第二代冠状动脉追踪冻结技术结合深度学习图像重建算法在较高心率冠状动脉成像中的应用

摘要
图/表
参考文献
相关文章 (7)

全文: PDF (2987 KB)
输出: BibTeX | EndNote (RIS)

摘要目的探索第二代冠状动脉追踪冻结(SSF2)技术,结合深度学习图像重建算法在较高心率患者冠状动脉CT血管成像(CCTA)中的应用价值。方法回顾性纳入心率≥70次/min行CCTA检查的患者62例,CT扫描管电压为100 kV。采用双盲法对冠状动脉的图像质量进行主观评价,分为常规图像(A组)、SSF1图像(B组)和SSF2图像(C组)。结果三组图像质量评分和优秀率比较显示RCA、PDA、LAD、D、LCX、OM、PLB、RI差异均有统计学意义(P<0.05)。冠状动脉的评分为(2.85±0.72)(A组)、(3.38±0.61)(B组)、(3.86±0.34)(C组)。冠状动脉的优秀率为15.19%(A组)、44.39%(B组)、86.42%(C组)。可诊断率比较显示RCA、PDA、D、LCX、OM、PLB、RI差异有统计学意义(P<0.05),而LAD可诊断率差异无统计学意义(χ²=4.01,P=0.135)。结论采用SSF2技术结合深度学习图像重建算法可以进一步提高较高心率患者CCTA图像质量。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	张秀芳
	罗春材

关键词 ：冠状动脉追踪冻结技术, 深度学习图像重建, 冠状动脉CT血管成像, 图像质量

Abstract：Objective To explore the application value of the second generation of snapshot freeze (SSF2) combined with deep learning image reconstruction algorithm in coronary CT angiography (CCTA) in patients with high heart rate. Methods A total of 62 patients with heart rate≥70 times/min who underwent CCTA examination were retrospectively included, and the tube voltage was 100 kV. The image quality of coronary artery was evaluated subjectively by double-blind method, which was divided into conventional images (group A), SSF1 images (group B), and SSF2 images (group C). Results The comparison of image quality scores and excellent rates among the three groups showed that RCA, PDA, LAD, D, LCX, OM, PLB and RI were significantly different(P<0.05). Coronary artery scores were (2.85±0.72)(group A), (3.38±0.61) (group B), and(3.86±0.34)(group C). The excellence rate of coronary artery was 15.19% (group A), 44.39% (group B), 86.42% (group C) respectively. The comparison of diagnosable rates showed that RCA, PDA, D, LCX, OM, PLB and RI were statistically significant (P<0.05), and LAD was not statistically significant (χ²=4.01,P=0.135). Conclusions SSF2 technology combined with deep learning image reconstruction algorithm can further improve CCTA image quality in patients with high heart rate.

Key words： snapshot freeze deep learning image reconstruction coronary CT angiography image quality

收稿日期: 2024-05-07

ZTFLH:

R541

通讯作者: 罗春材,E-mail: luochuncai2@126.com

作者简介: 张秀芳,本科学历,技师。

引用本文:

张秀芳, 罗春材. 第二代冠状动脉追踪冻结技术结合深度学习图像重建算法在较高心率冠状动脉成像中的应用[J]. 武警医学, 2024, 35(10): 847-852. ZHANG Xiufang, LUO Chuncai. Application of the second generation of snapshot freeze combined with deep learning image reconstruction algorithm in coronary CT angiography in patients with high heart rate. Med. J. Chin. Peop. Armed Poli. Forc., 2024, 35(10): 847-852.

链接本文:

https://journal08.magtechjournal.com/Jwk_wjyx/CN/ 或 https://journal08.magtechjournal.com/Jwk_wjyx/CN/Y2024/V35/I10/847

[1]	胡波,陈亚明,靳革革,等. 256层能谱CT冠状动脉成像及追踪冻结技术在不同心率患者中的应用价值[J].中国医学装备,2023,20(6):28-32.
[2]	高茹茹,刘建莉,刘显旺,等. 256排CT冠状动脉追踪冻结技术在胸痛三联检查中的应用价值[J].中国临床医学影像杂志,2022,33(4):253-257.
[3]	陈化,杜丹丹,赵小英,等. SSF技术在冠状动脉支架植入术后评估的应用价值[J].中国CT和MRI杂志,2021,19(7):97-99.
[4]	Ayman A,Sion K R,April K,et al. Evaluation of image quality for high heart rates for coronary computed tomographic angiography with advancement in CT technology: the converge registry[J]. J Cardiovasc Dev Dis, 2023, 10(9):404.
[5]	李昊岩,孙记航,田宏伟,等. 第二代全心运动校正算法改善高心率儿童心脏CTA瓣膜图像质量[J].中国医学影像技术,2021,37(12):1880-1884.
[6]	李如刚,王冬青,赵志伟,等. Revolution CT SSF联合ASiR-V算法在高心率CCTA中的应用价值-体模实验[J].中国医学计算机成像杂志,2018,24(4):342-347.
[7]	张卓璐,孙建,刘卓,等.全心运动校正算法在心肌桥-壁冠状动脉CT血管成像中的应用[J].临床放射学杂志,2023,42(5):845-849.
[8]	尹伟,王敏杰,徐瑞敏,等.基于人工智能的辅助运动校正算法对CCTA图像质量的影响[J].放射学实践,2022,37(8):1035-1041.
[9]	罗春材,李涛,杨立.双层探测器能谱CT的特点及临床应用[J].中国医疗设备,2021,36(7):170-173.
[10]	Andra H,Seyrani Y,Benjamin B,et al. Improved image quality in transcatheter aortic valve implantation planning CT using deep learning-based image reconstruction[J]. Quant Imaging Med Surg, 2023, 13(2):970-981.
[11]	Ren P L,He Y,Zhu Y,et al.Motion artefact reduction in coronary CT angiography images with a deep learning method[J]. BMC Med Imaging,2022, 22(1):184.
[12]	陆晓军,罗春材,齐叶青,等.深度学习图像重建算法在胸腹主动脉CTA图像质量中的临床应用研究[J].临床放射学杂志,2022,41(7): 1359-1364.
[13]	马燕辉,秦姝竹,徐妍妍,等.冠脉追踪冻结技术对冠脉CT血管成像优化研究[J].中日友好医院学报,2022,36(2):76-80+64.
[14]	王坤,李骋,金倞,等.基于SSF算法重组的冠状动脉CT血管成像中图像滤波改善图像质量的研究[J].临床放射学杂志,2022,41(2):357-361.
[15]	Liang J F,Wang H,Xu L,et al.Impact of SSF on diagnostic performance of coronary computed tomography angiography within 1 heart beat in patients with high heart rate using a 256-row detector computed tomography[J]. J Comput Assist Tomogr,2018, 42(1):54-61.
[16]	Liang J F,Wang H,Xu L,et al.Diagnostic performance of 256-row detector coronary CT angiography in patients with high heart rates within a single cardiac cycle: a preliminary study[J].Clin Radiol,2017, 72(8):694.e7-e14.
[17]	朱楠,刘亚,蒋双燕,等.第2代追踪冻结技术下冠状动脉钙化积分、CAD-RADS评分及CT血流储备分数的相关性分析[J].中国中西医结合影像学杂志,2023,21(5):503-507.
[18]	田宏伟,彭芸,李昊岩,等.第二代心脏冻结技术提升高心率先天性心脏病儿童大血管根部成像效果研究[J].医学影像学杂志,2022,32(11):1871-1875.
[19]	Ari G,William H A,Brian T,et al.Validation of second-generation motion-correction software for computed tomography coronary angiography with novel quantitative approach[J].J Comput Assist Tomogr,2021, 45(3):403-407.
[20]	李颖,杨楠,王春祥,等.追踪冻结技术在高心率儿童冠状动脉CTA中的应用[J].中国临床医学影像杂志,2020,31(11):801-804.
[21]	Liang J F,Sun Y,Ye Z Q,et al.Second-generation motion correction algorithm improves diagnostic accuracy of single-beat coronary CT angiography in patients with increased heart rate[J].Eur Radiol,2019, 29(8):4215-4227.
[22]	中华医学会放射学分会心胸学组,中国医师协会放射医师分会心血管学组,中国食品药品检定研究院.冠状动脉CT血管成像数据标注和质量控制专家共识[J].中华放射学杂志,2024,58(1):25-33.
[23]	宋维通,王坤,杨尚文,等. 自动追踪冻结联合智能边缘修复技术在宝石能谱CT冠状动脉成像诊断心肌桥中的应用价值[J].中国医学装备,2021,18(6):95-98.
[24]	Daniele A,Gianluca P,Saima M,et al.Image quality and radiation dose of coronary CT angiography performed with whole-heart coverage CT scanner with intra-cycle motion correction algorithm in patients with atrial fibrillation[J].Eur Radiol, 2018,28(4):1383-1392.
[25]	周莹,陈娇,何川东,等. 冠状动脉冻结技术在改善较高心率患者冠状动脉图像质量中的应用价值[J]. 华西医学,2015,30(11):2076-2080.
[26]	李昊岩,孙记航,彭芸,等. 冠状动脉追踪冻结技术对高心率儿童冠状动脉成像质量的改善[J]. 中国医学物理学杂志,2021,38(10):1237-1242.
[27]	Yoriaki M,Chikako F,Kazushi Y,et al.Evaluation of the second-generation whole-heart motion correction algorithm (SSF2) used to demonstrate the aortic annulus on cardiac CT[J].Sci Rep,2023,13(1):3636.
[28]	王梅,花立春,唐颖,等.基于深度学习的图像重建算法在CT图像重建中的应用进展[J].中国医疗设备,2022,37(12):157-161.
[29]	Joscha M,Sergej L,Julien E,et al.Deep learning-based coronary artery motion estimation and compensation for short-scan cardiac CT[J].Med Phys,2021,48(7):3559-3571.
[30]	文雨婷,李万江,李真林,等.超低管电压联合DLIR-H算法在冠脉CT血管成像“双低”扫描中的应用价值[J]. 中国医疗设备,2022,37(2):78-81,88.
[31]	Wang Y M, Wang G L,Huang X,et al.Improving image quality and resolution of coronary arteries in coronary computed tomography angiography by using high-definition scans and deep learning image reconstruction[J]. Quant Imag Med Surg,2023,13(5):2933-2940.