86 citations · 170 across the 11 of their papers we have counts for
11 papers
A Deep Learning Approach Utilizing Covariance Matrix Analysis for the ISBI Edited MRS Reconstruction Challenge
Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab +5
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 covaria…
A Review of Machine Learning Applications for the Proton Magnetic Resonance Spectroscopy Workflow
Dennis M. J. van de Sande, Julian P. Merkofer, Sina Amirrajab +6
This literature review presents a comprehensive overview of machine learning (ML) applications in proton magnetic resonance spectroscopy (MRS). As the use of ML techniques in MRS c…
Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs
Sina Amirrajab, Cristian Lorenz, Juergen Weese +2
We propose a method for synthesizing cardiac magnetic resonance (MR) images with plausible heart pathologies and realistic appearances for the purpose of generating labeled data fo…
sim2real: Cardiac MR Image Simulation-to-Real Translation via Unsupervised GANs
Sina Amirrajab, Yasmina Al Khalil, Cristian Lorenz +3
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. However,…
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…
Physics-informed neural networks for myocardial perfusion MRI quantification
Rudolf L. M. van Herten, Amedeo Chiribiri, Marcel Breeuwer +2
Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data…