14 citations · 18 across the 6 of their papers we have counts for
8 papers
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,…
Machine Learning for Quantitative MR Image Reconstruction
Andreas Kofler, Felix Frederik Zimmermann, Kostas Papafitsoros
In the last years, the design of image reconstruction methods in the field of quantitative Magnetic Resonance Imaging (qMRI) has experienced a paradigm shift. Often, when dealing w…
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…
Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction
Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba +6
We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based o…
Learning Regularization Parameter-Maps for Variational Image Reconstruction using Deep Neural Networks and Algorithm Unrolling
Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba +6
We introduce a method for fast estimation of data-adapted, spatio-temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variati…