Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography

Christina Habermehl, Jens Steinbrink, Klaus Muller, Stefan Haufe

Research output: Contribution to journalArticle

17 Citations (Scopus)

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 l2-norm estimate (l2MNE) and truncated singular value decomposition], recently proposed sparse reconstruction algorithms [minimum l1- and a smooth minimum l0-norm estimate (l1MNE, l0MNE, 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]. Indepedent 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 l1-norm estimate succeeds with multiple targets.

Original languageEnglish
Article number096006
JournalJournal of Biomedical Optics
Volume19
Issue number9
DOIs
Publication statusPublished - 2014 Sep 1

Fingerprint

Optical Tomography
Computer-Assisted Image Processing
Optical tomography
image reconstruction
Image reconstruction
tomography
norms
Near infrared spectroscopy
Chemical activation
activation
Near-Infrared Spectroscopy
Inverse problems
brain
Brain
estimates
Noise
infrared spectroscopy
electroencephalography
Surface measurement
beamforming

ASJC Scopus subject areas

  • Biomedical Engineering
  • Biomaterials
  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics

Cite this

Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography. / Habermehl, Christina; Steinbrink, Jens; Muller, Klaus; Haufe, Stefan.

In: Journal of Biomedical Optics, Vol. 19, No. 9, 096006, 01.09.2014.

Research output: Contribution to journalArticle

Habermehl, Christina ; Steinbrink, Jens ; Muller, Klaus ; Haufe, Stefan. / Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography. In: Journal of Biomedical Optics. 2014 ; Vol. 19, No. 9.
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