DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics
Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data provides a more thorough characterization of cardiac mechanics than volumetric parameters such as left-ventricular ejection fraction, but sources of variation including segmentation and motion estimation have limite...
Main Authors: | Manuel A. Morales, Maaike van den Boomen, Christopher Nguyen, Jayashree Kalpathy-Cramer, Bruce R. Rosen, Collin M. Stultz, David Izquierdo-Garcia, Ciprian Catana |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-09-01
|
Series: | Frontiers in Cardiovascular Medicine |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2021.730316/full |
Similar Items
-
DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics
by: Morales, Manuel A, et al.
Published: (2022) -
DeepStrain Evidence of Asymptomatic Left Ventricular Diastolic and Systolic Dysfunction in Young Adults With Cardiac Risk Factors
by: Morales, Manuel A., et al.
Published: (2022) -
Deep Learning for Cardiovascular Risk Stratification
by: Schlesinger, Daphne E., et al.
Published: (2021) -
Characterization of Exercise-Induced Myocardium Growth Using Finite Element Modeling and Bayesian Optimization
by: Yiling Fan, et al.
Published: (2021-08-01) -
The effect of a DeltaK280 mutation on the unfolded state of a microtubule-binding repeat in Tau.
by: Austin Huang, et al.
Published: (2008-01-01)