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Dimitris Perdios

Publications associées (22)

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Compressive Multiplexing of Ultrasound Signals

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

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

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

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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 ...

2018

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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

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IEEE2018

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Based on the success of deep neural networks for image recovery, we propose a new paradigm for the compression and decompression of ultrasound~(US) signals which relies on stacked denoising autoencoders. The first layer of the network is used to compress t ...

Ieee2017

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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

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We propose to map the fast iterative shrinkage-thresholding algorithm to a deep neural network (DNN), with a sparsity prior in a concatenation of wavelet bases, in the context of compressive imaging. We exploit the DNN architecture to learn the optimal wei ...

2017

USSR: An UltraSound Sparse Regularization framework

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

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

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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