22 citations · 22 across the 2 of their papers we have counts for
5 papers · 1 filter
Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentation
Didier R. P. R. M. Lustermans, Sina Amirrajab, Mitko Veta +2
Background: The clinical utility of late gadolinium enhancement (LGE) cardiac MRI is limited by the lack of standardization, and time-consuming postprocessing. In this work, we tes…
XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms
Sina Amirrajab, Samaneh Abbasi-Sureshjani, Yasmina Al Khalil +4
Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with…
4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model
Samaneh Abbasi-Sureshjani, Sina Amirrajab, Cristian Lorenz +3
We propose a hybrid controllable image generation method to synthesize anatomically meaningful 3D+t labeled Cardiac Magnetic Resonance (CMR) images. Our hybrid method takes the mec…
Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI
Cian M. Scannell, Piet van den Bosch, Amedeo Chiribiri +3
The quantification of myocardial perfusion MRI has the potential to provide a fast, automated and user-independent assessment of myocardial ischaemia. However, due to the relativel…
Hierarchical Bayesian myocardial perfusion quantification
Cian M. Scannell, Amedeo Chiribiri, Adriana D. M. Villa +2
Purpose: Tracer-kinetic models can be used for the quantitative assessment of contrast-enhanced MRI data. However, the model-fitting can produce unreliable results due to the limit…