3 papers
physics.med-ph2026
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…
cs.CV2025
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…
cs.CV2025
Tumor likelihood estimation on MRI prostate data by utilizing k-Space information
M. Rempe, F. Hörst, C. Seibold +7
We present a novel preprocessing and prediction pipeline for the classification of magnetic resonance imaging (MRI) that takes advantage of the information rich complex valued k-Sp…