Publications associées à Analytical Form of Shepp-Logan Phantom for Parallel MRI

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

Integrated electronics for time-of-flight positron emission tomography photodetectors

Andrada Alexandra Muntean

Positron emission tomography is a nuclear imaging technique well known for its use in oncology for cancer diagnosis and staging. A PET scanner is a complex machine which comprises photodetectors placed in a ring configuration that detect gamma photons gen ...

EPFL2023

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

Gulnaz Ganeeva

To obtain a more complete understanding of material microstructure at the nanoscale and to gain profound insights into their properties, there is a growing need for more efficient and precise methods that can streamline the process of 3D imaging using a tr ...

EPFL2023

Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions

Jean-Philippe Thiran, Tobias Kober, Elda Fischi Gomez, Alessandro Daducci, Tom Hilbert, Elena Beanato, Gabriel Girard, Julia Brügger, Gian Franco Piredda, Friedhelm Christoph Hummel, Takuya Morishita, Maximilian Jonas Wessel, Philipp Johannes Koch, Erick Jorge Canales Rodriguez, Marco Pizzolato, Andéol Geoffroy Cadic-Melchior, Thomas Yu, Chang-Hyun Park

Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acqu ...

2022

2.5 Hz sample rate time-domain near-infrared optical tomography based on SPAD-camera image tissue hemodynamics

Edoardo Charbon, Scott Anthony Lindner, Jingjing Jiang, Martin Wolf

Tim-domain near-infrared optical tomography (TD NIROT) techniques based on diffuse light were gaining performance over the last years. They are capable of imaging tissue at several centimeters depth and reveal clinically relevant information, such as tissu ...

OPTICAL SOC AMER2022

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

Thomas Yu

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 ...

EPFL2022

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

Tobias Kober

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 ...

LIPPINCOTT WILLIAMS & WILKINS2022

Polarization-sensitive optics diffraction tomography

Demetri Psaltis, Ahmed Ayoub, Joowon Lim, Elizabeth Elena Antoine, Amirhossein Saba Shirvan

Polarization of light has been widely used as a contrast mechanism in two-dimensional (2D) microscopy and also in some three-dimensional (3D) imaging modalities. In this paper, we report the 3D tomographic reconstruction of the refractive index (RI) tensor ...

2021

Distributed Learning in Non-Convex Environments-Part II: Polynomial Escape From Saddle-Points

Ali H. Sayed, Stefan Vlaski

The diffusion strategy for distributed learning from streaming data employs local stochastic gradient updates along with exchange of iterates over neighborhoods. In Part I [3] of this work we established that agents cluster around a network centroid and pr ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Quantitative Microstructural Imaging for Clinical Use

Gian Franco Piredda

Today, Magnetic Resonance Imaging (MRI) is a well-established medical imaging modality thanks to its excellent soft tissue contrast. Conventional MR image contrasts can be weighted towards one or more physical properties that discriminate different tissues ...

EPFL2021

Learning Lipschitz-Controlled Activation Functions in Neural Networks for Plug-and-Play Image Reconstruction Methods

Michaël Unser, Dimitris Perdios, Sébastien Alexandre Emery, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon

Ill-posed linear inverse problems are frequently encountered in image reconstruction tasks. Image reconstruction methods that combine the Plug-and-Play (PnP) priors framework with convolutional neural network (CNN) based denoisers have shown impressive per ...

2021

3-Dimensional magnetic resonance imaging of the freely moving human eye

Jérôme Yerly, Silvio Ionta, Josefina Adriana Maria Bastiaansen, David Zeugin, João Pedro Forjaco Jorge, Benedetta Franceschiello, Lorenzo Di Sopra

Eye motion is a major confound for magnetic resonance imaging (MRI) in neuroscience or ophthalmology. Currently, solutions toward eye stabilisation include participants fixating or administration of paralytics/anaesthetics. We developed a novel MRI protoco ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Model-informed machine learning for multi-component T2 relaxometry

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

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

Fat navigators based retrospective motion correction strategies for brain magnetic resonance imaging

Frédéric Gretsch

Magnetic resonance resonance (MRI) is a widely used modality to obtain in vivo tissue information. Clinical applications are near countless, and almost all body parts can be examined using an MR scanner. As the method is non invasive, does not use ionizing ...

EPFL2020

Data-driven Measurement Designs for Magnetic Resonance Imaging

Baran Gözcü

Despite being a powerful medical imaging technique which does not emit any ionizing radiation, magnetic resonance imaging (MRI) always had the major problem of long scanning times that can take up to an hour depending on the application. It also requires u ...

EPFL2019

Imaging from Echoes: On Inverse Problems in Ultrasound

Adrien Georges Jean Besson

Ultrasound (US) imaging is currently living a revolution. On the one hand, ultrafast US imaging, a novel way of acquiring and producing US images, has paved the way to several advanced imaging modes, e.g. shear-wave elastography, ultrafast Doppler imaging ...

EPFL2018

Fast high-resolution brain metabolite mapping on a clinical 3T MRI by accelerated H-1-FID-MRSI and low-rank constrained reconstruction

Michel Kocher, Dimitri Nestor Alice Van De Ville, François Lazeyras, Sébastien Courvoisier, Matthieu Guerquin-Kern, Jeffrey Adam Kasten

Purpose: Epitomizing the advantages of ultra short echo time and no chemical shift displacement error, high-resolution-free induction decay magnetic resonance spectroscopic imaging (FID-MRSI) sequences have proven to be highly effective in providing unbias ...

WILEY2019

Fast model-based T-2 mapping using SAR-reduced simultaneous multislice excitation

Jean-Philippe Thiran, Gunnar Krüger, Tobias Kober, José Pedro Rebelo Ferreira Marques, Tom Hilbert

Purpose To obtain whole-brain high-resolution T-2 maps in 2 minutes by combining simultaneous multislice excitation and low-power PINS (power independent of number of slices) refocusing pulses with undersampling and a model-based reconstruction. Methods A ...

WILEY2019

Rethinking Sampling in Parallel MRI: A Data-Driven Approach

Volkan Cevher, Baran Gözcü, Thomas Sanchez

In the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI em ...

IEEE2019

Optical projection tomography for whole organ imaging

David Nguyen

In the past twenty years, far-reaching studies of molecular and cellular processes have reached a milestone in their maturation, and the knowledge from these studies was ready to apply at higher organizational levels. At that time, rodent models were long ...

EPFL2019