Publications associées à A Forward Regridding Method with Minimal Oversampling for Accurate and Efficient Iterative Tomographic Algorithms

Towards Trustworthy Deep Learning for Image Reconstruction

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

EPFL2024

Localized model order reduction and domain decomposition methods for coupled heterogeneous systems

Jan Sickmann Hesthaven, Niccolo' Discacciati

We propose a model order reduction technique to accurately approximate the behavior of multi-component systems without any a-priori knowledge of the coupled model. In the offline phase, we construct independent surrogate models by solving the local problem ...

Wiley2023

Maximum likelihood bolometry for ASDEX upgrade experiments

Olivier Claude Martin Février, Riccardo Rossi

Bolometry is an essential diagnostic for calculating the power balances and for the understanding of different physical aspects of tokamak experiments. The reconstruction method based on the Maximum Likelihood (ML) principle, developed initially for JET, h ...

Iop Publishing Ltd2023

Exploring Novel Modalities for Optical Diffraction Tomography

Amirhossein Saba Shirvan

In the past decade, optical diffraction tomography has gained a lot of attention for its ability to create label-free three-dimensional (3D) images of the refractive index distribution of biological samples using scattered fields measured through holograph ...

EPFL2023

A Compressive Sensing Framework for EMI Source Localization Using a Metalens Structure: Localization Beyond the Diffraction Limit

Marcos Rubinstein, Mohammad Azadifar, Farhad Rachidi-Haeri, Hamidreza Karami

In this article, we propose a novel approach for the localization of electromagnetic interference (EMI) sources based on the concept of compressive sensing and metalenses. We show that using the spatial and frequency dispersion offered by the metalens, we ...

2022

High-Resolution Multi-Spectral Imaging With Diffractive Lenses and Learned Reconstruction

Oguzhan Fatih Kar, Can Deniz Bezek

Spectral imaging is a fundamental diagnostic technique with widespread application. Conventional spectral imaging approaches have intrinsic limitations on spatial and spectral resolutions due to the physical components they rely on. To overcome these physi ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Multi-CryoGAN: Reconstruction of Continuous Conformations in Cryo-EM Using Generative Adversarial Networks

Michaël Unser, Harshit Gupta, Thanh Hai Phan, Jaejun Yoo

We propose a deep-learning-based reconstruction method for cryo-electron microscopy (Cryo-EM) that can model multiple conformations of a nonrigid biomolecule in a standalone manner. Cryo-EM produces many noisy projections from separate instances of the sam ...

Springer, Cham2020

Machine learning of multiscale active force generation models for the efficient simulation of cardiac electromechanics

Alfio Quarteroni, Francesco Regazzoni

High fidelity (HF) mathematical models describing the generation of active force in the cardiac muscle tissue typically feature a large number of state variables to capture the intrinsically complex underlying subcellular mechanisms. With the aim of drasti ...

2020

Model-informed machine learning for multi-component T2 relaxometry

Jean-Philippe Thiran, Meritxell Bach Cuadra, Tobias Kober, Tom Hilbert, Erick Jorge Canales Rodriguez, Elda Fischi Gomez, Gian Franco Piredda, Thomas Yu, Cristina Granziera, Muhamed Barakovic

Recovering the distribution from multi-echo magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to comb ...

2020

SciFi detector and associated method for real-time determination of profile and output factor for small fields in stereotactic radiotherapy

Guido Haefeli, Frédéric Blanc, Plamen Hristov Hopchev, Philippe Pittet

Purpose For determining small-field profile and output factor during stereotactic radiotherapy quality assurance (QA) procedures, we propose a novel system based on the scintillating fiber (SciFi) detector with output image acquisition and processing to al ...

WILEY2020