Comparison of Biplane Area-Length Method and 3D Volume Quantification                     by Using Cardiac MRI for Assessment of Left Atrial Volume in Atrial                     Fibrillation

Maroun, Anthony; Baraboo, Justin; Gunasekaran, Suvai; Hwang, Julia M.; Liu, Sophia Z.; Passman, Rod; Kim, Daniel; Allen, Bradley D.; Markl, Michael; Pradella, Maurice

doi:10.1148/ryct.220133

Radiology: Cardiothoracic Imaging

2023

DOI: 10.1148/ryct.220133

|View full text |Cite

Comparison of Biplane Area-Length Method and 3D Volume Quantification by Using Cardiac MRI for Assessment of Left Atrial Volume in Atrial Fibrillation

Anthony Maroun

Justin Baraboo

Suvai Gunasekaran

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short- and long-axis analyses in UK Biobank

Muffoletto,

Xu,

Burns

et al. 2024

European Heart Journal - Cardiovascular Imaging

View full text Add to dashboard Cite

Aims Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank. Methods A deep learning network was adapted to predict 3D segmentations of left and right ventricles (LV, RV) and atria (LA, RA) at ∼1mm isotropic resolution from CMR short and long axis 2D segmentations obtained from a fully automated machine learning pipeline in 4723 individuals with cardiovascular disease (CVD) and 5733 without in the UK Biobank. Relationships between volumes at end-diastole (ED) and end-systole (ES) and risk/disease factors were quantified using univariate, multivariate and logistic regression analyses. Strength of association between deep learning volumes and standard volumes was compared using the area under the receiving operator characteristic curve (AUC). Results Univariate and multivariate associations between deep learning volumes and most risk and disease factors were stronger than for standard volumes (higher R2 and more significant P values), particularly for sex, age, and body mass index. AUC for all logistic regressions were higher for deep learning volumes than standard volumes (p<0.001 for all four chambers at ED and ES). Conclusions Neural network reconstructions of whole heart volumes had significantly stronger associations with cardiovascular disease and risk factors than standard volume estimation methods in an automatic processing pipeline.

show abstract

Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short- and long-axis analyses in UK Biobank

Muffoletto,

Xu,

Burns

et al. 2024

European Heart Journal - Cardiovascular Imaging

View full text Add to dashboard Cite

show abstract

Associations between 3D-based Left Atrial Volumetric and Blood Flow Parameters in a Single-Site Cohort of the Multi-Ethnic Study of Atherosclerosis

Pradella,

Baraboo,

Maroun

et al. 2024

Radiology: Cardiothoracic Imaging

View full text Add to dashboard Cite

Radiology: Cardiothoracic Imaging Highlights 2023

Aquino,

Mastrodicasa,

Alabed

et al. 2024

Radiology: Cardiothoracic Imaging

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Comparison of Biplane Area-Length Method and 3D Volume Quantification by Using Cardiac MRI for Assessment of Left Atrial Volume in Atrial Fibrillation

Cited by 4 publications

References 28 publications

Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short- and long-axis analyses in UK Biobank

Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short- and long-axis analyses in UK Biobank

Associations between 3D-based Left Atrial Volumetric and Blood Flow Parameters in a Single-Site Cohort of the Multi-Ethnic Study of Atherosclerosis

Radiology: Cardiothoracic Imaging Highlights 2023

Contact Info

Product

Resources

About