Search Results for author: Mohammad Golbabaee

Found 12 papers, 5 papers with code

Deep Unrolling for Magnetic Resonance Fingerprinting

no code implementations23 Jan 2022 Dongdong Chen, Mike E. Davies, Mohammad Golbabaee

Magnetic Resonance Fingerprinting (MRF) has emerged as a promising quantitative MR imaging approach.

Magnetic Resonance Fingerprinting

An off-the-grid approach to multi-compartment magnetic resonance fingerprinting

1 code implementation23 Nov 2020 Mohammad Golbabaee, Clarice Poon

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.

Magnetic Resonance Fingerprinting

Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations

1 code implementation27 Jun 2020 Dongdong Chen, Mike E. Davies, Mohammad Golbabaee

Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems.

De-aliasing Magnetic Resonance Fingerprinting

The Practicality of Stochastic Optimization in Imaging Inverse Problems

no code implementations22 Oct 2019 Junqi Tang, Karen Egiazarian, Mohammad Golbabaee, Mike Davies

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.

Deblurring Image Deblurring +1

Deep MR Fingerprinting with total-variation and low-rank subspace priors

no code implementations26 Feb 2019 Mohammad Golbabaee, Carolin M. Pirkl, Marion I. Menzel, Guido Buonincontri, Pedro A. Gómez

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.

Magnetic Resonance Fingerprinting Time Series

CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction

1 code implementation3 Oct 2018 Mohammad Golbabaee, Zhouye Chen, Yves Wiaux, Mike Davies

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.

Dimensionality Reduction

CoverBLIP: scalable iterative matched filtering for MR Fingerprint recovery

no code implementations6 Sep 2018 Mohammad Golbabaee, Zhouye Chen, Yves Wiaux, Mike E. Davies

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

no code implementations6 Sep 2018 Arnold Julian Vinoj Benjamin, Pedro A. Gómez, Mohammad Golbabaee, Tim Sprenger, Marion I. Menzel, Mike E. Davies, Ian Marshall

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

no code implementations5 Sep 2018 Mohammad Golbabaee, Dong-Dong Chen, Pedro A. Gómez, Marion I. Menzel, Mike E. Davies

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.

Dimensionality Reduction Image Reconstruction +1

Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery

no code implementations23 Jun 2017 Mohammad Golbabaee, Zhouye Chen, Yves Wiaux, Mike E. Davies

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.

Magnetic Resonance Fingerprinting

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