An algorithm to estimate the object support in truncated images.

Purpose: Truncation artifacts in CT occur if the object to be imaged extends past the scanner field of view (SFOV). These artifacts impede diagnosis and could possibly introduce errors in dose plans for radiation therapy. Several approaches exist for correcting truncation artifacts, but existing correction algorithms do not accurately recover the skin line (or support) of the patient, which is important in some dose planning methods.

The sparse matrix transform for covariance estimation and analysis of high dimensional signals.

Covariance estimation for high dimensional signals is a classically difficult problem in statistical signal analysis and machine learning. In this paper, we propose a maximum likelihood (ML) approach to covariance estimation, which employs a novel non-linear sparsity constraint. More specifically, the covariance is constrained to have an eigen decomposition which can be represented as a sparse matrix transform (SMT).

Noniterative MAP reconstruction using sparse matrix representations.

We present a method for noniterative maximum a posteriori (MAP) tomographic reconstruction which is based on the use of sparse matrix representations. Our approach is to precompute and store the inverse matrix required for MAP reconstruction. This approach has generally not been used in the past because the inverse matrix is typically large and fully populated (i.

Localization of an absorbing inhomogeneity in a scattering medium in a statistical framework.

An approach for the fast localization and detection of an absorbing inhomogeneity in a tissuelike scattering medium is presented. The probability of detection as a function of the size, location, and absorptive properties of the inhomogeneity is investigated. The detection sensitivity in relation to the source and detector location serves as a basis for instrument design.