AIMC Topic: Heart
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Computationally Efficient Implicit Training Strategy for Unrolled Networks (IMUNNE): A Preliminary Analysis Using Accelerated Real-Time Cardiac Cine MRI.
IEEE transactions on bio-medical engineering
Jan 15, 2025
OBJECTIVE: Highly-undersampled, dynamic MRI reconstruction, particularly in multi-coil scenarios, is a challenging inverse problem. Unrolled networks achieve state-of-the-art performance in MRI reconstruction but suffer from long training times and e...
Cardiac MR image reconstruction using cascaded hybrid dual domain deep learning framework.
PloS one
Jan 10, 2025
Recovering diagnostic-quality cardiac MR images from highly under-sampled data is a current research focus, particularly in addressing cardiac and respiratory motion. Techniques such as Compressed Sensing (CS) and Parallel Imaging (pMRI) have been pr...
FDDSeg: Unleashing the Power of Scribble Annotation for Cardiac MRI Images Through Feature Decomposition Distillation.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Cardiovascular diseases can be diagnosed with computer assistance when using the magnetic resonance imaging (MRI) image that is produced by the MRI sensor. Deep learning-based scribbling MRI image segmentation has demonstrated impressive results rece...
Deep learning in 3D cardiac reconstruction: a systematic review of methodologies and dataset.
Medical & biological engineering & computing
Jan 4, 2025
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employ...
Hybrid deep learning for computational precision in cardiac MRI segmentation: Integrating Autoencoders, CNNs, and RNNs for enhanced structural analysis.
Computers in biology and medicine
Jan 1, 2025
Recent advancements in cardiac imaging have been significantly enhanced by integrating deep learning models, offering transformative potential in early diagnosis and patient care. The research paper explores the application of hybrid deep learning me...
Generalizability, robustness, and correction bias of segmentations of thoracic organs at risk in CT images.
European radiology
Dec 31, 2024
OBJECTIVE: This study aims to assess and compare two state-of-the-art deep learning approaches for segmenting four thoracic organs at risk (OAR)-the esophagus, trachea, heart, and aorta-in CT images in the context of radiotherapy planning.
Computerized classification method for significant coronary artery stenosis on whole-heart coronary MRA using 3D convolutional neural networks with attention mechanisms.
Radiological physics and technology
Dec 31, 2024
This study aims to develop a computerized classification method for significant coronary artery stenosis on whole-heart coronary magnetic resonance angiography (WHCMRA) images using a 3D convolutional neural network (3D-CNN) with attention mechanisms...
Attention incorporated network for sharing low-rank, image and k-space information during MR image reconstruction to achieve single breath-hold cardiac Cine imaging.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dec 28, 2024
Cardiac Cine Magnetic Resonance Imaging (MRI) provides an accurate assessment of heart morphology and function in clinical practice. However, MRI requires long acquisition times, with recent deep learning-based methods showing great promise to accele...
Automated measurement of cardiothoracic ratio based on semantic segmentation integration model using deep learning.
Medical & biological engineering & computing
Dec 21, 2024
The objective of this study is to investigate the efficacy of the semantic segmentation model in predicting cardiothoracic ratio (CTR) and heart enlargement and compare its consistency with the reference standard. A total of 650 consecutive chest rad...
Reliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function.
Magnetic resonance imaging
Dec 19, 2024
OBJECTIVE: The total examination time can be reduced if high-quality two-dimensional (2D) cine images can be collected post-contrast to minimize non-scanning time prior to late gadolinium-enhanced imaging. This study aimed to assess the equivalency o...
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