Generative adversarial networks in medical image augmentation: a review
Object With the development of deep learning, the number of training samples for medical
image-based diagnosis and treatment models is increasing. Generative Adversarial …
image-based diagnosis and treatment models is increasing. Generative Adversarial …
Artificial Intelligence for Cardiovascular Care—Part 1: Advances: JACC Review Topic of the Week
P Elias, SS Jain, T Poterucha, M Randazzo… - Journal of the American …, 2024 - jacc.org
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential
enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on …
enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on …
Physics-driven synthetic data learning for biomedical magnetic resonance: The imaging physics-based data synthesis paradigm for artificial intelligence
Deep learning (DL) has driven innovation in the field of computational imaging. One of its
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
Cardiac magnetic resonance radiomics for disease classification
X Zhang, C Cui, S Zhao, L Xie, Y Tian - European Radiology, 2023 - Springer
Objectives This study investigated the discriminability of quantitative radiomics features
extracted from cardiac magnetic resonance (CMR) images for hypertrophic cardiomyopathy …
extracted from cardiac magnetic resonance (CMR) images for hypertrophic cardiomyopathy …
DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning
3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for
the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current …
the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current …
[HTML][HTML] Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance
Background Exercise cardiovascular magnetic resonance (Ex-CMR) myocardial tagging
would enable quantification of myocardial deformation after exercise. However, current …
would enable quantification of myocardial deformation after exercise. However, current …
Mesh-based 3d motion tracking in cardiac mri using deep learning
Abstract 3D motion estimation from cine cardiac magnetic resonance (CMR) images is
important for the assessment of cardiac function and diagnosis of cardiovascular diseases …
important for the assessment of cardiac function and diagnosis of cardiovascular diseases …
Physics‐informed deep learning for T2‐deblurred superresolution turbo spin echo MRI
Purpose Deep learning superresolution (SR) is a promising approach to reduce MRI scan
time without requiring custom sequences or iterative reconstruction. Previous deep learning …
time without requiring custom sequences or iterative reconstruction. Previous deep learning …
Myocardial segmentation of tagged magnetic resonance images with transfer learning using generative cine-to-tagged dataset transformation
The use of deep learning (DL) segmentation in cardiac MRI has the potential to streamline
the radiology workflow, particularly for the measurement of myocardial strain. Recent efforts …
the radiology workflow, particularly for the measurement of myocardial strain. Recent efforts …
High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning
H Huang, Q Yang, J Wang, P Zhang… - Physics in Medicine & …, 2023 - iopscience.iop.org
Objective. Bloch simulation constitutes an essential part of magnetic resonance imaging
(MRI) development. However, even with the graphics processing unit (GPU) acceleration …
(MRI) development. However, even with the graphics processing unit (GPU) acceleration …