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.
Regular monitoring of brain perfusion at the bedside in neurointensive care is desirable. Currently used imaging
modalities are not suited for constant monitoring and often require a transport of the patient. Noninvasive near infrared
spectroscopy (NIRS) in combination with an injection of a safe dye (indocyanine green, ICG) could serve as a quasi-continuous
brain perfusion monitor. In this work, we evaluate prerequisites for the development of a brain perfusion
monitor using continuous wave (cw) NIRS technique. We present results from a high-resolution diffuse optical
tomography (HR-DOT) experiment in humans demonstrating the separation of signals from skin from the brain. This
technique can help to monitor neurointensive care patients on a regular basis, detecting changes in cortical perfusion in
Near infrared spectroscopy (NIRS) and diffuse optical tomography (DOT) of the brain reveal no information about the
measurement's underlying anatomical structures. An independent anatomical mapping of DOT results onto the subject's
brain or a generic brain model is desirable, especially when regions prone to large inter-subject variability are studied.
We show two methods to match DOT data from high density fiber grids to anatomical structures. The forward model that
is used to predict the light propagation is based on one generic anatomical MR scan. In both approaches we use this
model MR-scan to translocate the position of the optical fiber grid from our experimental setup to the FEM model space.
The first method, using fiduciary marks, achieves the spatial normalization of the subject's MR-scan (with marked
corners of the fiber grid) and the model's MR scan, leading to a translocation of the fiber pad position to the FEM-Model
space. The second, anatomic landmark based, approach does not require the individual's MR scan. For this, 19 reference
points and the position of the fiber pad corners are determined using photogrammetry software. These coordinates are
translocated to the FEM model space by solving the least square problem of the subject's and the model's reference
points. We illustrate and compare both methods and show results from a vibrotactile stimulation experiment in humans.