{"title":"深度学习重建技术在3特斯拉机器高分辨率非对比磁共振冠状动脉造影中的效果。","authors":"Yasuhiro Yokota, Chika Takeda, Masafumi Kidoh, Seitaro Oda, Ryo Aoki, Kenichi Ito, Kosuke Morita, Kentaro Haraoka, Yuichi Yamashita, Hitoshi Iizuka, Shingo Kato, Kenichi Tsujita, Osamu Ikeda, Yasuyuki Yamashita, Daisuke Utsunomiya","doi":"10.1177/0846537119900469","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the effects of deep learning reconstruction (DLR) in qualitative and quantitative image quality of non-contrast magnetic resonance coronary angiography (MRCA).</p><p><strong>Methods: </strong>Ten healthy volunteers underwent conventional MRCA (C-MRCA) and high-resolution (HR) MRCA on a 3T magnetic resonance imaging with a voxel size of 1.8 × 1.1 × 1.7 mm<sup>3</sup> and 1.8 × 0.6 × 1.0 mm<sup>3</sup>, respectively, for C-MRCA and HR-MRCA. High-resolution magnetic resonance coronary angiography was also reconstructed with the DLR technique (DLR-HR-MRCA). We compared the contrast-to-noise ratio (CNR) and visual evaluation scores for vessel sharpness and traceability of proximal and distal coronary vessels on a 4-point scale among 3 image series.</p><p><strong>Results: </strong>The vascular CNR value on the C-MRCA and the DLR-HR-MRCA was significantly higher than that on the HR-MRCA in the proximal and distal coronary arteries (13.9 ± 6.4, 11.3 ± 4.4, and 7.8 ± 2.6 for C-MRCA, DLR-HR-MRCA, and HR-MRCA, <i>P</i> < .05, respectively). Mean visual evaluation scores for the vessel sharpness and traceability of proximal and distal coronary vessels were significantly higher on the HR-DLR-MRCA than the C-MRCA (<i>P</i> < .05, respectively).</p><p><strong>Conclusion: </strong>Deep learning reconstruction significantly improved the CNR of coronary arteries on HR-MRCA, resulting in both higher visual image quality and better vessel traceability compared with C-MRCA.</p>","PeriodicalId":444006,"journal":{"name":"Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes","volume":" ","pages":"120-127"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0846537119900469","citationCount":"19","resultStr":"{\"title\":\"Effects of Deep Learning Reconstruction Technique in High-Resolution Non-contrast Magnetic Resonance Coronary Angiography at a 3-Tesla Machine.\",\"authors\":\"Yasuhiro Yokota, Chika Takeda, Masafumi Kidoh, Seitaro Oda, Ryo Aoki, Kenichi Ito, Kosuke Morita, Kentaro Haraoka, Yuichi Yamashita, Hitoshi Iizuka, Shingo Kato, Kenichi Tsujita, Osamu Ikeda, Yasuyuki Yamashita, Daisuke Utsunomiya\",\"doi\":\"10.1177/0846537119900469\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To evaluate the effects of deep learning reconstruction (DLR) in qualitative and quantitative image quality of non-contrast magnetic resonance coronary angiography (MRCA).</p><p><strong>Methods: </strong>Ten healthy volunteers underwent conventional MRCA (C-MRCA) and high-resolution (HR) MRCA on a 3T magnetic resonance imaging with a voxel size of 1.8 × 1.1 × 1.7 mm<sup>3</sup> and 1.8 × 0.6 × 1.0 mm<sup>3</sup>, respectively, for C-MRCA and HR-MRCA. High-resolution magnetic resonance coronary angiography was also reconstructed with the DLR technique (DLR-HR-MRCA). We compared the contrast-to-noise ratio (CNR) and visual evaluation scores for vessel sharpness and traceability of proximal and distal coronary vessels on a 4-point scale among 3 image series.</p><p><strong>Results: </strong>The vascular CNR value on the C-MRCA and the DLR-HR-MRCA was significantly higher than that on the HR-MRCA in the proximal and distal coronary arteries (13.9 ± 6.4, 11.3 ± 4.4, and 7.8 ± 2.6 for C-MRCA, DLR-HR-MRCA, and HR-MRCA, <i>P</i> < .05, respectively). Mean visual evaluation scores for the vessel sharpness and traceability of proximal and distal coronary vessels were significantly higher on the HR-DLR-MRCA than the C-MRCA (<i>P</i> < .05, respectively).</p><p><strong>Conclusion: </strong>Deep learning reconstruction significantly improved the CNR of coronary arteries on HR-MRCA, resulting in both higher visual image quality and better vessel traceability compared with C-MRCA.</p>\",\"PeriodicalId\":444006,\"journal\":{\"name\":\"Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes\",\"volume\":\" \",\"pages\":\"120-127\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1177/0846537119900469\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/0846537119900469\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2020/2/19 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/0846537119900469","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/2/19 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Effects of Deep Learning Reconstruction Technique in High-Resolution Non-contrast Magnetic Resonance Coronary Angiography at a 3-Tesla Machine.
Purpose: To evaluate the effects of deep learning reconstruction (DLR) in qualitative and quantitative image quality of non-contrast magnetic resonance coronary angiography (MRCA).
Methods: Ten healthy volunteers underwent conventional MRCA (C-MRCA) and high-resolution (HR) MRCA on a 3T magnetic resonance imaging with a voxel size of 1.8 × 1.1 × 1.7 mm3 and 1.8 × 0.6 × 1.0 mm3, respectively, for C-MRCA and HR-MRCA. High-resolution magnetic resonance coronary angiography was also reconstructed with the DLR technique (DLR-HR-MRCA). We compared the contrast-to-noise ratio (CNR) and visual evaluation scores for vessel sharpness and traceability of proximal and distal coronary vessels on a 4-point scale among 3 image series.
Results: The vascular CNR value on the C-MRCA and the DLR-HR-MRCA was significantly higher than that on the HR-MRCA in the proximal and distal coronary arteries (13.9 ± 6.4, 11.3 ± 4.4, and 7.8 ± 2.6 for C-MRCA, DLR-HR-MRCA, and HR-MRCA, P < .05, respectively). Mean visual evaluation scores for the vessel sharpness and traceability of proximal and distal coronary vessels were significantly higher on the HR-DLR-MRCA than the C-MRCA (P < .05, respectively).
Conclusion: Deep learning reconstruction significantly improved the CNR of coronary arteries on HR-MRCA, resulting in both higher visual image quality and better vessel traceability compared with C-MRCA.
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