18 citations · 55 across the 16 of their papers we have counts for
17 papers
INR meets Multi-Contrast MRI Reconstruction
Natascha Niessen, Carolin M. Pirkl, Ana Beatriz Solana +6
Multi-contrast MRI sequences allow for the acquisition of images with varying tissue contrast within a single scan. The resulting multi-contrast images can be used to extract quant…
StoDIP: Efficient 3D MRF image reconstruction with deep image priors and stochastic iterations
Perla Mayo, Matteo Cencini, Carolin M. Pirkl +4
Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI for multiparametric tissue mapping. The reconstruction of quantitative maps requires tailor…
Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders
Perla Mayo, Matteo Cencini, Ketan Fatania +5
The estimation of multi-parametric quantitative maps from Magnetic Resonance Fingerprinting (MRF) compressed sampled acquisitions, albeit successful, remains a challenge due to the…
High-Dimensional Confidence Regions in Sparse MRI
Frederik Hoppe, Felix Krahmer, Claudio Mayrink Verdun +2
One of the most promising solutions for uncertainty quantification in high-dimensional statistics is the debiased LASSO that relies on unconstrained -minimization. The init…
Uncertainty quantification for sparse Fourier recovery
Frederik Hoppe, Felix Krahmer, Claudio Mayrink Verdun +2
One of the most prominent methods for uncertainty quantification in high-dimen-sional statistics is the desparsified LASSO that relies on unconstrained -minimization. The m…
Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI
Ketan Fatania, Kwai Y. Chau, Carolin M. Pirkl +3
Current state-of-the-art reconstruction for quantitative tissue maps from fast, compressive, Magnetic Resonance Fingerprinting (MRF), use supervised deep learning, with the drawbac…