Parametric level set reconstruction methods for hyperspectral diffuse optical tomography

Parametric level set reconstruction methods for hyperspectral diffuse optical tomography

A parametric level set method (PaLS) is implemented for image reconstruction for hyperspectral diffuse optical tomography (DOT). Chromophore concentrations and diffusion amplitude are recovered using a linearized Born approximation model and employing data from over 100 wavelengths. The images to be recovered are taken to be piecewise constant and a newly introduced, shape-based model is used a...

Hyperspectral image reconstruction for diffuse optical tomography

We explore the development and performance of algorithms for hyperspectral diffuse optical tomography (DOT) for which data from hundreds of wavelengths are collected and used to determine the concentration distribution of chromophores in the medium under investigation. An efficient method is detailed for forming the images using iterative algorithms applied to a linearized Born approximation mo...

Hyperspectral Imaging for Diffuse Optical Tomography

Diffuse optical tomography (DOT) has emerged in the last decade as a new and exciting tool for functional medical imaging with applications in a range of areas including breast cancer detection and diagnosis. DOT employs observations of near infrared (NIR) light that has propagated through tissue to reconstruct the spatial distribution of various chromophores present in the region of interest. ...

Fast Algorithms for Hyperspectral Diffuse Optical Tomography

diffuse optical tomography: image reconstruction and verification

introduction: in this study, we intend to use diffuse optical tomography (dot) as a noninvasive, safe and low cost technique that can be considered as a functional imaging method and mention the importance of image reconstruction in accuracy and procession of image. one of the most important and fastest methods in image reconstruction is the boundary element method (bem). this method is introdu...