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.

Evaluation of temporal moments and Fourier transformed data in time-domain diffuse optical tomography

“…Moment Reconstructed parameters Application [8] E Difference µ a Cylindrical phantom [27] <t>, c 2 , LT Difference and absolute µ a , µ s Cylindrical phantom [10] <t>, c 2 , LT Absolute µ a , µ s Cylindrical phantom [28] <t> Difference and absolute µ a , µ s Human forearm movement [29] <t> Difference µ a , µ s Infant brain haemorrage [16] Whole data, E, <t>, c 2 , c 3 . Absolute µ a , µ s Simulations [23] Fourier transform Difference µ a , µ s Infant brain cortical response [30] Fourier transform Absolute µ a , µ s Cylindrical phantom [31] <t> Difference µ a , µ s Infant brain oxygenation [32] Whole data Difference µ a Liquid phantom tank [22] <t> Absolute µ a Cylindrical phantom [26] Mellin-Laplace transform Absolute µ a , µ s Simulations [17] Whole data Difference µ a , µ s Simulations [33] Mellin-Laplace transform Difference µ a Cuboid phantom [15] E Absolute µ a Breast cancer phantom [34] Temporal windows Absolute µ a Simulations…”

Real-Time Dual-Wavelength Time-Resolved Diffuse Optical Tomography System for Functional Brain Imaging Based on Probe-Hosted Silicon Photomultipliers

“…Tomographic reconstruction was performed using an algorithm based on Mellin-Laplace moments [31,38]. The algorithm was initialized by using the average optical properties estimated from fitting the DTOFs of the resting periods (before performing arm occlusion or finger-tapping).…”

Special Issue on New Horizons in Time Domain Diffuse Optical Spectroscopy and Imaging

“…In TD DOT, regarding the datatypes obtained from the TPSF, such as temporal windows and Fourier transformations, determining which datatypes are used for image reconstruction is crucial for computational efficiency as well as for image quality. Orive-Miguel et al propose a new process for the efficient computation of long sets of temporal windows in the FD and demonstrate that the absorption quantification of the inclusions in a rectangular medium is improved at all depths in numerical experiments by the proposed method [25]. The M-th order delta-Eddington equation (dEM) is used as one effective approach to reduce the computational cost of a numerical solution to the RTE.…”