Edge-promoting reconstruction of absorption and diffusivity in optical tomography

In optical tomography a physical body is illuminated with near-infrared light and the resulting outward photon flux is measured at the object boundary. The goal is to reconstruct internal optical properties of the body, such as absorption and diffusivity. In this work, it is assumed that the imaged object is composed of an approximately homogeneous background with clearly distinguishable embedded inhomogeneities. An algorithm for finding the maximum a posteriori estimate for the absorption and diffusion coefficients is introduced assuming an edge-preferring prior and an additive Gaussian measurement noise model. The method is based on iteratively combining a lagged diffusivity step and a linearization of the measurement model of diffuse optical tomography with priorconditioned LSQR. The performance of the reconstruction technique is tested via three-dimensional numerical experiments with simulated measurement data.Comment: 18 pages, 6 figure

Sparsity Regularization in Diffuse Optical Tomography

The purpose of this dissertation is to improve image reconstruction in Diffuse Optical Tomography (DOT), a high contrast imaging modality that uses a near infrared light source. Because the scattering and absorption of a tumor varies significantly from healthy tissue, a reconstructed spatial representation of these parameters serves as tomographic image of a medium. However, the high scatter and absorption of the optical source also causes the inverse problem to be severely ill posed, and currently only low resolution reconstructions are possible, particularly when using an unmodulated direct current (DC) source. In this work, the well posedness of the forward problem and possible function space choices are evaluated, and the ill posed nature of the inverse problem is investigated along with the uniqueness issues stemming from using a DC source. Then, to combat the ill posed nature of the problem, a physically motivated additional assumption is made that the target reconstructions have sparse solutions away from simple backgrounds. Because of this, and success with a similar implementation in Electrical Impedance Tomography, a sparsity regularization framework is applied to the DOT inverse problem. The well posedness of this set up is rigorously proved through new regularization theory results and the application of a Hilbert space framework similar to recent work. With the sparsity framework justified in the DOT setting, the inverse problem is solved through a novel smoothed gradient and soft shrinkage algorithm. The effectiveness of the algorithm, and the sparsity regularization of DOT, is evaluated through several numerical simulations using a DC source with comparison to a Levenberg Marquardt implementation and published error results

Simultaneous uniqueness and numerical inversion for an inverse problem in the time-domain diffuse optical tomography with fluorescence

In this work, an inverse problem on the determination of multiple coefficients arising from the time-domain diffuse optical tomography with fluorescence (DOT-FDOT) is investigated. We simultaneously recover the distribution of background absorption coefficient, photon diffusion coefficient as well as the fluorescence absorption in biological tissue by the time-dependent boundary measurements. We build the uniqueness theorem of this multiple coefficients simultaneous inverse problem. After that, the numerical inversions are considered. We introduce an accelerated Landweber iterative algorithm and give several numerical examples illustrating the performance of the proposed inversion schemes

Mathematical Methods in Tomography

This is the seventh Oberwolfach conference on the mathematics of tomography, the first one taking place in 1980. Tomography is the most popular of a series of medical and scientific imaging techniques that have been developed since the mid seventies of the last century

EIT Reconstruction Algorithms: Pitfalls, Challenges and Recent Developments

We review developments, issues and challenges in Electrical Impedance Tomography (EIT), for the 4th Workshop on Biomedical Applications of EIT, Manchester 2003. We focus on the necessity for three dimensional data collection and reconstruction, efficient solution of the forward problem and present and future reconstruction algorithms. We also suggest common pitfalls or ``inverse crimes'' to avoid.Comment: A review paper for the 4th Workshop on Biomedical Applications of EIT, Manchester, UK, 200

Mathematics and Algorithms in Tomography

This is the eighth Oberwolfach conference on the mathematics of tomography. Modalities represented at the workshop included X-ray tomography, sonar, radar, seismic imaging, ultrasound, electron microscopy, impedance imaging, photoacoustic tomography, elastography, vector tomography, and texture analysis