Publications by Florian Martinez

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

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

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

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