AIMC Topic: Magnetic Resonance Imaging, Cine
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Utilizing Artificial Intelligence-Based Deformable Registration for Global and Layer-Specific Cardiac MRI Strain Analysis in Healthy Children and Young Adults.
Academic radiology
Jan 4, 2024
RATIONALE AND OBJECTIVES: The absence of published reference values for multilayer-specific strain measurement using cardiac magnetic resonance (CMR) in young healthy individuals limits its use. This study aimed to establish normal global and layer-s...
AI approach to biventricular function assessment in cine-MRI: an ultra-small training dataset and multivendor study.
Physics in medicine and biology
Dec 13, 2023
. It was a great challenge to train an excellent and generalized model on an ultra-small data set composed of multi-orientation cardiac cine magnetic resonance imaging (MRI) images. We try to develop a 3D deep learning method based on an ultra-small ...
High-resolution spiral real-time cardiac cine imaging with deep learning-based rapid image reconstruction and quantification.
NMR in biomedicine
Nov 5, 2023
The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) and deep learning (DL)-based segmentation approach to quantify the left ventricular ejection fraction (LVEF) for high-reso...
Accelerated Cine Cardiac MRI Using Deep Learning-Based Reconstruction: A Systematic Evaluation.
Journal of magnetic resonance imaging : JMRI
Oct 19, 2023
BACKGROUND: Breath-holding (BH) for cine balanced steady state free precession (bSSFP) imaging is challenging for patients with impaired BH capacity. Deep learning-based reconstruction (DLR) of undersampled k-space promises to shorten BHs while prese...
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis.
IEEE journal of biomedical and health informatics
Oct 5, 2023
Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic ...
Comparison of manual and artificial intelligence based quantification of myocardial strain by feature tracking-a cardiovascular MR study in health and disease.
European radiology
Aug 18, 2023
OBJECTIVES: The analysis of myocardial deformation using feature tracking in cardiovascular MR allows for the assessment of global and segmental strain values. The aim of this study was to compare strain values derived from artificial intelligence (A...
Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification.
Academic radiology
Aug 3, 2023
RATIONALE AND OBJECTIVES: Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual segmentation can lead to observer bias. Deep learning (DL) has been proposed for automated cardiac s...
Siamese pyramidal deep learning network for strain estimation in 3D cardiac cine-MR.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Aug 1, 2023
Strain represents the quantification of regional tissue deformation within a given area. Myocardial strain has demonstrated considerable utility as an indicator for the assessment of cardiac function. Notably, it exhibits greater sensitivity in detec...
From Compressed-Sensing to Deep Learning MR: Comparative Biventricular Cardiac Function Analysis in a Patient Cohort.
Journal of magnetic resonance imaging : JMRI
Jul 12, 2023
BACKGROUND: Conventional segmented, retrospectively gated cine (Conv-cine) is challenged in patients with breath-hold difficulties. Compressed sensing (CS) has shown values in cine imaging but generally requires long reconstruction time. Recent artif...
Deep learning-based prognostic model using non-enhanced cardiac cine MRI for outcome prediction in patients with heart failure.
European radiology
Jun 7, 2023
OBJECTIVES: To evaluate the performance of a deep learning-based multi-source model for survival prediction and risk stratification in patients with heart failure.
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