2 citations · 2 across the 7 of their papers we have counts for
7 papers
OpenMRF: A Modular, Vendor-Neutral Open-Source Framework for Reproducible Magnetic Resonance Fingerprinting using Pulseq
Tom Griesler, Jannik Stebani, Sydney Kaplan +15
Purpose: Widespread adoption and methodological advancement of Magnetic Resonance Fingerprinting (MRF) are limited by the lack of unified, reproducible implementation frameworks an…
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…
Representation of Classical Data on Quantum Computers
Thomas Lang, Anja Heim, Kilian Dremel +6
Quantum computing is currently gaining significant attention, not only from the academic community but also from industry, due to its potential applications across several fields f…
Controlling sharpness, SNR and SAR for 3D FSE at 7T by end-to-end learning
Peter Dawood, Martin Blaimer, Jürgen Herrler +5
Purpose: To non-heuristically identify dedicated variable flip angle (VFA) schemes optimized for the point-spread function (PSF) and signal-to-noise ratio (SNR) of multiple tissues…
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…
Rotary Excitation of non-sinusoidal pulsed magnetic fields: Towards non-invasive direct detection of cardiac conduction
Petra Albertova, Maximilian Gram, Martin Blaimer +3
Purpose: In the recent past, spin-locking MRI experiments were successfully applied for the direct detection of sinusoidal magnetic field oscillations in the sub-nT range. In the p…
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…