Imagerie photoacoustique pour la biomédecine

Résumé

vignette|300px|Fig. 1. Schéma illustrant l'imagerie photoacoustique. vignette|300px|Fig. 2. Spectre d'absorption des formes oxy et desoxy de l’hémoglobine. L'imagerie photoacoustique, en tant que modalité de l'imagerie biomédicale, est basée sur l'effet photoacoustique. Dans l'imagerie photoacoustique, des impulsions laser non ionisantes sont émises à l'intérieur de tissus biologiques ; lorsque ce sont des impulsions radiofréquences qui sont employées, on parle alors de thermoacoustique. Une partie de l'énergie émise par le laser est alors convertie en chaleur, entraînant un régime transitoire thermoélastique et produisant l'émission d'ultrasons. Les ultrasons générés sont alors mesurés par un transducteur ultrasonore, puis analysés dans le but de générer des images. L’absorption optique est intimement liée aux propriétés physiologiques, citons par exemple la concentration en hémoglobine et la saturation en oxygène. L'amplitude de l'émission d'ultrasons étant proportionnelle à la quan

Source officielle

https://fr.wikipedia.org/wiki/Imagerie_photoacoustique_pour_la_biom%C3%A9decine

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (6)

Publications associées

Chargement

Personnes associées (2)

Personnes associées

Chargement

Unités associées (3)

Unités associées

Chargement

Concepts associés (1)

Concepts associés

Chargement

Cours associés (3)

Cours associés

Chargement

Séances de cours associées (5)

Afficher plus

Séances de cours associées

Chargement

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

Zafer Dogan

Foundations of signal processing are heavily based on Shannon's sampling theorem for acquisition, representation and reconstruction. This theorem states that signals should not contain frequency components higher than the Nyquist rate, which is half of the sampling rate. Then, the signal can be perfectly reconstructed from its samples. Increasing evidence shows that the requirements imposed by Shannon's sampling theorem are too conservative for many naturally-occurring signals, which can be accurately characterized by sparse representations that require lower sampling rates closer to the signal's intrinsic information rates. Finite rate of innovation (FRI) is a new theory that allows to extract underlying sparse signal representations while operating at a reduced sampling rate. The goal of this PhD work is to advance reconstruction techniques for sparse signal representations from both theoretical and practical points of view. Specifically, the FRI framework is extended to deal with applications that involve temporal and spatial localization of events, including inverse source problems from radiating fields. We propose a novel reconstruction method using a model-fitting approach that is based on minimizing the fitting error subject to an underlying annihilation system given by the Prony's method. First, we showed that this is related to the problem known as structured low-rank matrix approximation as in structured total least squares problem. Then, we proposed to solve our problem under three different constraints using the iterative quadratic maximum likelihood algorithm. Our analysis and simulation results indicate that the proposed algorithms improve the robustness of the results with respect to common FRI reconstruction schemes. We have further developed the model-fitting approach to analyze spontaneous brain activity as measured by functional magnetic resonance imaging (fMRI). For this, we considered the noisy fMRI time course for every voxel as a convolution between an underlying activity inducing signal (i.e., a stream of Diracs) and the hemodynamic response function (HRF). We then validated this method using experimental fMRI data acquired during an event-related study. The results showed for the first time evidence for the practical usage of FRI for fMRI data analysis. We also addressed the problem of retrieving a sparse source distribution from the boundary measurements of a radiating field. First, based on Green's theorem, we proposed a sensing principle that allows to relate the boundary measurements to the source distribution. We focused on characterizing these sensing functions with particular attention for those that can be derived from holomorphic functions as they allow to control spatial decay of the sensing functions. With this selection, we developed an FRI-inspired non-iterative reconstruction algorithm. Finally, we developed an extension to the sensing principle (termed eigensensing) where we choose the spatial eigenfunctions of the Laplace operator as the sensing functions. With this extension, we showed that eigensensing principle allows to extract partial Fourier measurements of the source functions from boundary measurements. We considered photoacoustic tomography as a potential application of these theoretical developments.

EPFL2015

Chargement

, , ,

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 techniques 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 news 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.

Spie-Int Soc Optical Engineering2013

Chargement

Eigensensing And Deconvolution For The Reconstruction Of Heat Absorption Profiles From Photoacoustic Tomography Data

Thierry Blu, Zafer Dogan, Dimitri Nestor Alice Van De Ville

Photoacoustic tomography (PAT) is a relatively recent imaging modality that is promising for breast cancer detection and breast screening. It combines the high intrinsic contrast of optical radiation with acoustic imaging at submillimeter spatial resolution through the photoacoustic effect of absorption and thermal expansion. However, image reconstruction from boundary measurements of the propagating wave field is still a challenging inverse problem. Here we propose a new theoretical framework, for which we coin the term eigensensing, to recover the heat absorption profile of the tissue. One of the main features of our method is that there is no explicit forward model that needs to be used within a (usually) slow iterative scheme. Instead, the eigensensing principle allow us to computationally obtain several intermediate images that are blurred by known convolution kernels which are chosen as the eigenfunctions of the spatial Laplace operator. The source image can then be reconstructed by a joint deconvolution algorithm that uses the intermediate images as input. Moreover, total variation regularization is added to make the inverse problem well-posed and to favor piecewise-smooth images.

Ieee2013

Chargement

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

Zafer Dogan

EPFL2015