14 citations · 18 across the 6 of their papers we have counts for
5 papers · 1 filter
Physics-informed denoising method for image reconstruction in quantitative low-field MRI
Catarina Redshaw Kranich, Claudia Prieto, Christoph Kolbitsch +1
Low-field magnetic resonance imaging (MRI) is becoming increasingly important for medical imaging because it can reduce healthcare costs while ensuring high diagnostic output. Neve…
Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement
Felix F Zimmermann
Ultra-low-field (ULF) MRI promises broader accessibility but suffers from low signal-to-noise ratio (SNR), reduced spatial resolution, and contrasts that deviate from high-field st…
MRpro: open framework for model-based, learned, and quantitative MR imaging
Felix Frederik Zimmermann, Patrick Schuenke, Christoph S. Aigner +12
We preseent an open-source image reconstruction package built upon PyTorch, enabling modern deep-learning reconstructions. It uses open data formats for input and output (ISMRMRD,…
NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity maps
Felix Frederik Zimmermann, Andreas Kofler
We present a novel learned image reconstruction method for accelerated cardiac MRI with multiple receiver coils based on deep convolutional neural networks (CNNs) and algorithm unr…
PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction
Felix F Zimmermann, Christoph Kolbitsch, Patrick Schuenke +1
Quantitative Magnetic Resonance Imaging (qMRI) enables the reproducible measurement of biophysical parameters in tissue. The challenge lies in solving a nonlinear, ill-posed invers…