by Hector R. A. Basevi, Kenneth M. Tichauer, Frederic Leblond, Hamid Dehghani, James A. Guggenheim, Robert W. Holt, Iain B. Styles
Abstract:
Bioluminescence Tomography attempts to quantify 3-dimensional luminophore distributions from surface measurements of the light distribution. The reconstruction problem is typically severely under-determined due to the number and location of measurements, but in certain cases the molecules or cells of interest form localised clusters, resulting in a distribution of luminophores that is spatially sparse. A Conjugate Gradient-based reconstruction algorithm using Compressive Sensing was designed to take advantage of this sparsity, using a multistage sparsity reduction approach to remove the need to choose sparsity weighting a priori. Numerical simulations were used to examine the effect of noise on reconstruction accuracy. Tomographic bioluminescence measurements of a Caliper XPM-2 Phantom Mouse were acquired and reconstructions from simulation and this experimental data show that Compressive Sensing-based reconstruction is superior to standard reconstruction techniques, particularly in the presence of noise.
Reference:
Compressive sensing based reconstruction in bioluminescence tomography improves image resolution and robustness to noise (Hector R. A. Basevi, Kenneth M. Tichauer, Frederic Leblond, Hamid Dehghani, James A. Guggenheim, Robert W. Holt, Iain B. Styles), In Biomed. Opt. Express, OSA, volume 3, 2012.
Bibtex Entry:
@article{basevi2012compressive,
author = {Hector R. A. Basevi and Kenneth M. Tichauer and Frederic Leblond and Hamid Dehghani and James A. Guggenheim and Robert W. Holt and Iain B. Styles},
journal = {Biomed. Opt. Express},
keywords = {Image reconstruction techniques; Spectroscopy, fluorescence and luminescence; Tomography},
number = {9},
pages = {2131--2141},
publisher = {OSA},
title = {Compressive sensing based reconstruction in bioluminescence tomography improves image resolution and robustness to noise},
volume = {3},
month = {Sep},
year = {2012},
url = {http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-3-9-2131},
doi = {10.1364/BOE.3.002131},
abstract = {Bioluminescence Tomography attempts to quantify 3-dimensional luminophore distributions from surface measurements of the light distribution. The reconstruction problem is typically severely under-determined due to the number and location of measurements, but in certain cases the molecules or cells of interest form localised clusters, resulting in a distribution of luminophores that is spatially sparse. A Conjugate Gradient-based reconstruction algorithm using Compressive Sensing was designed to take advantage of this sparsity, using a multistage sparsity reduction approach to remove the need to choose sparsity weighting a priori. Numerical simulations were used to examine the effect of noise on reconstruction accuracy. Tomographic bioluminescence measurements of a Caliper XPM-2 Phantom Mouse were acquired and reconstructions from simulation and this experimental data show that Compressive Sensing-based reconstruction is superior to standard reconstruction techniques, particularly in the presence of noise.},
}