利用自动深度学习 EA 比率分析通过四维血流 CMR 评估舒张功能。

IF 4.2 1区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Federica Viola, Mariana Bustamante, Ann Bolger, Jan Engvall, Tino Ebbers
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引用次数: 0

摘要

背景:左心室舒张功能障碍对心力衰竭患者的症状和预后有很大影响。对于左室收缩功能减退的患者,EA 比值是确定舒张功能障碍等级的第一步。多普勒超声心动图是舒张功能评估的首选成像技术,而 CMR 作为一种方法还不太成熟。之前基于四维血流的研究主要观察二尖瓣近端的 EA 比值,需要人工操作。在本研究中,我们比较了基于 4D Flow CMR 的自动深度学习 EA 比值评估方法和两种半自动方法,以及基于回波的传统金标准方法:97名慢性缺血性心脏病患者接受了心脏回波检查和核磁共振成像检查。使用三种不同的方法计算基于 4D 流量的 EA 比值;两种是半自动方法,通过测量二尖瓣平面的血流速度(MVvel)和血流容量(MVflow)来评估 EA 比值;一种是全自动方法,使用基于深度学习的方法创建完整的 LV 分割,在此范围内评估 EA 比值而不受二尖瓣平面(LVvel)的限制:结果:MVvel、MVflow 和 LVvel EA 比值与 Echo EA 比值密切相关(R2= 0.60、0.58、0.72)。LVvel 峰值 E 和 A 与 Echo 峰值 E 和 A 呈中度相关,而 MVvel 值则呈弱相关。MVvel 和 MVflow EA 比值与 LVvel 的相关性非常强(R2= 0.84、0.86)。MVvel E 峰与 LVvel 的相关性一般,而 A 峰与 LVvel 的相关性较强(R2= 0.26,0.57):讨论与结论:E 峰、A 峰和 EA 比值是评估舒张功能障碍不可或缺的指标,可扩大心血管疾病患者 CMR 研究的实用性。虽然E峰值和A峰值速度的绝对值被低估,但所有三种4D Flow方法测得的EA比值与金标准多普勒超声心动图密切相关。基于深度学习的自动方法表现最佳,运行时间约为 40 秒。由于这两种半自动方法与 LVvel 的相关性非常强,因此可作为估算 EA 比值的替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diastolic function assessment with four-dimensional flow cardiovascular magnetic resonance using automatic deep learning E/A ratio analysis.

Background: Diastolic left ventricular (LV) dysfunction is a powerful contributor to the symptoms and prognosis of patients with heart failure. In patients with depressed LV systolic function, the E/A ratio, the ratio between the peak early (E) and the peak late (A) transmitral flow velocity, is the first step to defining the grade of diastolic dysfunction. Doppler echocardiography (echo) is the preferred imaging technique for diastolic function assessment, while cardiovascular magnetic resonance (CMR) is less established as a method. Previous four-dimensional (4D) Flow-based studies have looked at the E/A ratio proximal to the mitral valve, requiring manual interaction. In this study, we compare an automated, deep learning-based and two semi-automated approaches for 4D Flow CMR-based E/A ratio assessment to conventional, gold-standard echo-based methods.

Methods: Ninety-seven subjects with chronic ischemic heart disease underwent a cardiac echo followed by CMR investigation. 4D Flow-based E/A ratio values were computed using three different approaches; two semi-automated, assessing the E/A ratio by measuring the inflow velocity (MVvel) and the inflow volume (MVflow) at the mitral valve plane, and one fully automated, creating a full LV segmentation using a deep learning-based method with which the E/A ratio could be assessed without constraint to the mitral plane (LVvel).

Results: MVvel, MVflow, and LVvel E/A ratios were strongly associated with echocardiographically derived E/A ratio (R2 = 0.60, 0.58, 0.72). LVvel peak E and A showed moderate association to Echo peak E and A, while MVvel values were weakly associated. MVvel and MVflow EA ratios were very strongly associated with LVvel (R2 = 0.84, 0.86). MVvel peak E was moderately associated with LVvel, while peak A showed a strong association (R2 = 0.26, 0.57).

Conclusion: Peak E, peak A, and E/A ratio are integral to the assessment of diastolic dysfunction and may expand the utility of CMR studies in patients with cardiovascular disease. While underestimation of absolute peak E and A velocities was noted, the E/A ratio measured with all three 4D Flow methods was strongly associated with the gold standard Doppler echocardiography. The automatic, deep learning-based method performed best, with the most favorable runtime of ∼40 seconds. As both semi-automatic methods associated very strongly to LVvel, they could be employed as an alternative for estimation of E/A ratio.

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来源期刊
CiteScore
10.90
自引率
12.50%
发文量
61
审稿时长
6-12 weeks
期刊介绍: Journal of Cardiovascular Magnetic Resonance (JCMR) publishes high-quality articles on all aspects of basic, translational and clinical research on the design, development, manufacture, and evaluation of cardiovascular magnetic resonance (CMR) methods applied to the cardiovascular system. Topical areas include, but are not limited to: New applications of magnetic resonance to improve the diagnostic strategies, risk stratification, characterization and management of diseases affecting the cardiovascular system. New methods to enhance or accelerate image acquisition and data analysis. Results of multicenter, or larger single-center studies that provide insight into the utility of CMR. Basic biological perceptions derived by CMR methods.
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