2 citations · 3 across the 4 of their papers we have counts for
4 papers
Controlling spatial correlation in k-space interpolation networks for MRI reconstruction: denoising versus apparent blurring
Istvan Homolya, Jannik Stebani, Felix Breuer +4
Purpose: Interpretability is essential for the clinical adoption of state-of-the-art machine learning (ML) methods in magnetic resonance imaging (MRI). Conventional evaluation of M…
Phantom-based gradient waveform measurements with compensated variable-prephasing: Description and application to EPI at 7T
Hannah Scholten, Tobias Wech, Istvan Homolya +1
Purpose: Introducing "compensated variable-prephasing" (CVP), a phantom-based method for gradient waveform measurements. The technique is based on the "variable-prephasing" (VP) me…
Image space formalism of convolutional neural networks for k-space interpolation
Peter Dawood, Felix Breuer, Istvan Homolya +4
Purpose: Noise resilience in image reconstructions by scan-specific robust artificial neural networks for k-space interpolation (RAKI) is linked to nonlinear activations in k-space…
Iterative training of robust k-space interpolation networks for improved image reconstruction with limited scan specific training samples
Peter Dawood, Felix Breuer, Paul R. Burd +4
Purpose: To evaluate an iterative learning approach for enhanced performance of Robust Artificial-neural-networks for K-space Interpolation (RAKI), when only a limited amount of tr…