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Found 10 papers, 4 papers with code
A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge
This work proposes a method to accelerate the acquisition of high-quality edited magnetic resonance spectroscopy (MRS) scans using machine learning models taking the sample covariance matrix as input.
Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs
We devise three approaches for label manipulation in the latent space of the trained VAE model; i) \textbf{intra-subject synthesis} aiming to interpolate the intermediate slices of a subject to increase the through-plane resolution, ii) \textbf{inter-subject synthesis} aiming to interpolate the geometry and appearance of intermediate images between two dissimilar subjects acquired with different scanner vendors, and iii) \textbf{pathology synthesis} aiming to synthesize a series of pseudo-pathological synthetic subjects with characteristics of a desired heart disease.
sim2real: Cardiac MR Image Simulation-to-Real Translation via Unsupervised GANs
There has been considerable interest in the MR physics-based simulation of a database of virtual cardiac MR images for the development of deep-learning analysis networks.
Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentation
The mean DSC per-subject on the challenge test set, for the cascaded pipeline augmented by synthetic generated data, was 0. 86 (0. 03) and 0. 67 (0. 29) for myocardium and scar, respectively.
Physics-informed neural networks for myocardial perfusion MRI quantification
This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification, which provides a versatile scheme for the inference of kinetic parameters.
XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms
Moreover, the improvement in utilizing synthetic images for augmenting the real data is evident through the reduction of Hausdorff distance up to 28% and an increase in the Dice score up to 5%, indicating a higher similarity to the ground truth in all dimensions.
4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model
Using the parameterized motion model of the XCAT heart, we generate labels for 25 time frames of the heart for one cardiac cycle at 18 locations for the short axis view.
Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI
The quantification of myocardial perfusion MRI has the potential to provide a fast, automated and user-independent assessment of myocardial ischaemia.
Hierarchical Bayesian myocardial perfusion quantification
Purpose: Tracer-kinetic models can be used for the quantitative assessment of contrast-enhanced MRI data.
Invertible Orientation Scores of 3D Images
The enhancement and detection of elongated structures in noisy image data is relevant for many biomedical applications.
