Publications related to l0-deconvolution for compressive diffusion MRI

A first-order primal-dual method with adaptivity to local smoothness

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Resolving bundle-specific intra-axonal T2 values within a voxel using diffusion-relaxation tract-based estimation

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Alessandro Daducci, Cristina Granziera, Muhamed Barakovic

At the typical spatial resolution of MRI in the human brain, approximately 60–90% of voxels contain multiple fiber populations. Quantifying microstructural properties of distinct fiber populations within a voxel is therefore challenging but necessary. Whil ...

2020

Towards quantitative brain connectomics: microstructure informed tractography via convex optimization

Muhamed Barakovic

Tractography is the only non-invasive technique which is used to reconstruct the white mat- ter structural connectivity of the human brain. It relies on a specific Magnetic Resonance Imaging (MRI) acquisition, called diffusion MRI, which is sensitive to th ...

EPFL2019

Stochastic Frank-Wolfe for Composite Convex Minimization

Volkan Cevher, Alp Yurtsever

A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to some affine constraints. The majority of classical SDP solvers are designed fo ...

2019

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Alessandro Daducci

In this study, we developed a robust inversion algorithm to estimate the Neurite Orientation Dispersion and Density Imaging (NODDI) model. It is based on the Accelerated Microstructure Imaging via Convex Optimization (AMICO) framework. However, in contrast ...

2018

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

Jean-Philippe Thiran, Rafael Eduardo Carrillo Rangel, Rémi Charrier

OBJECTIVE The authors developed a new, real-time interactive inverse planning approach, based on a fully convex framework, to be used for Gamma Knife radiosurgery. METHODS The convex framework is based on the precomputation of a dictionary composed of the ...

AMER ASSOC NEUROLOGICAL SURGEONS2018

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

Alireza Karimi, Achille Nicoletti

Recently, a new data-driven method for robust control with H-infinity performance has been proposed. This method is based on convex optimization and converges to the optimal performance when the controller order increases. However, for low-order controller ...

IEEE2018

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Alireza Karimi, Achille Nicoletti

Recently, a new data-driven method for robust control with H∞ performance has been proposed. This method is based on convex optimization and converges to the optimal performance when the controller order increases. However, for low-order controllers, the p ...

Elsevier2018

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Alireza Karimi, Achille Nicoletti

Recently, a new data-driven method for robust control with H ∞ performance has been proposed. This method is based on convex optimization and converges to the optimal performance when the controller order increases. However, for low-order controllers, the ...

2018

Proximity Operators of Discrete Information Divergences

Mireille El Gheche, Giovanni Chierchia

While phi-divergences have been extensively studied in convex analysis, their use in optimization problems often remains challenging. In this regard, one of the main shortcomings of existing methods is that the minimization of phi-divergences is usually pe ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

l0-deconvolution for compressive diffusion MRI

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A first-order primal-dual method with adaptivity to local smoothness

Resolving bundle-specific intra-axonal T2 values within a voxel using diffusion-relaxation tract-based estimation

Towards quantitative brain connectomics: microstructure informed tractography via convex optimization

Stochastic Frank-Wolfe for Composite Convex Minimization

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Proximity Operators of Discrete Information Divergences

Fast and robust estimation of NODDI parameters using non-Gaussian noise models and spatial regularization

A first-order primal-dual method with adaptivity to local smoothness

Resolving bundle-specific intra-axonal T2 values within a voxel using diffusion-relaxation tract-based estimation

Towards quantitative brain connectomics: microstructure informed tractography via convex optimization

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Stochastic Frank-Wolfe for Composite Convex Minimization

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H∞ Performance

Proximity Operators of Discrete Information Divergences