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本文引用的文献

1
ACC/AHA/ASE/ASNC/ASPC/HFSA/HRS/SCAI/SCCT/SCMR/STS 2023 Multimodality Appropriate Use Criteria for the Detection and Risk Assessment of Chronic Coronary Disease.美国心脏病学会/美国心脏协会/美国超声心动图学会/美国核医学学会/美国预防心脏病学会/美国心力衰竭学会/美国心律学会/心血管造影和介入学会/心血管计算机断层扫描学会/心血管磁共振学会/胸外科医师学会2023年慢性冠状动脉疾病检测与风险评估的多模态合理使用标准
J Am Coll Cardiol. 2023 Jun 27;81(25):2445-2467. doi: 10.1016/j.jacc.2023.03.410. Epub 2023 May 25.
2
Correlation of pericoronary adipose tissue CT attenuation values of plaques and periplaques with plaque characteristics.冠状动脉脂肪组织 CT 衰减值与斑块和斑块周围特征的相关性。
Clin Radiol. 2023 Sep;78(9):e591-e599. doi: 10.1016/j.crad.2023.04.007. Epub 2023 May 6.
3
Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography.深度学习图像重建算法:对冠状动脉 CT 血管造影图像质量的影响。
Radiol Med. 2023 Apr;128(4):434-444. doi: 10.1007/s11547-023-01607-8. Epub 2023 Feb 27.
4
Clinical effectiveness of contrast medium injection protocols for 80-kV coronary and craniocervical CT angiography-a prospective multicenter observational study.80kV 千伏冠状动脉和颅颈 CT 血管造影对比剂注射方案的临床效果——一项前瞻性多中心观察研究。
Eur Radiol. 2022 Jun;32(6):3808-3818. doi: 10.1007/s00330-021-08505-5. Epub 2022 Feb 1.
5
Assessment of Image Quality of Coronary Computed Tomography Angiography in Obese Patients by Comparing Deep Learning Image Reconstruction With Adaptive Statistical Iterative Reconstruction Veo.比较深度学习图像重建与自适应统计迭代重建 Veo 对肥胖患者冠状动脉 CT 血管造影图像质量的评估。
J Comput Assist Tomogr. 2022;46(1):34-40. doi: 10.1097/RCT.0000000000001252.
6
High-strength deep learning image reconstruction in coronary CT angiography at 70-kVp tube voltage significantly improves image quality and reduces both radiation and contrast doses.在70千伏管电压下进行冠状动脉CT血管造影的高强度深度学习图像重建可显著提高图像质量,并减少辐射剂量和造影剂剂量。
Eur Radiol. 2022 May;32(5):2912-2920. doi: 10.1007/s00330-021-08424-5. Epub 2022 Jan 21.
7
A deep-learning reconstruction algorithm that improves the image quality of low-tube-voltage coronary CT angiography.一种深度学习重建算法,可提高低管电压冠状动脉 CT 血管造影的图像质量。
Eur J Radiol. 2022 Jan;146:110070. doi: 10.1016/j.ejrad.2021.110070. Epub 2021 Nov 24.
8
Potential increase in radiation-induced DNA double-strand breaks with higher doses of iodine contrast during coronary CT angiography.在冠状动脉 CT 血管造影术期间,碘对比剂剂量增加可能会导致辐射诱导的 DNA 双链断裂增加。
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深度学习图像重建对图像质量和冠状动脉周围脂肪衰减指数的影响。

Effect of Deep Learning Image Reconstruction on Image Quality and Pericoronary Fat Attenuation Index.

作者信息

Mei Junqing, Chen Chang, Liu Ruoting, Ma Hongbing

机构信息

Department of Radiology, BenQ Hospital, Nanjing Medical University, Nanjing, Jiangsu Province, China.

出版信息

J Imaging Inform Med. 2025 Jun;38(3):1881-1890. doi: 10.1007/s10278-024-01234-3. Epub 2024 Sep 19.

DOI:10.1007/s10278-024-01234-3
PMID:39299956
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12092906/
Abstract

To compare the image quality and fat attenuation index (FAI) of coronary artery CT angiography (CCTA) under different tube voltages between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASIR-V). Three hundred one patients who underwent CCTA with automatic tube current modulation were prospectively enrolled and divided into two groups: 120 kV group and low tube voltage group. Images were reconstructed using ASIR-V level 50% (ASIR-V50%) and high-strength DLIR (DLIR-H). In the low tube voltage group, the voltage was selected according to Chinese BMI classification: 70 kV (BMI < 24 kg/m), 80 kV (24 kg/m ≤ BMI < 28 kg/m), 100 kV (BMI ≥ 28 kg/m). At the same tube voltage, the subjective and objective image quality, edge rise distance (ERD), and FAI between different algorithms were compared. Under different tube voltages, we used DLIR-H to compare the differences between subjective, objective image quality, and ERD. Compared with the 120 kV group, the DLIR-H image noise of 70 kV, 80 kV, and 100 kV groups increased by 36%, 25%, and 12%, respectively (all P < 0.001); contrast-to-noise ratio (CNR), subjective score, and ERD were similar (all P > 0.05). In the 70 kV, 80 kV, 100 kV, and 120 kV groups, compared with ASIR-V50%, DLIR-H image noise decreased by 50%, 53%, 47%, and 38-50%, respectively; CNR, subjective score, and FAI value increased significantly (all P < 0.001), ERD decreased. Compared with 120 kV tube voltage, the combination of DLIR-H and low tube voltage maintains image quality. At the same tube voltage, compared with ASIR-V, DLIR-H improves image quality and FAI value.

摘要

比较深度学习图像重建(DLIR)与自适应统计迭代重建V(ASIR-V)在不同管电压下冠状动脉CT血管造影(CCTA)的图像质量和脂肪衰减指数(FAI)。前瞻性纳入301例行CCTA并采用自动管电流调制的患者,分为两组:120 kV组和低管电压组。图像采用50%水平的ASIR-V(ASIR-V50%)和高强度DLIR(DLIR-H)进行重建。在低管电压组中,根据中国BMI分类选择电压:70 kV(BMI<24 kg/m²)、80 kV(24 kg/m²≤BMI<28 kg/m²)、100 kV(BMI≥28 kg/m²)。在相同管电压下,比较不同算法之间的主观和客观图像质量、边缘上升距离(ERD)和FAI。在不同管电压下,使用DLIR-H比较主观、客观图像质量和ERD之间的差异。与120 kV组相比,70 kV、80 kV和100 kV组的DLIR-H图像噪声分别增加了36%、25%和12%(均P<0.001);对比噪声比(CNR)、主观评分和ERD相似(均P>0.05)。在70 kV、80 kV、100 kV和120 kV组中,与ASIR-V50%相比,DLIR-H图像噪声分别降低了50%、53%、47%和38%-50%;CNR、主观评分和FAI值显著增加(均P<0.001),ERD降低。与120 kV管电压相比,DLIR-H与低管电压相结合可维持图像质量。在相同管电压下,与ASIR-V相比,DLIR-H可提高图像质量和FAI值。