Compressive sensing based reconstruction in bioluminescence tomography improves image resolution and robustness to noise
Research output: Contribution to journal › Article › peer-review
Authors
Colleges, School and Institutes
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.
Details
Original language | English |
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Pages (from-to) | 2131-41 |
Number of pages | 11 |
Journal | Biomedical Optics Express |
Volume | 3 |
Issue number | 9 |
Publication status | Published - 1 Sep 2012 |