Efficient compressive sampling strategies and novel reconstruction methods with applications in MRI

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Fairness of decision-making algorithms is an increasingly important issue. In this paper, we focus on spectral clustering with group fairness constraints, where every demographic group is represented in each cluster proportionally as in the general populat ...

The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and efficiently navigate complex optimization landscapes. Classical gradient desce ...

. We study very weak solutions to scalar Euler-Lagrange equations associated with quadratic convex functionals. We investigate whether W1,1 solutions are necessarily W 1,2 Nash and Schauder applicable. We answer this question positively for a suitable clas ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

Noninvasive tracking of biochemical processes in the body is paramount in diagnostic medicine. Among the leading techniques is spectroscopic magnetic resonance imaging (MRI), which tracks metabolites with an amplified (hyperpolarized) magnetization signal ...

In the quest for controlling materials' properties, light as an external stimulus has a special place as it can create new states of matter and enable their ultrafast manipulation. In particular, spintronics, an exciting emergent field relying on the elect ...

PurposeIn this study, we aimed to measure the apparent diffusion coefficients (ADCs) of major phosphorous metabolites in the human calf muscle at 7 T with a diffusion-weighted (DW)-STEAM sequence.MethodsA DW-STEAM sequence with bipolar gradients was implem ...

Introduction: Estimating the risk of manic relapse could help the psychiatrist individually adjust the treatment to the risk. Some authors have attempted to estimate this risk from baseline clinical data. Still, no studies have assessed whether the estimat ...

ObjectiveThe application value of T2 mapping in evaluating endometrial carcinoma (EMC) features remains unclear. The aim of the study was to determine the quantitative T2 values in EMC using a novel accelerated T2 mapping, and evaluate them for detection, ...

Magnetic resonance spectroscopy (MRS) is the only technique that can detect endogenous metabolites directly and non-invasively in vivo. It allows to identify different metabolites and analyze the dynamic neurochemical processes in the brain, skeletal muscl ...

We describe the first gradient methods on Riemannian manifolds to achieve accelerated rates in the non-convex case. Under Lipschitz assumptions on the Riemannian gradient and Hessian of the cost function, these methods find approximate first-order critical ...

Magnetic Resonance Imaging (MRI) is an established technique to study in vivo neurological disorders such as Multiple Sclerosis (MS). To avoid errors on MRI data organization and automated processing, a standard called Brain Imaging Data Structure (BIDS) h ...

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and security guarantees. However, FL could result in task-oriented data traffic ...

In real-time optimization, the solution quality depends on the model ability to predict the plant KarushKuhn-Tucker (KKT) conditions. In the case of non-parametric plant-model mismatch, one can add input-affine modifiers to the model cost and constraints a ...

Harmonic nanoparticles (HNPs) have emerged as appealing exogenous probes for optical bioimaging due to their distinctive features such as long-term photostability and spectral flexibility, allowing multiphoton excitation in the classical (NIR-I) and extend ...

Machine learning has become the state of the art for the solution of the diverse inverse problems arising from computer vision and medical imaging, e.g. denoising, super-resolution, de-blurring, reconstruction from scanner data, quantitative magnetic reson ...

Objectives Fluid and white matter suppression (FLAWS) is a recently proposed magnetic resonance sequence derived from magnetization-prepared 2 rapid acquisition gradient-echo providing 2 coregistered datasets with white matter- and cerebrospinal fluid-supp ...

This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...

The relationship between age-related changes in brain structural connectivity (SC) and functional connectivity (FC) with cognition is not well understood. Furthermore, it is not clear whether cognition is represented via a similar spatial pattern of FC and ...

Efficient compressive sampling strategies and novel reconstruction methods with applications in MRI

Chat with Graph Search

Accelerating Spectral Clustering under Fairness Constraints

Learning to Optimize with Convergence Guarantees Using Nonlinear System Theory

WILD SOLUTIONS TO SCALAR EULER-LAGRANGE EQUATIONS

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Long-lived enhanced magnetization-A practical metabolic MRI contrast material

Ultrafast control of emergent quantum matter probed by electron microscopy

Apparent diffusion coefficients of 31P metabolites in the human calf muscle at 7 T

Improved estimation of the risk of manic relapse by combining clinical and brain scan data

Feasibility of accelerated T2 mapping for the preoperative assessment of endometrial carcinoma

Robust gamma-aminobutyric acid and energy metabolism measurements by proton and phosphorus magnetic resonance spectroscopy and fingerprinting

An Accelerated First-Order Method for Non-convex Optimization on Manifolds

BMAT: An open-source BIDS managing and analysis tool

Over-the-Air Federated Learning via Second-Order Optimization

On improving the efficiency of modifier adaptation via directional information

Gd3+-Functionalized Lithium Niobate Nanoparticles for Dual Multiphoton and Magnetic Resonance Bioimaging

Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

3-Dimensional Fluid and White Matter Suppression Magnetic Resonance Imaging Sequence Accelerated With Compressed Sensing Improves Multiple Sclerosis Cervical Spinal Cord Lesion Detection Compared With Standard 2-Dimensional Imaging

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Improving the Training of Compact Neural Networks for Visual Recognition

Mapping of Structure-Function Age-Related Connectivity Changes on Cognition Using Multimodal MRI