Compressive sensing based reconstruction in bioluminescence tomography improves image resolution and robustness to noise

Research output: Contribution to journalArticlepeer-review


  • Kenneth M Tichauer
  • Frederic Leblond
  • James A Guggenheim
  • Robert W Holt

Colleges, School and Institutes


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
Issue number9
Publication statusPublished - 1 Sep 2012