Publications related to Image Reconstruction in K-Space from MR Data Encoded with Ambiguous Gradient Fields

Iterative image subset processing for image reconstruction

Paul Hurley, Matthieu Martin Jean-André Simeoni, Giovanni Cherubini, Sanaz Kazemi

The present invention is notably directed to computer-implemented methods and systems for recovering an image. Present methods comprise: accessing signal data representing signals; identifying subsets of points arranged so as to span a region of interest a ...

2017

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

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Martin Vetterli, Arash Amini, Mitra Fatemi

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of

\mathds{1}_{\{p\leq0\}}

to the image plane, where

\mathds{1}

denotes the indicator function and

p

is some r ...

2016

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

An Algorithmic Framework for Mumford-Shah Regularization of Inverse Problems in Imaging

Martin Kurt Storath

The Mumford-Shah model is a very powerful variational approach for edge preserving regularization of image reconstruction processes. However, it is algorithmically challenging because one has to deal with a non-smooth and non-convex functional. In this pap ...

Iop Publishing Ltd2015

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

Zafer Dogan

Foundations of signal processing are heavily based on Shannon's sampling theorem for acquisition, representation and reconstruction. This theorem states that signals should not contain frequency components higher than the Nyquist rate, which is half of the ...

EPFL2015

Single-Shot Imaging with Higher-Dimensional Encoding Using Magnetic Field Monitoring and Concomitant Field Correction

Daniel Gallichan

PurposePatLoc (Parallel Imaging Technique using Localized Gradients) accelerates imaging and introduces a resolution variation across the field-of-view. Higher-dimensional encoding employs more spatial encoding magnetic fields (SEMs) than the corresponding ...

Wiley-Blackwell2015

MR Image Reconstruction from Generalized Projections

Daniel Gallichan

Purpose: Currently, the time required for image reconstruction is prohibitively long if data are acquired using multidimensional imaging trajectories that make use of multichannel systems equipped with nonlinear gradients. Methods are presented that reduce ...

Wiley-Blackwell2014

Jaggies as aliasing or reconstruction phenomena: a tutorial

Roger Hersch, Isaac Amidror

Jaggies (staircasing effects) along slanted lines or curved edges are omnipresent in digital imaging. They are so widespread in digital display devices that very often they are associated with the modern "computerized" world (and sometimes even intentional ...

Is&T & Spie2014

Compressed Quantitative MRI: Bloch Response Recovery through iterated projection

Pierre Vandergheynst, Yves Wiaux, Gilles Puy

Inspired by the recently proposed Magnetic Resonance Fin- gerprinting technique, we develop a principled compressed sensing framework for quantitative MRI. The three key com- ponents are: a random pulse excitation sequence following the MRF technique; a ra ...

2014

Image Reconstruction in K-Space from MR Data Encoded with Ambiguous Gradient Fields

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Iterative image subset processing for image reconstruction

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Sparse regularization methods in ultrafast ultrasound imaging

An Algorithmic Framework for Mumford-Shah Regularization of Inverse Problems in Imaging

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

Single-Shot Imaging with Higher-Dimensional Encoding Using Magnetic Field Monitoring and Concomitant Field Correction

MR Image Reconstruction from Generalized Projections

Jaggies as aliasing or reconstruction phenomena: a tutorial

Compressed Quantitative MRI: Bloch Response Recovery through iterated projection

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

Sparse regularization methods in ultrafast ultrasound imaging

MR Image Reconstruction from Generalized Projections

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Jaggies as aliasing or reconstruction phenomena: a tutorial

Iterative image subset processing for image reconstruction

An Algorithmic Framework for Mumford-Shah Regularization of Inverse Problems in Imaging

Single-Shot Imaging with Higher-Dimensional Encoding Using Magnetic Field Monitoring and Concomitant Field Correction

Compressed Quantitative MRI: Bloch Response Recovery through iterated projection