title: Computationally efficient methods for incorporation of spectral priors in 3-d optical tomography.
abstract: Use of spectral prior in optical tomography has significantly improved accuracy and quality of images, when applied in two-dimensional (2-D) models. However, the size of the problem increases substantially when applied in 3-D. Two methods are presented here that make 3-D spectral imaging computationally feasible. The data-subset approach uses a smaller subset of variable measurement s to reduce the size of the inverse problem. The basic principle consists of using a dynamic criterion to select optimal subset that capture the major changes in the imaging domain. Additionally, the sensitivity matrix is analyzed and made sparse based on a memory requirements (to 8% of full matrix) and provides less tha 2% percent difference in quantification compared to use of full matrices in the image reconstruction.
authors.affiliation: Dartmouth College, Thayer School of Engineering, Hanover, NH -03755, USA . subha@dartmouth.edu
authors.forename: Subhadra
authors.initials: S
authors.lastname: Srinivasan
authors.forename: Brian W
authors.initials: BW
authors.lastname: Pogue
authors.forename: Frederic
authors.initials: F
authors.lastname: Leblond
mesh_terms: D007090:Image Interpretation, Computer-Assisted; D021621:Imaging, Three-Dimensional; D019047:Phantoms, Imaging; D041622:Tomography, Optical / Q000379:methods*