title: Compressive sensing based reconstruction in bioluminescence tomography improves image resolution and robustness to noise.
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.
authors.affiliation: PSIBS, University of Birmingham, Edgbaston, Birmingham, B15 2TT,  UK ; School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT,  UK.
authors.forename: Hector R A
authors.initials: HR
authors.lastname: Basevi
authors.forename: Kenneth M
authors.initials: KM
authors.lastname: Tichauer
authors.forename: Frederic
authors.initials: F
authors.lastname: Leblond
authors.forename: Hamid
authors.initials: H
authors.lastname: Dehghani
authors.forename: James A
authors.initials: JA
authors.lastname: Guggenheim
authors.forename: Robert W
authors.initials: RW
authors.lastname: Holt
authors.forename: Iain B
authors.initials: IB
authors.lastname: Styles