dc.citation.endingpage | 91 | - |
dc.citation.publicationname | MAGNETIC RESONANCE IMAGING | - |
dc.identifier.doi | 10.1016/j.mri.2023.11.003 | - |
dc.contributor.localauthor | Park, Sung-Hong | - |
dc.contributor.nonIdAuthor | Kim, Dong Kyun | - |
dc.contributor.nonIdAuthor | Lee, So-Yeon | - |
dc.contributor.nonIdAuthor | Lee, Jinyoung | - |
dc.contributor.nonIdAuthor | Huh, Yeon Jong | - |
dc.contributor.nonIdAuthor | Lee, Seungeun | - |
dc.contributor.nonIdAuthor | Lee, Sungwon | - |
dc.contributor.nonIdAuthor | Jung, Joon-Yong | - |
dc.contributor.nonIdAuthor | Lee, Hyun-Soo | - |
dc.contributor.nonIdAuthor | Benkert, Thomas | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Whole spine | - |
dc.subject.keywordAuthor | Diffusion-weighted imaging | - |
dc.subject.keywordAuthor | Echo planar imaging | - |
dc.subject.keywordAuthor | Image reconstruction | - |
dc.subject.keywordPlus | ARTIFACTS | - |
dc.subject.keywordPlus | CONTRAST | - |
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