Publications de Dimitris Perdios

Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions

Michaël Unser, Dimitris Perdios, Stanislas Ducotterd, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon, Sebastian Jonas Neumayer

Lipschitz-constrained neural networks have several advantages over unconstrained ones and can be applied to a variety of problems, making them a topic of attention in the deep learning community. Unfortunately, it has been shown both theoretically and empi ...

2024

CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging

Jean-Philippe Thiran, Dimitris Perdios, Florian Martinez, Marcel Arditi, Manuel Vonlanthen

Ultrafast ultrasound (US) revolutionized biomedical imaging with its capability of acquiring full-view frames at over 1 kHz, unlocking breakthrough modalities such as shear-wave elastography and functional US neuroimaging. Yet, it suffers from strong diffr ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

CNN-Based Ultrasound Image Reconstruction for Ultrafast Displacement Tracking

Jean-Philippe Thiran, Dimitris Perdios, Florian Martinez, Marcel Arditi, Manuel Vonlanthen

Thanks to its capability of acquiring full-view frames at multiple kilohertz, ultrafast ultrasound imaging unlocked the analysis of rapidly changing physical phenomena in the human body, with pioneering applications such as ultrasensitive flow imaging in t ...

Institute of Electrical and Electronics Engineers2021

Ultrasound Imaging: From Physical Modeling to Deep Learning

Dimitris Perdios

Among the medical imaging modalities, ultrasound (US) imaging is one of the safest, most widespread, and least expensive method used in medical diagnosis. In the past decades, several technological advances enabled the advent of ultrafast US imaging, an ac ...

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

Image reconstruction method based on a trained non-linear mapping

Adrien Besson, Jean-Philippe Thiran, Dimitris Perdios, Adrien Georges Jean Besson, Florian Martinez, Marcel Arditi

The present invention concerns an array processing image reconstruction method comprising: receiving (13) a set of echo waveforms as measurements from the object of interest; defining (15) a measurement model linking an unknown image of the object to the m ...

2020

Single-Shot CNN-Based Ultrasound Imaging with Sparse Linear Arrays

Jean-Philippe Thiran, Dimitris Perdios, Florian Martinez, Marcel Arditi, Manuel Vonlanthen

Sparse arrays are a topic of high interest within the ultrasound (US) imaging community, because of their promising ability to reduce costs, complexity, energy consumption, and data transfer requirements of US systems, thus addressing the main challenges o ...

IEEE2020

A Physical Model of Non-stationary Blur in Ultrasound Imaging

Paul Hurley, Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Adrien Georges Jean Besson, Matthieu Martin Jean-André Simeoni, Marcel Arditi

Conventional ultrasound (US) imaging relies on delay-and-sum (DAS) beamforming which retrieves a radio- frequency (RF) image, a blurred estimate of the tissue reflectivity function (TRF). Despite the non-stationarity of the blur induced by propagation effe ...

2019

Model-based image reconstruction method

Adrien Besson, Jean-Philippe Thiran, Dimitris Perdios, Adrien Georges Jean Besson, Marcel Arditi

The present invention concerns a pulse wave image reconstruction method to be used for example in ultrasound imaging. The proposed method is based on an image measurement model and its adjoint operator. The proposed method introduces matrix-free formulatio ...

2019

On Problem Formulation, Efficient Modeling and Deep Neural Networks for High-Quality Ultrasound Imaging

Adrien Besson, Jean-Philippe Thiran, Dimitris Perdios, Adrien Georges Jean Besson, Florian Martinez, Marcel Arditi, Manuel Vonlanthen

Recently, many pulse-echo ultrasound (US) imaging methods have relied on the transmission of unfocused wavefronts. Such a strategy allows for very high frame rates at the cost of a degraded image quality. In this work, we present a regularized inverse prob ...

IEEE2019

Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Adrien Georges Jean Besson

We investigate the benefits of known partial support for the recovery of joint-sparse signals and demonstrate that it is advantageous in terms of recovery performance for both rank-blind and rank-aware algorithms. We suggest extensions of several joint-spa ...

IEEE Institute of Electrical and Electronics Engineers2019

Pulse-Stream Models In Time-Of-Flight Imaging

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Adrien Georges Jean Besson

This paper considers the problem of reconstructing raw signals from random projections in the context of time-of-flight imaging with an array of sensors. It presents a new signal model, coined as multi-channel pulse-stream model, which exploits pulse-strea ...

IEEE2018

Sparse Recovery of Strong Reflectors With an Application to Non-Destructive Evaluation

Martin Vetterli, Jean-Philippe Thiran, Dimitris Perdios, Hanjie Pan, Adrien Georges Jean Besson, Eric Bezzam

In this paper we show that it is sufficient to recover the locations of K strong reflectors within an insonified medium from three receive elements and 2K+1 samples per element. The proposed approach leverages advances in sampling signals with a finite rat ...

IEEE2018

Compressive Multiplexing of Ultrasound Signals

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Adrien Georges Jean Besson, Marcel Arditi

High-quality 3D ultrasound (US) imaging requires dense matrix-array probes with thousands of elements and necessitates an unrealistic number of coaxial cables to connect such probes to back-end systems. To address this issue, many techniques have been deve ...

IEEE2018

Deep Convolutional Neural Network for Ultrasound Image Enhancement

Adrien Besson, Jean-Philippe Thiran, Dimitris Perdios, Adrien Georges Jean Besson, Florian Martinez, Marcel Arditi, Manuel Vonlanthen

The problem of improving image quality in ultrafast ultrasound (US) imaging by means of regularized iterative algorithms has raised a vast interest in the US community. These approaches usually rely on standard image processing priors, such as wavelet spar ...

IEEE2018

Ultrafast ultrasound imaging as an inverse problem: Matrix-free sparse image reconstruction

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Adrien Georges Jean Besson, Rafael Eduardo Carrillo Rangel, Florian Martinez, Marcel Arditi

Conventional ultrasound (US) image reconstruction methods rely on delay-and-sum (DAS) beamforming, which is a relatively poor solution of the image reconstruction problem. An alternative to DAS consists in using iterative techniques which require both an a ...

2017

USSR: An UltraSound Sparse Regularization framework

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Adrien Georges Jean Besson, Florian Martinez, Marcel Arditi

Ultrafast ultrasound (US) imaging uses unfocused waves to insonify the whole medium of interest at once, allowing pulse-echo US imaging to achieve very high frame rates, at the cost of a lower image quality. In this paper, we present USSR, an UltraSound Sp ...

Ieee2017

A compressed-sensing approach for ultrasound imaging

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Adrien Georges Jean Besson, Rafael Eduardo Carrillo Rangel, Marcel Arditi

Ultrasonography uses multiple piezo-electric element probes to image tissues. Current time-domain beamforming techniques require the signal at each transducer-element to be sampled at a rate higher than the Nyquist criterion, resulting in an extensive amou ...

2017

Morphological component analysis for sparse regularization in plane wave imaging

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Adrien Georges Jean Besson, Rafael Eduardo Carrillo Rangel, Marcel Arditi, Eric Bezzam

Classical ultrasound image reconstruction mainly relies on the well-known delay-and-sum (DAS) beamforming for its simplicity and real-time capability. Sparse regularization methods propose an alternative to DAS which lead to a better inversion of the ill-p ...

Ieee2016

A compressed beamforming framework for ultrafast ultrasound imaging

Jean-Philippe Thiran, Yves Wiaux, Olivier Bernard, Dimitris Perdios, Adrien Georges Jean Besson, Rafael Eduardo Carrillo Rangel, Marcel Arditi

Classical beamforming methods, based on Delay- And-Sum (DAS) require an extensive number of samples and delay calculations to obtain high-quality images. Compressed Beamforming (CB) proposes an alternative to DAS, based on compressed sensing, which aims at ...

Ieee2016