Publications related to Sparse inverse problems for Fourier imaging

Sparse regularization methods in ultrafast ultrasound imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Adrien Georges Jean Besson, Olivier Bernard

Ultrafast ultrasound (US) imaging based on plane wave (PW) insonification is a widely used modality nowadays. Two main types of approaches have been proposed for image reconstruction either based on classical delay-and-sum (DAS) or on Fourier reconstructio ...

Ieee2016

A sparse reconstruction framework for Fourier-based plane wave imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Adrien Georges Jean Besson, Olivier Bernard

Ultrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Among PW imaging methods, Fourier-based approaches have demonstrated to be competitive compared to traditional delay ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

Estimation Error of the Constrained Lasso

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

2016

3D-Printed Shepp-Logan Phantom as a Real-World Benchmark for MRI

Dimitri Nestor Alice Van De Ville, Jeffrey Adam Kasten, François Lazeyras

Purpose: As prescribed and reliable geometrical entities, phantoms have served as indispensable validation tools in a variety of MR-related topics. Though a number of phantoms have been conceived, certain applications may warrant highly customized geometri ...

Wiley-Blackwell2016

A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix

Kyong Hwan Jin

Parallel MRI (pMRI) and compressed sensing MRI (CS-MRI) have been considered as two distinct reconstruction problems. Inspired by recent k-space interpolation methods, an annihilating filter-based low-rank Hankel matrix approach is proposed as a general fr ...

IEEE2016

Superresolution Reconstruction for Magnetic Resonance Spectroscopic Imaging Exploiting Low-Rank Spatio-Spectral Structure

Magnetic resonance spectroscopic imaging (MRSI) is a rapidly developing medical imaging modality, capable of conferring both spatial and spectral information content, and has become a powerful clinical tool. The ability to non-invasively observe spatial ma ...

EPFL2015

Signal structure: from manifolds to molecules and structured sparsity

Sofia Karygianni

Effective representation methods and proper signal priors are crucial in most signal processing applications. In this thesis we focus on different structured models and we design appropriate schemes that allow the discovery of low dimensional latent struct ...

EPFL2015

Sensing the real world

Juri Ranieri

A sensor is a device that detects or measures a physical property and records, indicates, or otherwise responds to it. In other words, a sensor allows us to interact with the surrounding environment, by measuring qualitatively or quantitatively a given phe ...

EPFL2014

Jump-Sparse and Sparse Recovery Using Potts Functionals

Martin Kurt Storath

We recover jump-sparse and sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and pr ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Sparse inverse problems for Fourier imaging

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Sparse regularization methods in ultrafast ultrasound imaging

A sparse reconstruction framework for Fourier-based plane wave imaging

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Estimation Error of the Constrained Lasso

3D-Printed Shepp-Logan Phantom as a Real-World Benchmark for MRI

A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix

Superresolution Reconstruction for Magnetic Resonance Spectroscopic Imaging Exploiting Low-Rank Spatio-Spectral Structure

Signal structure: from manifolds to molecules and structured sparsity

Sensing the real world

Jump-Sparse and Sparse Recovery Using Potts Functionals

A sparse reconstruction framework for Fourier-based plane wave imaging

Superresolution Reconstruction for Magnetic Resonance Spectroscopic Imaging Exploiting Low-Rank Spatio-Spectral Structure

Jump-Sparse and Sparse Recovery Using Potts Functionals

Signal structure: from manifolds to molecules and structured sparsity

Sensing the real world

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Sparse regularization methods in ultrafast ultrasound imaging

3D-Printed Shepp-Logan Phantom as a Real-World Benchmark for MRI

Estimation Error of the Constrained Lasso

A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix