心脏磁共振电影成像纹理特征与传统心功能参数分析肥厚型心肌病心衰程度的对比研究

方良瑞

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心脏磁共振电影成像纹理特征与传统心功能参数分析肥厚型心肌病心衰程度的对比研究

更新时间：2025-09-11

- 心脏磁共振电影成像纹理特征与传统心功能参数分析肥厚型心肌病心衰程度的对比研究
- Textural feature analysis of Cine-cardiac magnetic resonance in the grading of heart failure in patients with hypertrophic cardiomyopathy compared with conventional cardiac function parameters
- 重庆医科大学学报 2022年47卷第6期页码：719-726
- 作者机构：
  
  陆军军医大学第一附属医院放射科,重庆,400038
- 作者简介：
- 基金信息：
  
  国家自然科学基金面上资助项目(62076247);国家自然科学青年基金资助项目(61701506);陆军军医大学临床医学科研人才计划资助项目(2018XLC3023)
- DOI：
  中图分类号： R816;R445.2;R811.5
- 纸质出版日期：2022
- 稿件说明：
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方良瑞. 心脏磁共振电影成像纹理特征与传统心功能参数分析肥厚型心肌病心衰程度的对比研究[J]. 重庆医科大学学报, 2022,47(6):719-726.

Fang Liangrui. Textural feature analysis of Cine-cardiac magnetic resonance in the grading of heart failure in patients with hypertrophic cardiomyopathy compared with conventional cardiac function parameters[J]. Journal of chongqing medical university, 2022, 47(6): 719-726.
方良瑞. 心脏磁共振电影成像纹理特征与传统心功能参数分析肥厚型心肌病心衰程度的对比研究[J]. 重庆医科大学学报, 2022,47(6):719-726. DOI：

Fang Liangrui. Textural feature analysis of Cine-cardiac magnetic resonance in the grading of heart failure in patients with hypertrophic cardiomyopathy compared with conventional cardiac function parameters[J]. Journal of chongqing medical university, 2022, 47(6): 719-726. DOI：

摘要

目的：探讨心脏磁共振（cardiac magnetic resonance，CMR）电影序列纹理特征分析肥厚型心肌病（hypertrophic cardiomyopathy，HCM）患者心衰程度的可行性。方法：回顾性分析陆军军医大学第一附属医院2013年至2016年接受CMR检查的HCM患者82例，按照纽约心脏协会（New York Heart Association，NYHA）心功能分级法分组，NYHAⅠ~Ⅱ为轻度心衰组，NYHAⅢ~Ⅳ为重度心衰组。电影序列图像用MATLAB软件手动提取所有患者左室舒张末期心肌区域2次，Bland-Altman检验计算2次特征一致性，卷积神经网络（convolutional neural networks，CNN）计算优化后特征曲线下面积（area under the curve，AUC）。卡方检验比较2组患者男女分布差异性，心血管后处理软件（Circle Cardiovascular Imaging，CVI，Canada）计算左心室延时强化（late gadolinium enhancement，LGE）百分比，Argus软件计算左心室射血分数（ejection fraction，EF）、每分钟心输出量（cardiac output，CO）及心脏指数（cardiac index，CI），并做Mann-Whitney U检验比较2组患者年龄、体质指数（body mass index，BMI）、左心室功能差异性，受试者工作特征曲线（receiver operating characteristic，ROC）分析计算曲线下面积。结果： 2组患者性别分布、年龄和BMI差异无统计学意义（P=0.407、0.338、0.120）。轻重度心衰2组患者左心室EF、LGE、CO和CI心功能参数差异无统计学意义（P=0.860、0.072、0.944、0.568）。ROC计算得出纹理特征曲线下面积为91.31%，明显大于EF、LGE、CO和CI的曲线下面积（51.22%、62.53%、49.51%、48.91%）。结论：纹理特征分析算法能够有效定量鉴别HCM患者纽约心功能分级的Ⅰ~Ⅱ、Ⅲ~Ⅳ级差别，敏感性明显高于EF、LGE、CO和CI。

Abstract

Objective: To assess the feasibility of textural feature analysis of Cine-cardiac magnetic resonance (CMR) as a tool to evaluate heart failure grades in patients with hypertrophic cardiomyopathy (HCM). Methods: Eighty-two patients with HCM that underwent CMR examination at The Southwest Hospital of Army Medical University from 2013 to 2016 were enrolled in this retrospective study. All of the patients were divided into two groups according to the New York Heart Association (NYHA) classification of heart failure. Patients with NYHAⅠ-Ⅱwere selected as the mild heart failure group

and the other patients with NYHAⅢ-Ⅳwere selected as the severe heart failure group. The textures of left ventricular myocardium at the end of diastasis were extracted manually by MATLAB software twice from Cine sequence

and the Bland-Altman analysis was used to calculate the features consistency of the two times. The convolutional neural networks (CNN) calculated the area under the curve (AUC) of the features after optimization. Chi-square test was used to compare the difference of gender distribution between the two groups. The percentage of late gadolinium enhancement (LGE) in the left ventricle was calculated by Circle Cardiovascular Imaging (CVI

Canada) software. Argus software was used to calculate the left ventricular ejection fraction (EF)

cardiac output (CO) and cardiac index (CI). Mann-Whitney U test was performed to compare the difference of the age

body mass index (BMI) and left ventricular function parameters between the two groups

and the receiver operating characteristic (ROC) was used to analyze the AUC of those function parameters. Results: The gender distribution

age and BMI were not significantly different between the two groups (P=0.407

0.338

0.120). The EF

LGE

CO and CI cardiac function parameters of left ventricles between the mild and severe heart failure groups were of no significant difference (P=0.860

0.072

0.944

0.568). The AUC calculated by CNN was 91.31%

which was significantly larger than that of EF

LGE

CO and CI (51.22%

62.53%

49.51%

48.91%). Conclusion: Textural feature analysis algorithm can effectively identify the difference of NYHAⅠ-Ⅱand NYHAⅢ-Ⅳgrades in patients with HCM

and with higher sensitivity than that of CMR function parameters.

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