Performance dependence of hybrid x-ray computed tomography/fluorescence molecular tomography on the optical forward problem

Abstract

Hybrid imaging systems combining x-ray computed tomography (CT) and fluorescence tomography can improve fluorescence imaging performance by incorporating anatomical x-ray CT information into the optical inversion problem. While the use of image priors has been investigated in the past, little is known about the optimal use of forward photon propagation models in hybrid optical systems. In this paper, we explore the impact on reconstruction accuracy of the use of propagation models of varying complexity, specifically in the context of these hybrid imaging systems where significant structural information is known a priori. Our results demonstrate that the use of generically known parameters provides near optimal performance, even when parameter mismatch remains.

Hybrid µCT-FMT imaging and image analysis

“…Furthermore, the mouse shape is automatically segmented and this information significantly improves the fluorescence reconstruction 11,13,22 . For quantitative fluorescence reconstruction, absorption and scattering maps are needed 13,23 . We derive the scattering map by automated segmentation of the µCT data and assigning known scattering coefficients of several tissue types (lung, bone, skin, fat, and remaining soft tissue) 24 .…”

In vivo small animal imaging: Current status and future prospects

“…143 The first optical imaging and PET prototype has been designed and initial results demonstrate its potential in preclinical research. 144,145 There are some less obvious combinations for multimodal systems technology including optical with CT, 146 x ray with MRI, 147 and optical with MRI.…”

Sparsity-Driven Reconstruction for FDOT With Anatomical Priors

“…That is, there exists such that (16) We can now define the primal and dual objective functions and , respectively…”

“…In the context of fluorescence tomography, anatomical priors were either used to enhance the forward modeling, or directly in the reconstruction procedure. In [16] the authors report that improving the forward model with structural information leads to more accurate images. On the reconstruction side, the structural a priori knowledge often takes form of a labeling of the pixels, derived from a segmentation of the anatomical image.…”