11 citations · 21 across the 12 of their papers we have counts for
13 papers
Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification
Gonzalo Esteban Mosquera Rojas, Sebastian R. van der Voort, Carolin M. Pirkl +3
Uncertainty Quantification (UQ) is a key requirement for trustworthy AI in high-stakes medical image analysis. In this work, we evaluate UQ in a multi-task Deep Learning framework…
Learning to reason about rare diseases through retrieval-augmented agents
Ha Young Kim, Jun Li, Ana Beatriz Solana +4
Rare diseases represent the long tail of medical imaging, where AI models often fail due to the scarcity of representative training data. In clinical workflows, radiologists freque…
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…
Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction
Perla Mayo, Carolin M. Pirkl, Alin Achim +2
We introduce MRF-DiPh, a novel physics informed denoising diffusion approach for multiparametric tissue mapping from highly accelerated, transient-state quantitative MRI acquisitio…
Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting
Perla Mayo, Carolin M. Pirkl, Alin Achim +2
Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI, enabling the mapping of multiple tissue properties from a single, accelerated scan. Howeve…
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…
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…
A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers
Ketan Fatania, Carolin M. Pirkl, Marion I. Menzel +2
Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern whic…
Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting
Carolin M. Pirkl, Pedro A. Gómez, Ilona Lipp +13
Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the match…
Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders
Mohammad Golbabaee, Guido Buonincontri, Carolin Pirkl +4
We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and dee…
Rapid three-dimensional multiparametric MRI with quantitative transient-state imaging
Pedro A. Gómez, Matteo Cencini, Mohammad Golbabaee +8
Novel methods for quantitative, transient-state multiparametric imaging are increasingly being demonstrated for assessment of disease and treatment efficacy. Here, we build on thes…
Deep MR Fingerprinting with total-variation and low-rank subspace priors
Mohammad Golbabaee, Carolin M. Pirkl, Marion I. Menzel +2
Deep learning (DL) has recently emerged to address the heavy storage and computation requirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprinting (MR…