59 citations · 208 across the 29 of their papers we have counts for
33 papers
Diffusion Meets Unrolling: Compressive SAR Image Reconstruction with Interleaved Learned Corrections
Odysseas Pappas, Andrew C. M. Austin, Perla Mayo +2
Compressive Synthetic Aperture Radar (SAR) imaging, typically formulated as an inverse problem and solved with traditional iterative optimisation methods, can be very computational…
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…
Performance of data-driven inner speech decoding with same-task EEG-fMRI data fusion and bimodal models
Holly Wilson, Scott Wellington, Foteini Simistira Liwicki +13
Decoding inner speech from the brain signal via hybridisation of fMRI and EEG data is explored to investigate the performance benefits over unimodal models. Two different bimodal f…
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…