AIMC Topic: Heart Ventricles
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Accelerated Cardiac MRI with Deep Learning-based Image Reconstruction for Cine Imaging.
Radiology. Cardiothoracic imaging
Dec 1, 2024
Purpose To assess the influence of deep learning (DL)-based image reconstruction on acquisition time, volumetric results, and image quality of cine sequences in cardiac MRI. Materials and Methods This prospective study (performed from January 2023 to...
Early Detection of Left Ventricular Dysfunction With Machine Learning-Based Strain Imaging in Aortic Stenosis Patients.
Echocardiography (Mount Kisco, N.Y.)
Nov 1, 2024
PURPOSE: Aortic stenosis (AS) is a common cardiovascular condition where early detection of left ventricular (LV) dysfunction is essential for timely intervention and optimal management. Current echocardiographic measurements, such as ejection fracti...
Structure Preserving Cycle-Gan for Unsupervised Medical Image Domain Adaptation.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
The presence of domain shift in medical imaging is a common issue, which can greatly impact the performance of segmentation models when dealing with unseen image domains. This work introduces the Structure Preserving Cycle-GAN (SP Cycle-GAN) for unsu...
CBAM_SAUNet: A novel attention U-Net for effective segmentation of corner cases.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
U-Net has been demonstrated to be effective for the task of medical image segmentation. Additionally, integrating attention mechanism into U-Net has been shown to yield significant benefits. The Shape Attentive U-Net (SAUNet) is one such recently pro...
Deep Left Ventricular Motion Estimation Methods in Echocardiography: A Comparative Study.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Motion estimation in echocardiography is critical when assessing heart function and calculating myocardial deformation indices. Nevertheless, there are limitations in clinical practice, particularly with regard to the accuracy and reliability of meas...
A DenseNet-based Abnormal Ventricular Potentials Onset Delineation: A Feasibility Study.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Abnormal ventricular potentials (AVPs) are fractionated and complex electrograms (EGMs), typically associated with slow conduction areas in the myocardium. As such, in ventricular tachycardia (VT), their identification supports the localization of th...
A deep learning solution to detect left ventricular structural abnormalities with chest X-rays: towards trustworthy AI in cardiology.
European heart journal
Jun 7, 2024
Deep learning to detect left ventricular structural abnormalities in chest X-rays.
European heart journal
Jun 7, 2024
BACKGROUND AND AIMS: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportuni...
Deep learning for automatic volumetric segmentation of left ventricular myocardium and ischaemic scar from multi-slice late gadolinium enhancement cardiovascular magnetic resonance.
European heart journal. Cardiovascular Imaging
May 31, 2024
AIMS: This study details application of deep learning for automatic volumetric segmentation of left ventricular (LV) myocardium and scar and automated quantification of myocardial ischaemic scar burden from late gadolinium enhancement cardiovascular ...
In the right direction: A deep learning tool for assessment of right ventricular function.
Echocardiography (Mount Kisco, N.Y.)
May 1, 2024
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