Time-resolved Diffuse Optical Tomography is a valuable tool to localize and characterize heterogeneities inside a biological tissue. Novel strategies based on structured light illumination and compressive-sensing detection have been exploited to reduce the dataset while preserving the information content. In this work, we present a setup based on those strategies implementing an adaptive scheme based on Singular-Value Decomposition (SVD) to generate a set of optimal input and output patterns.

This presentation is part of Minisymposium “MS61 - Imaging with Light and Sound (3 parts)”
organized by: Felix Lucka (CWI & UCL) , Tanja Tarvainen (University of Eastern Finland) .

Authors:

Cosimo D'Andrea (Politecnico di Milano)

Andrea Farina (Consiglio Nazionale delle Ricerche (CNR), Istituto di Fotonica e Nanotecnologie (IFN))

Marta Betcke (University College London)

Andrea Bassi (Dept. Physics - Politecnico di Milano)

Gianluca Valentini (Dept. Physics - Politecnico di Milano)

Simon Arridge (University College London)

Keywords:

image compression, image reconstruction, inverse problems