Search Results for author:
Found 16 papers, 5 papers with code
StoDIP: Efficient 3D MRF image reconstruction with deep image priors and stochastic iterations
Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI for multiparametric tissue mapping.
Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders
The estimation of multi-parametric quantitative maps from Magnetic Resonance Fingerprinting (MRF) compressed sampled acquisitions, albeit successful, remains a challenge due to the high underspampling rate and artifacts naturally occuring during image reconstruction.
Performance of data-driven inner speech decoding with same-task EEG-fMRI data fusion and bimodal models
Decoding inner speech from the brain signal via hybridisation of fMRI and EEG data is explored to investigate the performance benefits over unimodal models.
Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI
This paper proposes NonLinear Equivariant Imaging (NLEI), a self-supervised learning approach to eliminate the need for ground truth for deep MRF image reconstruction.
A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers
This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process.
Deep Unrolling for Magnetic Resonance Fingerprinting
Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative MR imaging approach.
Magnetic Resonance Fingerprinting
Rolling Shutter Correction
An off-the-grid approach to multi-compartment magnetic resonance fingerprinting
We propose a novel numerical approach to separate multiple tissue compartments in image voxels and to estimate quantitatively their nuclear magnetic resonance (NMR) properties and mixture fractions, given magnetic resonance fingerprinting (MRF) measurements.
Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations
Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems.
Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders
We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing.
The Practicality of Stochastic Optimization in Imaging Inverse Problems
We investigate this phenomenon and propose a theory-inspired mechanism for the practitioners to efficiently characterize whether it is beneficial for an inverse problem to be solved by stochastic optimization techniques or not.
Deep MR Fingerprinting with total-variation and low-rank subspace priors
Deep learning (DL) has recently emerged to address the heavy storage and computation requirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprinting (MRF) reconstruction.
CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction
Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy computations of a matched-filtering step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications.
CoverBLIP: scalable iterative matched filtering for MR Fingerprint recovery
Current proposed solutions for the high dimensionality of the MRF reconstruction problem rely on a linear compression step to reduce the matching computations and boost the efficiency of fast but non-scalable searching schemes such as the KD-trees.
Balanced multi-shot EPI for accelerated Cartesian MRF: An alternative to spiral MRF
The main purpose of this study is to show that a highly accelerated Cartesian MRF scheme using a multi-shot EPI readout (i. e. multi-shot EPI-MRF) can produce good quality multi-parametric maps such as T1, T2 and proton density (PD) in a sufficiently short scan duration that is similar to conventional MRF.
Geometry of Deep Learning for Magnetic Resonance Fingerprinting
Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy storage and computation requirements of a dictionary-matching (DM) step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications.
Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery
We adopt data structure in the form of cover trees and iteratively apply approximate nearest neighbour (ANN) searches for fast compressed sensing reconstruction of signals living on discrete smooth manifolds.
