Mr. Zafer Dogan Profile

Zafer Dogan

at Ecole Polytechnique Fédérale de Lausanne

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Proceedings Article | 4 March 2013 Paper

Application of a new sensing principle for photoacoustic imaging of point absorbers

Zafer Doğan, Ivana Jovanovic, Thierry Blu, Dimitri Van De Ville

Proceedings Volume 8581, 85813Z (2013) https://doi.org/10.1117/12.2005063

KEYWORDS: Sensors, Absorption, Electroluminescent displays, Acquisition tracking and pointing, Reconstruction algorithms, Acoustics, Signal to noise ratio, Tissue optics, Spherical lenses, Photoacoustic imaging

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Photoacoustic tomography (PAT) is a hybrid imaging method, which combines ultrasonic and optical imaging modalities, in order to overcome their respective weaknesses and to combine their strengths. It is based on the reconstruction of optical absorption properties of the tissue from the measurements of a photoacoustically generated pressure field. Current methods consider laser excitation, under thermal and stress confinement assumptions, which leads to the generation of a propagating pressure field. Conventional reconstruction tech niques then recover the initial pressure field based on the boundary measurements by iterative reconstruction algorithms in time- or Fourier-domain. Here, we propose an application of a new sensing principle that allows for efficient and non-iterative reconstruction algorithm for imaging point absorbers in PAT. We consider a closed volume surrounded by a measurement surface in an acoustically homogeneous medium and we aim at recovering the positions and the amount of heat absorbed by these absorbers. We propose a two-step algorithm based on proper choice of so-called sensing functions. Specifically, in the first step, we extract the projected positions on the complex plane and the weights by a sensing function that is well-localized on the same plane. In the second step, we recover the remaining z-location by choosing a proper set of plane waves. We show that the proposed families of sensing functions are sufficient to recover the parameters of the unknown sources without any discretization of the domain. We extend the method for sources that have joint-sparsity; i.e., the absorbers have the same positions for different frequencies. We evaluate the performance of the proposed algorithm using simulated and noisy sensor data and we demonstrate the improvement obtained by exploiting joint sparsity.

Proceedings Article | 27 September 2011 Paper

Localization of point sources for systems governed by the wave equation

Zafer Dogan, Vagia Tsiminaki, Ivana Jovanovic, Thierry Blu, Dimitri Van De Ville

Proceedings Volume 8138, 81380P (2011) https://doi.org/10.1117/12.894645

KEYWORDS: Acoustics, Source localization, Denoising, Signal to noise ratio, Sensing systems, Reconstruction algorithms, Statistical modeling, Electroencephalography, Nonlinear filtering, Spherical lenses

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Analytic sensing has recently been proposed for source localization from boundary measurements using a generalization of the finite-rate-of-innovation framework. The method is tailored to the quasi-static electromagnetic approximation, which is commonly used in electroencephalography. In this work, we extend analytic sensing for physical systems that are governed by the wave equation; i.e., the sources emit signals that travel as waves through the volume and that are measured at the boundary over time. This source localization problem is highly ill-posed (i.e., the unicity of the source distribution is not guaranteed) and additional assumptions about the sources are needed. We assume that the sources can be described with finite number of parameters, particularly, we consider point sources that are characterized by their position and strength. This assumption makes the solution unique and turns the problem into parametric estimation. Following the framework of analytic sensing, we propose a two-step method. In the first step, we extend the reciprocity gap functional concept to wave-equation based test functions; i.e., well-chosen test functions can relate the boundary measurements to generalized measure that contain volumetric information about the sources within the domain. In the second step-again due to the choice of the test functions - we can apply the finite-rate-of-innovation principle; i.e., the generalized samples can be annihilated by a known filter, thus turning the non-linear source localization problem into an equivalent root-finding one. We demonstrate the feasibility of our technique for a 3-D spherical geometry. The performance of the reconstruction algorithm is evaluated in the presence of noise and compared with the theoretical limit given by Cramer-Rao lower bounds.

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