Brendan T Crabb, Rahul S Chandrupatla, Evan M Masutani, Sophie Y Wong, Sachin Govil, Silvia Montserrat, Susana Prat-González, Julián Vega-Adauy, Melany Atkins, Daniel Lorenzatti, Chiara Zocchi, Elena Panaioli, Nathalie Boddaert, Laith Alshawabkeh, Lewis Hahn, Sanjeet Hegde, Andrew D McCulloch, Francesca Raimondi, Albert Hsiao
{"title":"修复法洛四联症左心室功能障碍的特征:区域劳损和非同步化的多机构深度学习分析。","authors":"Brendan T Crabb, Rahul S Chandrupatla, Evan M Masutani, Sophie Y Wong, Sachin Govil, Silvia Montserrat, Susana Prat-González, Julián Vega-Adauy, Melany Atkins, Daniel Lorenzatti, Chiara Zocchi, Elena Panaioli, Nathalie Boddaert, Laith Alshawabkeh, Lewis Hahn, Sanjeet Hegde, Andrew D McCulloch, Francesca Raimondi, Albert Hsiao","doi":"10.1016/j.jocmr.2025.101886","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Patients with repaired tetralogy of Fallot (rTOF) are commonly followed with cardiovascular magnetic resonance (CMR) imaging and frequently develop right ventricular (RV) dysfunction, which can be severe enough to impact left ventricular (LV) function in some patients. In this study, we sought to characterize patterns of LV dysfunction in this patient population using deep learning synthetic strain (DLSS), a fully automated deep learning algorithm capable of measuring regional LV strain and dyssynchrony.</p><p><strong>Methods: </strong>We retrospectively collected cine steady-state free precession (SSFP) MRI images from a multi-institutional cohort of 198 patients with rTOF and 21 healthy controls. Using DLSS, we measured LV strain and strain rate across 16 American Heart Association segments from short-axis cine SSFP images and compared these values to controls. We then performed a clustering analysis to identify unique patterns of LV contraction, using segmental peak strain and several measures of dyssynchrony. We further characterized these patterns by assessing their relationship to traditional MRI metrics of volume and function. Lastly, we assessed their impact on subsequent progression to pulmonary valve replacement (PVR) through a multivariate analysis.</p><p><strong>Results: </strong>Overall, patients with rTOF had decreased septal radial strain, increased lateral wall radial strain, and increased dyssynchrony relative to healthy controls. Clustering of rTOF patients identified four unique patterns of LV contraction. Most notably, patients in cluster 1 (n = 39) demonstrated an LV contraction pattern with paradoxical septal wall motion and severely reduced septal strain. These patients had significantly elevated RV end-diastolic volume relative to clusters 3 and 4 (153 ± 34 vs 127 ± 34 and 126 ± 31 mL/m<sup>2</sup>, analysis of variance p < 0.01). In the multivariate analysis, this contraction pattern was the only LV metric associated with future progression to PVR (heart rate = 2.69, p < 0.005). A smaller subset of patients (cluster 2, n = 29) showed reduced septal strain and LV ejection fraction despite synchronous ventricular contraction.</p><p><strong>Conclusion: </strong>Patients with rTOF demonstrate four unique patterns of LV dysfunction. Most commonly, but not exclusively, LV dysfunction is characterized by septal wall motion abnormalities and severely reduced septal strain. Patients with this pattern of LV dysfunction had concomitant RV dysfunction and rapid progression to PVR.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":" ","pages":"101886"},"PeriodicalIF":6.1000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12182814/pdf/","citationCount":"0","resultStr":"{\"title\":\"Characteristics of left ventricular dysfunction in repaired tetralogy of Fallot: A multi-institutional deep learning analysis of regional strain and dyssynchrony.\",\"authors\":\"Brendan T Crabb, Rahul S Chandrupatla, Evan M Masutani, Sophie Y Wong, Sachin Govil, Silvia Montserrat, Susana Prat-González, Julián Vega-Adauy, Melany Atkins, Daniel Lorenzatti, Chiara Zocchi, Elena Panaioli, Nathalie Boddaert, Laith Alshawabkeh, Lewis Hahn, Sanjeet Hegde, Andrew D McCulloch, Francesca Raimondi, Albert Hsiao\",\"doi\":\"10.1016/j.jocmr.2025.101886\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Patients with repaired tetralogy of Fallot (rTOF) are commonly followed with cardiovascular magnetic resonance (CMR) imaging and frequently develop right ventricular (RV) dysfunction, which can be severe enough to impact left ventricular (LV) function in some patients. In this study, we sought to characterize patterns of LV dysfunction in this patient population using deep learning synthetic strain (DLSS), a fully automated deep learning algorithm capable of measuring regional LV strain and dyssynchrony.</p><p><strong>Methods: </strong>We retrospectively collected cine steady-state free precession (SSFP) MRI images from a multi-institutional cohort of 198 patients with rTOF and 21 healthy controls. Using DLSS, we measured LV strain and strain rate across 16 American Heart Association segments from short-axis cine SSFP images and compared these values to controls. We then performed a clustering analysis to identify unique patterns of LV contraction, using segmental peak strain and several measures of dyssynchrony. We further characterized these patterns by assessing their relationship to traditional MRI metrics of volume and function. Lastly, we assessed their impact on subsequent progression to pulmonary valve replacement (PVR) through a multivariate analysis.</p><p><strong>Results: </strong>Overall, patients with rTOF had decreased septal radial strain, increased lateral wall radial strain, and increased dyssynchrony relative to healthy controls. Clustering of rTOF patients identified four unique patterns of LV contraction. Most notably, patients in cluster 1 (n = 39) demonstrated an LV contraction pattern with paradoxical septal wall motion and severely reduced septal strain. These patients had significantly elevated RV end-diastolic volume relative to clusters 3 and 4 (153 ± 34 vs 127 ± 34 and 126 ± 31 mL/m<sup>2</sup>, analysis of variance p < 0.01). In the multivariate analysis, this contraction pattern was the only LV metric associated with future progression to PVR (heart rate = 2.69, p < 0.005). A smaller subset of patients (cluster 2, n = 29) showed reduced septal strain and LV ejection fraction despite synchronous ventricular contraction.</p><p><strong>Conclusion: </strong>Patients with rTOF demonstrate four unique patterns of LV dysfunction. Most commonly, but not exclusively, LV dysfunction is characterized by septal wall motion abnormalities and severely reduced septal strain. 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引用次数: 0
摘要
背景:修复性法洛四联症(rTOF)患者通常进行MRI随访,并经常出现右心室(RV)功能障碍,其严重程度足以影响某些患者的左心室(LV)功能。在这项研究中,我们试图使用深度学习合成应变(DLSS)来表征该患者群体中左室功能障碍的模式,这是一种能够测量区域左室应变和不同步的全自动深度学习算法。方法:我们回顾性收集了198例rTOF患者和21名健康对照者的多机构队列的SSFP MRI图像。使用DLSS,我们测量了来自短轴电影SSFP图像的16个AHA片段的左室应变和应变率,并将这些值与对照组进行了比较。然后,我们使用节段峰值应变和几种不同步测量方法进行聚类分析,以确定左室收缩的独特模式。我们通过评估它们与传统MRI体积和功能指标的关系进一步表征了这些模式。最后,我们通过多变量分析评估了它们对肺瓣膜置换术(PVR)后续进展的影响。结果:总的来说,与健康对照相比,rTOF患者的室间隔径向应变降低,侧壁径向应变增加,不同步运动增加。rTOF患者的聚类确定了四种独特的左室收缩模式。最值得注意的是,第1组患者(n=39)表现出左室收缩模式,伴有矛盾的间隔壁运动和严重减少的间隔张力。与聚类3和聚类4相比,这些患者的左室舒张末期容积显著升高(153±34 vs 127±34和126±31mL/m2)。结论:rTOF患者表现出四种独特的左室功能障碍。最常见的,但不是唯一的,左室功能障碍的特征是室间隔壁运动异常和严重减少的室间隔张力。这种类型的左室功能障碍患者伴有左室功能障碍,并迅速发展为PVR。
Characteristics of left ventricular dysfunction in repaired tetralogy of Fallot: A multi-institutional deep learning analysis of regional strain and dyssynchrony.
Background: Patients with repaired tetralogy of Fallot (rTOF) are commonly followed with cardiovascular magnetic resonance (CMR) imaging and frequently develop right ventricular (RV) dysfunction, which can be severe enough to impact left ventricular (LV) function in some patients. In this study, we sought to characterize patterns of LV dysfunction in this patient population using deep learning synthetic strain (DLSS), a fully automated deep learning algorithm capable of measuring regional LV strain and dyssynchrony.
Methods: We retrospectively collected cine steady-state free precession (SSFP) MRI images from a multi-institutional cohort of 198 patients with rTOF and 21 healthy controls. Using DLSS, we measured LV strain and strain rate across 16 American Heart Association segments from short-axis cine SSFP images and compared these values to controls. We then performed a clustering analysis to identify unique patterns of LV contraction, using segmental peak strain and several measures of dyssynchrony. We further characterized these patterns by assessing their relationship to traditional MRI metrics of volume and function. Lastly, we assessed their impact on subsequent progression to pulmonary valve replacement (PVR) through a multivariate analysis.
Results: Overall, patients with rTOF had decreased septal radial strain, increased lateral wall radial strain, and increased dyssynchrony relative to healthy controls. Clustering of rTOF patients identified four unique patterns of LV contraction. Most notably, patients in cluster 1 (n = 39) demonstrated an LV contraction pattern with paradoxical septal wall motion and severely reduced septal strain. These patients had significantly elevated RV end-diastolic volume relative to clusters 3 and 4 (153 ± 34 vs 127 ± 34 and 126 ± 31 mL/m2, analysis of variance p < 0.01). In the multivariate analysis, this contraction pattern was the only LV metric associated with future progression to PVR (heart rate = 2.69, p < 0.005). A smaller subset of patients (cluster 2, n = 29) showed reduced septal strain and LV ejection fraction despite synchronous ventricular contraction.
Conclusion: Patients with rTOF demonstrate four unique patterns of LV dysfunction. Most commonly, but not exclusively, LV dysfunction is characterized by septal wall motion abnormalities and severely reduced septal strain. Patients with this pattern of LV dysfunction had concomitant RV dysfunction and rapid progression to PVR.
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.