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综述
磁共振非对比增强三维冠状动脉成像研究进展
伍希 唐露 岳汛 彭鹏飞 邓巧 吴韬 孙家瑜

Cite this article as: Wu X, Tang L, Yue X, et al. Research progress of magnetic resonance non-contrast three-dimensional coronary imaging[J]. Chin J Magn Reson Imaging, 2022, 13(9): 148-150, 155.本文引用格式:伍希, 唐露, 岳汛, 等. 磁共振非对比增强三维冠状动脉成像研究进展[J]. 磁共振成像, 2022, 13(9): 148-150, 155. DOI:10.12015/issn.1674-8034.2022.09.035.


[摘要] 磁共振非对比增强三维冠状动脉成像具有无电离辐射、不依赖对比剂和无需屏气等独特优势,能无创检测冠脉管腔及管壁异常等病变。随着MRI序列、加速技术和人工智能的不断发展,磁共振冠脉成像技术也逐渐应用于临床,使患者受益。本文就磁共振冠脉成像在检测冠脉管腔及管壁异常等方面的应用进展进行综述。
[Abstract] MR non-contrast three-dimensional coronary imaging has the unique advantages of no-radiation, no dependence on contrast agents and free breathing, which can non-invasively detect abnormalities in the coronary wall and lumen. With the continuous development of MRI sequences, acceleration technology and artificial intelligence, coronary imaging technology is also becoming more and more perfect and gradually applied in clinical practice, benefiting more patients. The application progress of coronary MRI in detecting coronary lumen and wall abnormalities is reviewed in this paper.
[关键词] 冠状血管;磁共振血管成像;深度学习;冠心病;冠脉斑块成像
[Keywords] coronary vessels;magnetic resonance angiography;deep learning;coronary artery disease;coronary plaque imaging

伍希 1, 2   唐露 2   岳汛 1, 2   彭鹏飞 2   邓巧 2   吴韬 2   孙家瑜 2*  

1 川北医学院附属医院放射科,南充 637000

2 四川大学华西医院放射科,成都 610041

*孙家瑜,E-mail:cardiac_wchscu@163.com

作者利益冲突声明:全体作者均声明无利益冲突。


基金项目: 四川省科技计划重点研发项目 2020YFS0123
收稿日期:2022-05-24
接受日期:2022-08-10
中图分类号:R445.2  R541.4 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2022.09.035
本文引用格式:伍希, 唐露, 岳汛, 等. 磁共振非对比增强三维冠状动脉成像研究进展[J]. 磁共振成像, 2022, 13(9): 148-150, 155. DOI:10.12015/issn.1674-8034.2022.09.035

       磁共振非对比增强三维冠状动脉成像包括冠状动脉磁共振血管造影(magnetic resonance angiography, MRA)和冠状动脉管壁斑块成像,具有无电离辐射、不依赖对比剂和无需屏气等独特优势,能无创检测冠状动脉的管腔狭窄或扩张及管壁斑块等病变,在肾功能不全、对比剂过敏、儿童、孕妇及需要重复检查的特殊患者中具有更突出的应用价值[1, 2, 3]。但是由于冠脉直径较小、走形弯曲、且成像受呼吸和心跳等因素的影响,导致采集时间长、空间分辨率低和信噪比低等缺点,使磁共振冠脉成像技术具有较大的挑战性[4, 5, 6]。近年来,随着MRI序列、加速技术和人工智能的不断发展,磁共振冠脉成像在加速采集、基于深度学习的运动估计、欠采样和超分辨率重建等方面都取得了巨大进展[5,7]。本文就磁共振三维全心非对比增强冠状动脉成像在检测冠脉管腔及管壁斑块等方面的应用进展进行综述,以期磁共振冠脉成像能更广泛应用于临床,让更多患者受益。

1 冠状动脉MRA

       冠心病是冠状动脉粥样硬化导致冠状动脉狭窄、供血不足,引起心肌缺血坏死,进而出现心脏器质性功能改变的疾病[8],是全球发病和死亡的主要原因之一,预计在未来几十年对社会的负担还会增加[9, 10, 11]。冠状动脉MRA作为一种无创、无辐射的成像技术,诊断冠心病具有较高的敏感性和特异性,并具有较高的阴性预测价值[12]。稳态自由进动(steady-state free precession, SSFP)序列因具有良好的血管对比度,是1.5 T冠脉MRA的常用序列[13, 14, 15]。而梯度回波(gradient echo, GRE)序列不易受到磁场不均匀性的影响,因而在3.0 T场强下更具有优势[6]。Kaul等[16]研究结果显示在3.0 T场强下GRE比SSFP序列获得更好的图像质量、更高的对比噪声比和更长的血管长度。GRE序列、心电门控、膈肌导航和脂肪抑制已成为3.0 T非对比增强冠脉MRA的标准采集方案[1,17]。但因冠脉直径小、走形弯曲、且成像受呼吸和心跳等因素的影响,扫描时间长、空间分辨率低和信噪比低等因素成为限制冠脉MRA应用于临床的主要因素。随着硝酸甘油等血管舒张剂的应用和加速技术、心脏运动“冻结”、呼吸运动校正、深度学习等技术的创新,冠脉MRA正在克服这些挑战。

1.1 硝酸甘油等血管舒张剂

       冠心病患者冠状动脉舒张功能的异常不仅局限于内皮依赖机制,还可能涉及平滑肌细胞功能的损害,而硝酸甘油提供的非内皮依赖性一氧化氮可诱导平滑肌细胞依赖性血管舒张[13]。Heer等[18]研究表明,舌下给药硝酸甘油后冠状动脉直径和可见血管长度显著增加,从而提高了1.5 T非对比增强冠状动脉MRA检测>50%冠状动脉狭窄的诊断性能。冠脉舒张功能反映动脉硬度,而动脉硬化是未来心血管事件(卒中和心肌梗死)的独立预测因子,且与心力衰竭相关[13,19]。在1.5 T[13]和3.0 T[20]冠脉MRA的研究中发现,硝酸甘油给药后图像质量和诊断性能显著提高,硝酸甘油诱导的冠状动脉舒张功能在严重冠心病患者中受损,并表明结合硝酸甘油给药前和给药后的冠状动脉MRA能够提供一种简单无创、无电离辐射的技术来评估冠状动脉舒张功能。

       在没有明显禁忌证的情况下,建议舌下给药硝酸甘油,以改善管腔内信噪比、血管直径和冠状动脉MRA的血管清晰度[1]。但硝酸甘油也可能提高患者心率而损害图像质量,因此,对高心率患者应谨慎使用硝酸甘油。

1.2 加速技术

       冠状动脉管径细小,其成像需要更高的空间分辨率。而空间分辨率越高,信噪比会相应降低,所以在维持高空间分辨率和高图像质量的同时会使采集时间增加。同时,图像采集过程中,呼吸和心率发生不规整改变,也会增加采集时间。并行采集和压缩感知是冠状动脉MRA最常用的加速方法。高分辨率冠状动脉MRA采用并行采集技术可缩短图像采集时间,并保持图像质量,但高加速因子会导致特定的伪影,如残差混叠和g因子噪声增强[1]。压缩感知技术通过k空间的随机欠采样和迭代重建来快速成像[4,21]。Nakamura等[17]研究显示,在3.0 T非对比增强冠脉MRA中,压缩感知技术比并行采集的扫描时间显著缩短,同时保持可接受的冠脉可视化。也有研究[22]表明,压缩感知加速的冠脉MRA比传统的并行采集产生更高的整体图像质量。Lu等[6]于2021年提出3.0 T非对比增强Dion水-脂分离全心冠脉MRA成像,结合合适的压缩感知加速因子能够检测疑似冠心病患者显著的冠状动脉狭窄,其诊断准确度为81%。

       在追求快速采集的同时,并行采集和压缩感知技术采用合适的加速因子对图像质量至关重要。此外,并行采集与压缩感知技术的结合也是活跃的研究领域,其有望提高冠脉MRA空间分辨率同时减少采集时间。

1.3 心脏运动“冻结”

       冠脉随着心脏运动也不断处于运动的状态,成功的冠脉MRA需要有效的运动控制,目前的自由呼吸方案常用节段采集和前瞻性心电门控来抑制心脏运动伪影,通常在舒张中晚期采集数据[23, 24]。但对于心律失常、异位性搏动、高心率和可变心率的患者,可以通过收缩期进行图像采集,因为收缩期对心律失常和心率变异性敏感性低,但是与舒张期成像相比,其静止期相对较短,可能需要更长的扫描时间[1,23]。右冠状动脉相对左冠状动脉运动幅度更大[25],因而多通过观察右冠状动脉来选择合适的延迟触发时间和采集窗。在冠脉MRA采集之前一般会扫描多时相的电影图像,选择右冠状动脉运动幅度最小的R–R间期作为冠脉采集窗。另一种方法是以类似于冠状动脉CT血管造影的方式连续采集,然后回顾性重建多个心脏期相,最后选择运动伪影最少的时相图像[4]。目前常用的前瞻性心电门控使数据采集与心动周期同步,并调整到冠脉运动相对较少的期相,降低心脏运动对图像质量的干扰,选择准确的冠脉采集窗对成功磁共振冠脉成像至关重要。

1.4 呼吸运动校正

       最初通过屏气来抑制呼吸运动伪影,但由于膈顶漂移和患者长时间憋气能力有限而图像质量并不理想。因此,自由呼吸运动补偿的3D冠状动脉MRA引起广泛关注。膈肌导航技术利用肝-膈肌界面的轮廓实现呼吸上下平移运动估计,同时用于门控或校正[4]。为了获得高质量图像,膈肌导航一般会采用窄的采集窗(±5 mm),并依赖于患者的呼吸模式,导致采集时间不可预测且采集效率较低(30%~50%),同时长的采集时间会导致患者不耐受而产生更多的运动伪影或心率和呼吸模式的改变使图像质量降低[1,5]。因此,新的呼吸运动补偿框架如一维自导航[26]和基于图像的导航技术(image-based navigators, iNAVs)[27]被提出,将呼吸扫描效率提高到100%,从而显著减少采集时间。Heerfordt等[28]将笛卡尔膈肌导航技术与3D径向自导航进行比较,结果显示自导航技术获得了更短的采集时间和更均匀的血池图像,但血管锐利度低和可见血管长度相对更短。一维自导航很难将运动(如心脏)和静止(如胸壁)的组织分离开来,从而产生伪影。iNAVs是自导航的一个可行替代方法,它在采集高分辨率冠脉MRA之前,通过在每个心动周期采集低空间分辨率的2D/3D图像,将运动组织分离于静态组织,且该框架能够在多个方向上估计呼吸运动[4]。Nazir等[27]前瞻性纳入45例疑似冠心病患者,验证了1.5 T非对比增强基于2D图像导航高分辨率冠脉MRA检测冠心病良好的诊断准确性,与有创冠脉造影相比,具有高的敏感度(95%)和阴性预测值(93%)。Munoz等[29]也证明了一种基于图像导航的水/脂肪冠脉MRA方法的可行性,其图像质量与膈肌导航相当,但扫描时间更短且可预测。Hajhosseiny等[30]将基于图像导航器的高度欠采样采集和非刚性运动校正相结合,使三维高分辨率非对比增强冠脉MRA在临床可行和可预测的时间内(约10 min)进行采集,在排除低中等风险冠心病患者的显著狭窄方面实现较高的诊断准确性。

       尽管提供了100%的呼吸扫描效率,但自导航或iNAVs框架本身是不够的。获得高分辨率(1 mm等体素)冠脉MRA可能需要30 min来获得完全采样[5]。将自导航或iNAVs与图像加速技术相结合,或应用更先进的运动校正框架,以望在临床可行的采集时间内实现高空间分辨率的冠脉MRA。各导航技术的效率和诊断性能还需更多研究进一步验证。

1.5 深度学习

       在各种加速技术的应用下,图像去噪是冠脉MRA不可或缺的步骤。深度学习基于“学习噪声”策略的特点,通过有效地优化去噪水平和良好的边缘保存来改进去噪,有利于提高冠状动脉MRA图像质量,且深度学习应用具有灵活性,包括应用于图像域和k空间域来创建去噪的空间图像或去噪的k空间数据[31, 32]。多项研究[33, 34, 35, 36, 37, 38]通过训练不同的神经网络去寻找重建的最优变换,来改善冠脉MRA图像质量或减少采集时间。Yokota等[33]评估深度学习重建在3.0 T非对比增强冠脉MRA图像质量的影响,研究结果显示与传统冠脉MRA相比,深度学习重建显著提高了高分辨率冠脉MRA的对比噪声比,从而具有更高的视觉图像质量和更好的血管可追溯性。Qi等[34]引入深度学习方法,用于高度欠采样自由呼吸全心冠脉MRA同步非刚性运动估计和运动校正重建,达到快速采集和快速重建,可作为一种有效排除显著冠状动脉疾病的工具。虽然深度学习与图像采集加速技术相结合时,其在不同框架中具有很大的潜在应用价值,但临床应用必须谨慎,因为不适当的去噪可能影响图像和临床诊断的准确性。

       冠脉MRA成像不仅受场强、序列、血管舒张剂、加速技术、心脏运动“冻结”、呼吸运动校正及深度学习的影响,同时,检查前患者准备和患者自身情况(心率快慢,呼吸模式和耐受性等)也是影响冠脉MRA成功的关键因素。随着MRI序列和各技术的不断发展,冠脉MRA的临床应用限制因素也正逐渐克服。

2 磁共振冠状动脉管壁斑块成像

       冠状动脉粥样硬化的早期表现是动脉重塑(动脉扩大),并在冠状动脉壁形成动脉粥样硬化斑块。因此,更大的动脉粥样硬化负担往往先于管腔狭窄。这种早期斑块不会导致冠状动脉狭窄,在传统的有创冠状动脉造影中无法识别[39]。冠状动脉管壁斑块成像是一个快速发展的领域,在不依赖对比剂的情况下冠状动脉MRI可以利用斑块特征(斑块内出血、血栓、脂质核心等)的T1缩短作用来识别高危冠状动脉斑块[4,40],如非对比增强T1加权反转恢复(T1-weighted inversion-recovery, T1W-IR)序列,在过去十几年内已成为用于识别高危冠脉斑块的一种新的无创成像技术[41]。Kawasaki等[42]发现非对比增强T1W-IR图像上的高信号冠脉斑块与动脉重塑、超声衰减和较低的CT值有关,所有这些都被认为是不稳定斑块的指示。最近也有研究表明,非对比增强T1W-IR图像上的管壁高信号斑块可预测稳定型冠状动脉疾病患者未来的冠状动脉事件[43]。Stuber[39]认为当非对比增强T1W-IR图像中存在高信号时,冠状动脉斑块可能更容易破裂。

       尽管传统的T1加权图像对评估冠状动脉粥样硬化具有前瞻性的预后能力,但仍有一些技术障碍阻碍了临床转化,如缺乏解剖参考、低空间分辨率、长时间和不可预测的扫描时间。为解决上述斑块表征的局限性,Xie等[44]于2017年引入冠状动脉粥样硬化T1加权表征(coronary atherosclerosis T1-weighted characterization, CATCH)技术,即在一次自由呼吸三维全心检查中,能够同时获得亮血和黑血冠脉MRA以及高信号冠状动脉斑块的可视化。其研究结果显示CATCH提供了高时效(约10 min)全心冠状动脉斑块的特征,高T1信号强度区域与侵入性研究中的高危斑块特征呈正相关。但高信号斑块的原因尚不清楚,斑块内出血和局部脂质积累都可能导致冠状动脉壁的高T1信号。Sato等[45]在一项回顾性研究中,使用CATCH方法探讨了冠状动脉斑块成分的无创表征。其研究结果显示血管内超声的回声区与高信号斑块有很强的联系,但在稳定型冠状动脉疾病中的高信号斑块的主要底物是斑块内出血,而不是脂质。而另一项研究显示高信号斑块与愈合的破裂斑块和大脂质核密切相关[46]。由于呼吸运动参数在亮血和黑血数据集中部分共享,冠状动脉斑块的错位可能无法避免。Ginami等[47]最近也介绍了3D全心非对比增强亮血和黑血相位敏感反转恢复序列,同时获得冠状动脉NRA和血栓/斑块内出血可视化,允许独立估计/纠正呼吸运动,进一步减少配准错误的可能性。

       总之,磁共振非对比增强T1加权图像上出现的高信号与斑块破裂的倾向有关,而冠状动脉信号增强的幅度对指导和监测治疗具有重要意义。用于评估冠状动脉管壁斑块的各序列正在研究中,其应用到临床环境中还需要更进一步的验证。

3 小结与展望

       磁共振非对比增强冠状动脉成像具有无电离辐射、不依赖对比剂和无需屏气等独特优势,对冠脉疾病的早期检测、危险分层和预后评估等方面具有重要意义。扫描时间长、空间分辨率低和图像质量差是冠状动脉MRI的主要限制,随着MRI序列、加速技术和人工智能的不断发展,冠脉MRA和磁共振冠脉管壁斑块成像技术逐渐得到完善,同时联合心脏电影技术、mapping技术、首过灌注、延迟强化、弥散张量成像、相位对比和4D FLOW等技术,对心血管形态、功能、心肌活性及血流动力等提供全面检查,真正实现“一站式”心血管MRI,造福更多心血管疾病的患者。

[1]
Kato Y, Ambale-Venkatesh B, Kassai Y, et al. Non-contrast coronary magnetic resonance angiography: current frontiers and future horizons[J]. Magn Reson Mater Phy, 2020, 33(5): 591-612. DOI: 10.1007/s10334-020-00834-8.
[2]
中国医学装备协会磁共振应用专业委员会. 冠状动脉MR血管成像临床应用专家共识(第一版)[J]. 中华放射学杂志, 2021, 55(9): 895-902. DOI: 10.3760/cma.j.cn112149-20210511-00468.
Magnetic Resonance Application Professional Committee of China Medical Equipment Association.Expert consensus on clinical application of coronary MR angiography (first edition)[J]. Chin J Radiol, 2021, 55(9): 895-902. DOI: 10.3760/cma.j.cn112149-20210511-00468.
[3]
Kato S, Fukui K. Successful stent implantation with the use of non contrast whole-heart coronary magnetic resonance angiography and intravascular ultrasound in patient with allergy to iodinated contrast media[J]. Cardiovasc Interv and Ther, 2021, 36(4): 539-541. DOI: 10.1007/s12928-020-00712-z.
[4]
Hajhosseiny R, Bustin A, Munoz C, et al. Coronary magnetic resonance angiography: technical innovations leading us to the promised land?[J]. JACC Cardiovasc Imaging, 2020, 13(12): 2653-2672. DOI: 10.1016/j.jcmg.2020.01.006.
[5]
Hajhosseiny R, Munoz C, Cruz G, et al. Coronary magnetic resonance angiography in chronic coronary syndromes[J/OL]. Front Cardiovasc Med, 2021, 8 [2022-05-20]. https://doi.org/10.3389/fcvm.2021.682924. DOI: 10.3389/fcvm.2021.682924.
[6]
Lu H, Guo J, Zhao S, et al. Assessment of non-contrast-enhanced Dixon water-fat separation compressed sensing whole-heart coronary MR angiography at 3.0 T: a single-center experience[J]. Acad Radiol, 2022, 29(Suppl 4): S82-S90. DOI: 10.1016/j.acra.2021.05.009.
[7]
Roy CW, Heerfordt J, Piccini D, et al. Motion compensated whole-heart coronary cardiovascular magnetic resonance angiography using focused navigation (fNAV)[J/OL]. J Cardiovasc Magn Reson, 2021, 23(1) [2022-05-20]. https://doi.org/10.1186/s12968-021-00717-4. DOI: 10.1186/s12968-021-00717-4.
[8]
中国冠状动脉杂交血运重建专家共识编写组. 中国冠状动脉杂交血运重建专家共识(2022)[J]. 中华胸心血管外科杂志, 2022, 38(7): 385-395. DOI: 10.3760/cma.j.cn112434-20220507-00152.
Experts Consensus Group on the Hybrid Coronary Revascularization in China. Hybrid coronary revascularization experts consensus in China (2022)[J]. Chin J Thorac Cardiovasc Surg, 2022, 38(7): 385-395. DOI: 10.3760/cma.j.cn112434-20220507-00152.
[9]
Ties D, van Dorp P, Pundziute G, et al. Early detection of obstructive coronary artery disease in the asymptomatic high-risk population: objectives and study design of the EARLY-SYNERGY trial[J]. Am Heart J, 2022, 246: 166-177. DOI: 10.1016/j.ahj.2022.01.005.
[10]
GBD Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet, 2020, 396(10258): 1204-1222. DOI: 10.1016/S0140-6736(20)30925-9.
[11]
Taylor AJ, Papapostolou S. Finding the right pathway for the assessment of stable coronary artery disease[J]. JACC Cardiovasc Imaging, 2022, 15(4): 626-628. DOI: 10.1016/j.jcmg.2021.12.009.
[12]
Zahergivar A, Kocher M, Waltz J, et al. The diagnostic value of non-contrast magnetic resonance coronary angiography in the assessment of coronary artery disease: a systematic review and meta-analysis[J/OL]. Heliyon, 2021, 7(3) [2022-05-20]. https://doi.org/10.1016/j.heliyon.2021. DOI: 10.1016/j.heliyon.2021.e06386.
[13]
Zhao SH, Li CG, Chen YY, et al. Applying nitroglycerin at coronary MR angiography at 1.5 T: diagnostic performance of coronary vasodilation in patients with coronary artery disease[J/OL]. Radiol Cardiothorac Imaging, 2020, 2(2) [2022-05-20]. https://doi.org/10.1148/ryct.2020190018. DOI: 10.1148/ryct.2020190018.
[14]
Lin L, Wang L, Zhang XN, et al. A clinical strategy to improve the diagnostic accuracy of 1.5-T non-contrast MR coronary angiography for detection of coronary artery disease: combination of whole-heart and volume-targeted imaging[J]. Eur Radiol, 2021, 31(4): 1894-1904. DOI: 10.1007/s00330-020-07135-7.
[15]
Zhao SH, Chen YY, Yun H, et al. Three-dimensional free-breathing whole-heart coronary magnetic resonance angiography at 1.5 T: gadobutrol-enhanced gradient-echo acquisition sequence versus non-contrast-enhanced steady-state free precession sequence[J]. J Comput Assist Tomogr, 2019, 43(6): 919-925. DOI: 10.1097/RCT.0000000000000933.
[16]
Kaul MG, Stork A, Bansmann PM, et al. Evaluation of balanced steady-state free precession (TrueFISP) and K-space segmented gradient echo sequences for 3D coronary MR angiography with navigator gating at 3 Tesla[J]. Rofo, 2004, 176(11): 1560-1565. DOI: 10.1055/s-2004-813629.
[17]
Nakamura M, Kido T, Kido T, et al. Non-contrast compressed sensing whole-heart coronary magnetic resonance angiography at 3T: a comparison with conventional imaging[J]. Eur J Radiol, 2018, 104: 43-48. DOI: 10.1016/j.ejrad.2018.04.025.
[18]
Heer T, Reiter S, Trißler M, et al. Effect of nitroglycerin on the performance of MR coronary angiography[J]. J Magn Reson Imaging, 2017, 45(5): 1419-1428. DOI: 10.1002/jmri.25483.
[19]
Kang S, Fan HM, Li J, et al. Relationship of arterial stiffness and early mild diastolic heart failure in general middle and aged population[J]. Eur Heart J, 2010, 31(22): 2799-2807. DOI: 10.1093/eurheartj/ehq296.
[20]
Lu HF, Zhao SH, Tian D, et al. Clinical application of non-contrast-enhanced Dixon water-fat separation compressed SENSE whole-heart coronary MR angiography at 3.0 T with and without nitroglycerin[J]. J Magn Reson Imaging, 2022, 55(2): 579-591. DOI: 10.1002/jmri.27829.
[21]
Hirai K, Kido T, Kido T, et al. Feasibility of contrast-enhanced coronary artery magnetic resonance angiography using compressed sensing[J/OL]. J Cardiovasc Magn Reson, 2020, 22(1) [2022-05-20]. https://doi.org/10.1186/s12968-020-0601-0. DOI: 10.1186/s12968-020-0601-0.
[22]
Akçakaya M, Basha TA, Chan RH, et al. Accelerated isotropic sub-millimeter whole-heart coronary MRI: compressed sensing versus parallel imaging[J]. Magn Reson Med, 2014, 71(2): 815-822. DOI: 10.1002/mrm.24683.
[23]
Androulakis E, Mohiaddin R, Bratis K. Magnetic resonance coronary angiography in the era of multimodality imaging[J/OL]. Clin Radiol, 2022, 77(7) [2022-08-03]. https://doi.org/10.1016/j.crad.2022.03.008. DOI: 10.1016/j.crad.2022.03.008.
[24]
Zitzelsberger T, Krumm P, Hornung A, et al. Multi-phase coronary magnetic resonance angiography improves delineation of coronary arteries[J]. Acta Radiol, 2019, 60(11): 1422-1429. DOI: 10.1177/0284185119830289.
[25]
Hofman MB, Wickline SA, Lorenz CH. Quantification of in-plane motion of the coronary arteries during the cardiac cycle: implications for acquisition window duration for MR flow quantification[J]. J Magn Reson Imaging, 1998, 8(3): 568-576. DOI: 10.1002/jmri.1880080309.
[26]
Albrecht MH, Varga-Szemes A, Schoepf UJ, et al. Diagnostic accuracy of noncontrast self-navigated free-breathing MR angiography versus CT angiography: a prospective study in pediatric patients with suspected anomalous coronary arteries[J]. Acad Radiol, 2019, 26(10): 1309-1317. DOI: 10.1016/j.acra.2018.12.010.
[27]
Nazir MS, Bustin A, Hajhosseiny R, et al. High-resolution non-contrast free-breathing coronary cardiovascular magnetic resonance angiography for detection of coronary artery disease: validation against invasive coronary angiography[J/OL]. J Cardiovasc Magn Reson, 2022, 24(1) [2022-08-01]. https://doi.org/10.1186/s12968-022-00858-0. DOI: 10.1186/s12968-022-00858-0.
[28]
Heerfordt J, Stuber M, Maillot A, et al. A quantitative comparison between a navigated Cartesian and a self-navigated radial protocol from clinical studies for free-breathing 3D whole-heart bSSFP coronary MRA[J]. Magn Reson Med, 2020, 84(1): 157-169. DOI: 10.1002/mrm.28101.
[29]
Munoz C, Cruz G, Neji R, et al. Motion corrected water/fat whole-heart coronary MR angiography with 100% respiratory efficiency[J]. Magn Reson Med, 2019, 82(2): 732-742. DOI: 10.1002/mrm.27732.
[30]
Hajhosseiny R, Rashid I, Bustin A, et al. Clinical comparison of sub-mm high-resolution non-contrast coronary CMR angiography against coronary CT angiography in patients with low-intermediate risk of coronary artery disease: a single center trial[J/OL]. J Cardiovasc Magn Reson, 2021, 23(1) [2022-5-20]. https://doi.org/10.1186/s12968-021-00758-9. DOI: 10.1186/s12968-021-00758-9.
[31]
Zhu B, Liu JZ, Cauley SF, et al. Image reconstruction by domain-transform manifold learning[J]. Nature, 2018, 555(7697): 487-492. DOI: 10.1038/nature25988.
[32]
Han Y, Sunwoo L, Ye JC. k-space deep learning for accelerated MRI[J]. IEEE Trans Med Imaging, 2020, 39(2): 377-386. DOI: 10.1109/tmi.2019.2927101.
[33]
Yokota Y, Takeda C, Kidoh M, et al. Effects of deep learning reconstruction technique in high-resolution non-contrast magnetic resonance coronary angiography at a 3-tesla machine[J]. J L'association Can Des Radiol, 2021, 72(1): 120-127. DOI: 10.1177/0846537119900469.
[34]
Qi HK, Hajhosseiny R, Cruz G, et al. End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA[J]. Magn Reson Med, 2021, 86(4): 1983-1996. DOI: 10.1002/mrm.28851.
[35]
Fuin N, Bustin A, Küstner T, et al. A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography[J]. Magn Reson Imaging, 2020, 70: 155-167. DOI: 10.1016/j.mri.2020.04.007.
[36]
Hosseini SAH, Zhang C, Weingärtner S, et al. Accelerated coronary MRI with sRAKI: a database-free self-consistent neural network k-space reconstruction for arbitrary undersampling[J/OL]. PLoS One, 2020, 15(2) [2022-05-20]. https://doi.org/10.1371/journal.pone.0229418. DOI: 10.1371/journal.pone.0229418.
[37]
Kobayashi H, Nakayama R, Hizukuri A, et al. Improving image resolution of whole-heart coronary MRA using convolutional neural network[J]. J Digit Imaging, 2020, 33(2): 497-503. DOI: 10.1007/s10278-019-00264-6.
[38]
Qi HK, Fuin N, Cruz G, et al. Non-rigid respiratory motion estimation of whole-heart coronary MR images using unsupervised deep learning[J]. IEEE Trans Med Imaging, 2021, 40(1): 444-454. DOI: 10.1109/TMI.2020.3029205.
[39]
Stuber M. CATCH the wave of coronary atherosclerotic plaque MRI[J]. Radiology, 2022, 302(3): 566-567. DOI: 10.1148/radiol.212911.
[40]
Hajhosseiny R, Bahaei TS, Prieto C, et al. Molecular and nonmolecular magnetic resonance coronary and carotid imaging[J]. Arterioscler Thromb Vasc Biol, 2019, 39(4): 569-582. DOI: 10.1161/ATVBAHA.118.311754.
[41]
Liu W, Xie YB, Wang C, et al. Atherosclerosis T1-weighted characterization (CATCH): evaluation of the accuracy for identifying intraplaque hemorrhage with histological validation in carotid and coronary artery specimens[J/OL]. J Cardiovasc Magn Reson, 2018, 20(1) [2022-08-01]. https://doi.org/10.1186/s12968-018-0447-x. DOI: 10.1186/s12968-018-0447-x.
[42]
Kawasaki T, Koga S, Koga N, et al. Characterization of hyperintense plaque with noncontrast T1-weighted cardiac magnetic resonance coronary plaque imaging[J]. JACC Cardiovasc Imaging, 2009, 2(6): 720-728. DOI: 10.1016/j.jcmg.2009.01.016.
[43]
Noguchi T, Kawasaki T, Tanaka A, et al. High-intensity signals in coronary plaques on noncontrast T1-weighted magnetic resonance imaging as a novel determinant of coronary events[J]. J Am Coll Cardiol, 2014, 63(10): 989-999. DOI: 10.1016/j.jacc.2013.11.034.
[44]
Xie YB, Kim YJ, Pang JN, et al. Coronary atherosclerosis T1-weighed characterization with integrated anatomical reference: comparison with high-risk plaque features detected by invasive coronary imaging[J]. JACC Cardiovasc Imaging, 2017, 10(6): 637-648. DOI: 10.1016/j.jcmg.2016.06.014.
[45]
Sato S, Matsumoto H, Li DB, et al. Coronary high-intensity plaques at T1-weighted MRI in stable coronary artery disease: comparison with near-infrared spectroscopy intravascular US[J]. Radiology, 2022, 302(3): 557-565. DOI: 10.1148/radiol.211463.
[46]
Kanaya T, Noguchi T, Otsuka F, et al. Optical coherence tomography-verified morphological correlates of high-intensity coronary plaques on non-contrast T1-weighted magnetic resonance imaging in patients with stable coronary artery disease[J]. Eur Heart J Cardiovasc Imaging, 2019, 20(1): 75-83. DOI: 10.1093/ehjci/jey035.
[47]
Ginami G, Neji R, Rashid I, et al. 3D whole-heart phase sensitive inversion recovery CMR for simultaneous black-blood late gadolinium enhancement and bright-blood coronary CMR angiography[J/OL]. J Cardiovasc Magn Reson, 2017, 19(1) [2022-08-01]. https://doi.org/10.1186/s12968-017-0405-z. DOI: 10.1186/s12968-017-0405-z.

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