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Improving Localization of Deep Inclusions in Time-Resolved Diffuse Optical Tomography

Abstract : Time-resolved diffuse optical tomography is a technique used to recover the optical properties of an unknown diffusive medium by solving an ill-posed inverse problem. In time-domain, reconstructions based on datatypes are used for their computational efficiency. In practice, most used datatypes are temporal windows and Fourier transform. Nevertheless, neither theoretical nor numerical studies assessing different datatypes have been clearly expressed. In this paper, we propose an overview and a new process to compute efficiently a long set of temporal windows in order to perform diffuse optical tomography. We did a theoretical comparison of these large set of temporal windows. We also did simulations in a reflectance geometry with a spherical inclusion at different depths. The results are presented in terms of inclusion localization and its absorption coefficient recovery. We show that (1) the new windows computed with the developed method improve inclusion localization for inclusions at deep layers, (2) inclusion absorption quantification is improved at all depths and, (3) in some cases these windows can be equivalent to frequency based reconstruction at GHz order.
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David Orive-Miguel, Lionel Hervé, Laurent Condat, Jerome Mars. Improving Localization of Deep Inclusions in Time-Resolved Diffuse Optical Tomography. Applied Sciences, MDPI, 2019, ⟨10.3390/app9245468⟩. ⟨hal-02476617⟩

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