Iterative reconstruction method for light emitting sources based on the diffusion equation

Abstract

Bioluminescent imaging (BLI) of luciferase-expressing cells in live small animals is a powerful technique for investigating tumor growth, metastasis, and specific biological molecular events. Three-dimensional imaging would greatly enhance applications in biomedicine since light emitting cell populations could be unambiguously associated with specific organs or tissues. Any imaging approach must account for the main optical properties of biological tissue because light emission from a distribution of sources at depth is strongly attenuated due to optical absorption and scattering in tissue. Our image reconstruction method for interior sources is based on the deblurring expectation maximization method and takes into account both of these effects. To determine the boundary of the object we use the standard iterative algorithm-maximum likelihood reconstruction method with an external source of diffuse light. Depth-dependent corrections were included in the reconstruction procedure to obtain a quantitative measure of light intensity by using the diffusion equation for light transport in semi-infinite turbid media with extrapolated boundary conditions.

Image reconstruction for bioluminescence tomography from partial measurement

“…It only works in 2D imaging mode and is incapable of 3D imaging of the light source location associated with specific organs and tissues [2]. Since its first introduction in 2003 [11,12], the bioluminescence tomography (BLT) has been undergone a rapid development [11,12,13,14,15,16,17,18,19,20,21,4,22,2,23,24]. BLT is to address the needs for 3D localization and quantification of an internal bioluminescent source distribution in a small animal.…”

“…With BLT, optimal analyzes on a bioluminescent source distribution become feasible inside a living mouse. Although there are currently several approaches for this technique [11,12,13,14,15,16,17,18,19,20,21,4,22,2,23,24], the originally proposed approach in [11,12] is still one of the promising techniques in this field. With this technique, the complete knowledge on the optical properties of anatomical components is assumed to be available from an independent tomography scan, such as a CT/micro-CT, MRI scan and/or diffuse optical tomography (DOT), by image segmentation and optical property mapping.…”

Current Optical Imaging Techniques for Brain Tumor Research: Application of in vivo Laser Scanning Microscopy Imaging with a Cranial Window System

“…Disturbances in the light transmission due to light absorption by hemoglobin and signal attenuation by melanin and fur also limit application of BLI (O'Neill et al, 2010). Additionally, even though advances have been made to translate BLI data into three-dimensional (3D) tomographic imaging (Chaudhari et al, 2005;Slavine et al, 2006), commonly used BLI has limited use for 3D reconstruction. Kircher et al, 2003;Veiseh et al, 2007) CLSM 0.5-1.0 m < 100-200 m…”

Practical method for radioactivity distribution analysis in small-animal PET cancer studies

“…we applied the interactive utility for visualizing data on a PC graphic display Physics Analysis Workstation (PAW 1995) based on a statistical analysis and histogram package (HBOOK 1998). Note, the algorithm based on PAW was successfully used for volume size quantification in our bioluminescence imaging investigations (Richer et al 2004,Slavine et al 2006. Now, after we reconstruct the intensity and size of the source and determine the geometry of interested ROI, we can calculate a radioactivity distribution value (RDV) for tumor and mouse organs.…”