Ning Ding, Xi-Ao Yang, Min Xu, Yun Wang, Zhengyu Jin, Yining Wang, Huadan Xue, Lingyan Kong, Zhiwei Wang, Daming Zhang
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Quantitative parameters of aortic wall image quality were evaluated.</p><p><strong>Results: </strong>Compared to the arterial phase image sets, decreased image noise and increased signal-noise-ratio (SNR) and CNR<sub>outer</sub> (all p < 0.05) were obtained for the DB image sets. Compared with delayed-phase image sets, dark-blood image sets combined with the DLR algorithm revealed equivalent noise (p > 0.99) and increased SNR (p < 0.001), CNR<sub>outer</sub> (p = 0.006), and CNR<sub>inner</sub> (p < 0.001). For overall image quality, the scores of DB image sets were significantly higher than those of delayed-phase image sets (all p < 0.001). Image sets obtained using the DLR algorithm received significantly better qualitative scores (all p < 0.05) in all three phases. The image quality improvement caused by the DLR algorithm was most prominent for the DB phase image sets.</p><p><strong>Conclusion: </strong>DB CTA improves image quality and provides better visualization of the aorta for the LVV aorta vessel wall. The DB technique reconstructed by the DLR algorithm achieved the best overall performance compared with the other image sequences.</p><p><strong>Critical relevance statement: </strong>Deep-learning-based \"dark blood\" images improve vessel wall image wall quality and boundary visualization.</p><p><strong>Key points: </strong>Dark blood CTA improves image quality and provides better aortic wall visualization. Deep-learning CTA presented higher quality and subjective scores compared to HIR. 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引用次数: 0
摘要
目的评估 "暗血"（DB）技术、深度学习重建（DLR）及其组合在大血管炎（LVV）患者主动脉图像上的表现：在一个中心前瞻性地招募了 50 名确诊为大血管炎的患者，计划对他们进行主动脉计算机断层扫描（CTA）。使用混合迭代重建（HIR）和 DLR 算法重建主动脉的动脉和延迟相图像。HIR 或 DLR DB 图像集是基于 "对比度增强增强 "技术，使用相应的动脉和延迟相图像集生成的。对主动脉壁图像质量的定量参数进行了评估：与动脉相位图像集相比，图像噪声降低，信噪比（SNR）和 CNRouter（均为 p 0.99）提高，SNR（p outer（p = 0.006））和 CNRinner（p 结论：DB CTA 改善了图像质量，并提高了主动脉壁的成像质量：DB CTA 提高了图像质量，并能更好地显示 LVV 主动脉血管壁。与其他图像序列相比，采用 DLR 算法重建的 DB 技术取得了最佳的整体性能：基于深度学习的 "暗血 "图像可改善血管壁图像质量和边界可视化：深色血液 CTA 可改善图像质量，提供更好的主动脉壁可视化。与 HIR 相比，深度学习 CTA 的质量和主观评分更高。深色血液与深度学习重建的结合获得了最佳的整体性能。
Large vessel vasculitis evaluation by CTA: impact of deep-learning reconstruction and "dark blood" technique.
Objectives: To assess the performance of the "dark blood" (DB) technique, deep-learning reconstruction (DLR), and their combination on aortic images for large-vessel vasculitis (LVV) patients.
Materials and methods: Fifty patients diagnosed with LVV scheduled for aortic computed tomography angiography (CTA) were prospectively recruited in a single center. Arterial and delayed-phase images of the aorta were reconstructed using the hybrid iterative reconstruction (HIR) and DLR algorithms. HIR or DLR DB image sets were generated using corresponding arterial and delayed-phase image sets based on a "contrast-enhancement-boost" technique. Quantitative parameters of aortic wall image quality were evaluated.
Results: Compared to the arterial phase image sets, decreased image noise and increased signal-noise-ratio (SNR) and CNRouter (all p < 0.05) were obtained for the DB image sets. Compared with delayed-phase image sets, dark-blood image sets combined with the DLR algorithm revealed equivalent noise (p > 0.99) and increased SNR (p < 0.001), CNRouter (p = 0.006), and CNRinner (p < 0.001). For overall image quality, the scores of DB image sets were significantly higher than those of delayed-phase image sets (all p < 0.001). Image sets obtained using the DLR algorithm received significantly better qualitative scores (all p < 0.05) in all three phases. The image quality improvement caused by the DLR algorithm was most prominent for the DB phase image sets.
Conclusion: DB CTA improves image quality and provides better visualization of the aorta for the LVV aorta vessel wall. The DB technique reconstructed by the DLR algorithm achieved the best overall performance compared with the other image sequences.
Key points: Dark blood CTA improves image quality and provides better aortic wall visualization. Deep-learning CTA presented higher quality and subjective scores compared to HIR. Combination of dark blood and deep-learning reconstruction obtained the best overall performance.
期刊介绍：
Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere!
I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe.
Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy.
A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field.
I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly.
The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members.
The journal went open access in 2012, which means that all articles published since then are freely available online.
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