Publications by Dimitris Perdios

Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions

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

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, Marcel Arditi, Florian Martinez, 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, Marcel Arditi, Florian Martinez, 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, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon, Sébastien Alexandre Emery

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

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

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, Marcel Arditi, Florian Martinez, 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

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

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

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

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