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

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)2131-41
Number of pages11
JournalBiomedical Optics Express
Volume3
Issue number9
DOIs
Publication statusPublished - 1 Sep 2012

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