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 language | English |
|---|---|
| Article number | 096006 |
| Journal | Journal of Biomedical Optics |
| Volume | 19 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 2014 Sep 1 |
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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 journal › Article
}
TY - JOUR
T1 - Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography
AU - Habermehl, Christina
AU - Steinbrink, Jens
AU - Muller, Klaus
AU - Haufe, Stefan
PY - 2014/9/1
Y1 - 2014/9/1
N2 - 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.
AB - 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.
KW - Brain imaging
KW - Diffuse optical tomography
KW - Image reconstruction
KW - Inverse problem
KW - Near-infrared spectroscopy
KW - Simulation
UR - http://www.scopus.com/inward/record.url?scp=84911492390&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84911492390&partnerID=8YFLogxK
U2 - 10.1117/1.JBO.19.9.096006
DO - 10.1117/1.JBO.19.9.096006
M3 - Article
C2 - 25208243
AN - SCOPUS:84911492390
VL - 19
JO - Journal of Biomedical Optics
JF - Journal of Biomedical Optics
SN - 1083-3668
IS - 9
M1 - 096006
ER -