# Diffuse Optical Tomography Using Bayesian Filtering in the Human Brain
## 3 Results and Discussion
### Imaging Results
However, they use a simpler topography approach with fewer source-detector pairs (~16) and without complex mathematic models to reconstruct three-dimensional (3D) images, in contrast to DOT. This is a clear example of how data filtering affects image reconstruction. The high fidelity of Bayesian filtering on raw DOT data has been tested using motor tasks to generate cerebral activity in temporal lobes [29], even during transcranial magnetic stimulation [13]. Some studies have used the Bayesian method in different steps during the DOT reconstruction, such as in the inverse problem [30], in order to improve the depth accuracy for the forward model [31], or to remove the scalp artefact [32], but most of them have tested the method in phantoms or simulation models Table 1. Representation [TC33] [S34] of number of voxels common by anatomical area between HbO-BOLD (T: 2.33, \(p<0.05\)) \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Anatomical Region**} & \multicolumn{2}{c}{**Peak MNI Coordinate**} & \multirow{2}{*}{**Num. Voxels**} \\  & **x** & **y** & **z** & \\ \hline Frontal Superior Left & \(-20\) & 52 & 4 & 13 \\ Frontal Medial Right & 38 & 40 & 4 & 97 \\ Frontal Superior Medial Right & 10 & 60 & 14 & 6 \\ Cingulum Anterior Left & \(-8\) & 46 & 8 & 4 \\ Frontal Superior Medial Left & \(-8\) & 58 & 14 & 31 \\ Frontal Superior Right & 24 & 58 & 8 & 4 \\ Frontal Superior Medial Left & \(-14\) & 56 & 4 & 19 \\ Total num. voxels & & & 174 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Representation of number of voxels common by anatomical area between oxyhemoglobin-blood-oxygen level-dependent (HbO-BOLD) (T: 2.33, \(p<0.05\))  
Figure 4: T-maps of brain activation measured by DOT and functional magnetic resonance imaging (fMRI) devices in the same subject. All results were mapped onto individual anatomy. Threshold \(p<0.001\) corrected false discovery rate (FDR), at the voxel level for BOLD signal (**a**) and HbO signal (**b**) in a healthy subject. Note that cerebral activation maps provided by DOT data filtered with a Bayesian method follow a similar spatial distribution to maps provided by fMRI, using the general Linear Model (GLM) as a statistical method for both neuroimaging modalities. Here, we present the new approach on the raw DOT data recorded in the human brain. Other authors have modeled and compared with the BOLD signal as well [33]; however, they have applied the method in other steps during the DOT image reconstruction and have used DOT equipment with different characteristics than in the present work. The physiological changes (cardiac and respiratory) must be recorded during the experimental period in order to apply the Bayesian filter, because they are used as regressors and removed from the raw data. Future studies could record other variables such as the motion artifact time variable, which could also be used as a regressor to improve the quality of the results. Finally, we are inclined to think that the results would be similar even if the physiological modeling approach used phases instead of frequencies.