Dr. Christina Habermehl Profile

Dr. Christina Habermehl

at Charité Universitätsmedizin Berlin

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Publications (3)

SPIE Journal Paper | 10 September 2014 Open Access

Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography

Christina Habermehl, Jens Steinbrink, Klaus-Robert Müller, Stefan Haufe

JBO, Vol. 19, Issue 09, 096006, (September 2014) https://doi.org/10.1117/12.10.1117/1.JBO.19.9.096006

KEYWORDS: Image restoration, Reconstruction algorithms, Brain, Data modeling, Phased arrays, Interference (communication), Brain activation, Absorption, Inverse problems, Head

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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]. 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 ℓ1-norm estimate succeeds with multiple targets.

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Proceedings Article | 9 February 2012 Paper

Contrast-enhanced diffuse optical tomography of brain perfusion in humans using ICG

Christina Habermehl, Christoph Schmitz, Jens Steinbrink

Proceedings Volume 8207, 82074O (2012) https://doi.org/10.1117/12.905742

KEYWORDS: Brain, Near infrared spectroscopy, Absorption, Optical fibers, Skin, Sensors, Diffuse optical tomography, Tissue optics, Imaging systems

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Proceedings Article | 15 June 2011 Paper

Three-dimensional superposition of diffuse optical tomography results and subjacent anatomic structures

Christina Habermehl, Christoph Schmitz, Jan Mehnert, Susanne Holtze, Jens Steinbrink

Proceedings Volume 8088, 80880I (2011) https://doi.org/10.1117/12.889372

KEYWORDS: Head, Brain, Optical fibers, Near infrared spectroscopy, Finite element methods, Diffuse optical tomography, Brain mapping, Neuroimaging, Magnetic resonance imaging, Systems modeling

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