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Journal of Biomedical Optics • Open Access

Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography
Author(s): Christina Habermehl; Jens M. Steinbrink; Klaus-Robert Müller; Stefan Haufe

Paper Abstract

Functional near-infrared spectroscopy (fNIRS) is an optical method for noninvasively determining brain activation by estimating changes in the absorption of near-infrared light. Diffuse optical tomography (DOT) extends fNIRS by applying overlapping “high density” measurements, and thus providing a three-dimensional imaging with an improved spatial resolution. Reconstructing brain activation images with DOT requires solving an underdetermined inverse problem with far more unknowns in the volume than in the surface measurements. All methods of solving this type of inverse problem rely on regularization and the choice of corresponding regularization or convergence criteria. While several regularization methods are available, it is unclear how well suited they are for cerebral functional DOT in a semi-infinite geometry. Furthermore, the regularization parameter is often chosen without an independent evaluation, and it may be tempting to choose the solution that matches a hypothesis and rejects the other. In this simulation study, we start out by demonstrating how the quality of cerebral DOT reconstructions is altered with the choice of the regularization parameter for different methods. To independently select the regularization parameter, we propose a cross-validation procedure which achieves a reconstruction quality close to the optimum. Additionally, we compare the outcome of seven different image reconstruction methods for cerebral functional DOT. The methods selected include reconstruction procedures that are already widely used for cerebral DOT [minimum 2-norm estimate (2MNE) and truncated singular value decomposition], recently proposed sparse reconstruction algorithms [minimum1- and a smooth minimum0-norm estimate (1MNE, 0MNE, respectively)] and a depth- and noise-weighted minimum norm (wMNE). Furthermore, we expand the range of algorithms for DOT by adapting two EEG-source localization algorithms [sparse basis field expansions and linearly constrained minimum variance (LCMV) beamforming]. Independent of the applied noise level, we find that the LCMV beamformer is best for single spot activations with perfect location and focality of the results, whereas the minimum 1-norm estimate succeeds with multiple targets.

Paper Details

Date Published: 10 September 2014
PDF: 15 pages
J. Biomed. Opt. 19(9) 096006 doi: 10.1117/1.JBO.19.9.096006
Published in: Journal of Biomedical Optics Volume 19, Issue 9
Show Author Affiliations
Christina Habermehl, Berlin Institute of Technology (Germany)
Bernstein Focus Neurotechnology (Germany)
Charité University Medicine (Germany)
Jens M. Steinbrink, Bernstein Focus Neurotechnology (Germany)
Charité Univ. Medicine (Germany)
Klaus-Robert Müller, Berlin Institute of Technology (Germany)
Bernstein Focus Neurotechnology (Germany)
Bernstein Ctr. for Computational Neuroscience (Germany)
Stefan Haufe, Berlin Institute of Technology (Germany)
Bernstein Focus Neurotechnology (Germany)


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