Share Email Print
cover

Proceedings Paper

Free-space fluorescence tomography with adaptive sampling based on anatomical information from microCT
Author(s): Xiaofeng Zhang; Cristian T. Badea; Greg Hood; Arthur W. Wetzel; Joel R. Stiles; G. Allan Johnson
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Image reconstruction is one of the main challenges for fluorescence tomography. For in vivo experiments on small animals, in particular, the inhomogeneous optical properties and irregular surface of the animal make free-space image reconstruction challenging because of the difficulties in accurately modeling the forward problem and the finite dynamic range of the photodetector. These two factors are fundamentally limited by the currently available forward models and photonic technologies. Nonetheless, both limitations can be significantly eased using a signal processing approach. We have recently constructed a free-space panoramic fluorescence diffuse optical tomography system to take advantage of co-registered microCT data acquired from the same animal. In this article, we present a data processing strategy that adaptively selects the optical sampling points in the raw 2-D fluorescent CCD images. Specifically, the general sampling area and sampling density are initially specified to create a set of potential sampling points sufficient to cover the region of interest. Based on 3-D anatomical information from the microCT and the fluorescent CCD images, data points are excluded from the set when they are located in an area where either the forward model is known to be problematic (e.g., large wrinkles on the skin) or where the signal is unreliable (e.g., saturated or low signal-to-noise ratio). Parallel Monte Carlo software was implemented to compute the sensitivity function for image reconstruction. Animal experiments were conducted on a mouse cadaver with an artificial fluorescent inclusion. Compared to our previous results using a finite element method, the newly developed parallel Monte Carlo software and the adaptive sampling strategy produced favorable reconstruction results.

Paper Details

Date Published: 23 February 2010
PDF: 8 pages
Proc. SPIE 7557, Multimodal Biomedical Imaging V, 755706 (23 February 2010); doi: 10.1117/12.841891
Show Author Affiliations
Xiaofeng Zhang, Duke Univ. Medical Ctr. (United States)
Cristian T. Badea, Duke Univ. Medical Ctr. (United States)
Greg Hood, Carnegie Mellon Univ. (United States)
Arthur W. Wetzel, Carnegie Mellon Univ. (United States)
Joel R. Stiles, Carnegie Mellon Univ. (United States)
G. Allan Johnson, Duke Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 7557:
Multimodal Biomedical Imaging V
Fred S. Azar; Xavier Intes, Editor(s)

© SPIE. Terms of Use
Back to Top