ArticleScientific reports2020
A New Framework for Performing Cardiac Strain Analysis from Cine MRI Imaging in Mice.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 43 citations in OpenAlex.
- MyoNet: Deep Learning-Based Myocardial Strain Quantification from Cine Cardiac MRI.Bioengineering (Basel, Switzerland) · 2026Article
- Automated cardiac MRI analysis for robust profiling of heart failure models in mice.Scientific reports · 2025Article
- Complete spatiotemporal quantification of cardiac motion in mice through multi-view magnetic resonance imaging and super-resolution reconstruction.Scientific reports · 2025Article
- Comparison of Myocardial Function in Young and Old Mice During Acute Myocardial Infarction: A Cardiac Magnetic Resonance Study.Diagnostics (Basel, Switzerland) · 2025Article
- Validation of in Vivo Myocardial Strain with PET Derived Feature Tracking: Direct Comparison with Tagging-Cine Magnetic Resonance.Annals of nuclear cardiology · 2025Article
- In-silico heart model phantom to validate cardiac strain imaging.Computers in biology and medicine · 2024Article
- In-silico heart model phantom to validate cardiac strain imaging.bioRxiv : the preprint server for biology · 2024Article
- Localized strain characterization of cardiomyopathy in Duchenne muscular dystrophy using novel 4D kinematic analysis of cine cardiovascular magnetic resonance.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- Multi-Stage Classification-Based Deep Learning for Gleason System Grading Using Histopathological Images.Cancers · 2022Article
- Biomedical Imaging in Experimental Models of Cardiovascular Disease.Circulation research · 2022Review
- Towards fully automated segmentation of rat cardiac MRI by leveraging deep learning frameworks.Scientific reports · 2022Article
- Myocardial strain imaging in Duchenne muscular dystrophy.Frontiers in cardiovascular medicine · 2022Review
- A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens.Sensors (Basel, Switzerland) · 2021Article
- Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2021Article
- DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics.Frontiers in cardiovascular medicine · 2021Article
- Open-source pre-clinical image segmentation: mouse cardiac magnetic resonance imaging datasets with a deep learning segmentation framework.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
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Authors and funding
13 authors at 2 institutions in 2 countries.
Funding
Abstract
Cardiac magnetic resonance (MR) imaging is one of the most rigorous form of imaging to assess cardiac function in vivo. Strain analysis allows comprehensive assessment of diastolic myocardial function, which is not indicated by measuring systolic functional parameters using with a normal cine imaging module. Due to the small heart size in mice, it is not possible to perform proper tagged imaging to assess strain. Here, we developed a novel deep learning approach for automated quantification of strain from cardiac cine MR images. Our framework starts by an accurate localization of the LV blood pool center-point using a fully convolutional neural network (FCN) architecture. Then, a region of interest (ROI) that contains the LV is extracted from all heart sections. The extracted ROIs are used for the segmentation of the LV cavity and myocardium via a novel FCN architecture. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. Furthermore, we demonstrated that our new algorithm can determine the strain differences between normal and diseased hearts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.