Compressed sensing reconstruction of statistical parameter map for functional diffuse optical tomography

Functional near infrared spectroscopy (fNIRS) is a non-invasive imaging modality to measure functional brain activities. Many researches have investigated diffuse optical tomography (DOT) to overcome the limitation of lack of depth information in fNIRS topographic approach. In this paper, we proposes a novel compressed sensing approach, especially using a 2-thresholding algorithm, that directly reconstructs statistical parameter maps by exploiting spatio-temporal sparsity of neural activation. The final reconstruction algorithm has very intuitive form which is similar to conventional fDOT with SPM analysis. However, the main advantage of the new algorithm is that the unknown weighting components in inversion kernel are iteratively updated for more accurate reconstruction which significantly improves the reconstruction performance. Experimental results demonstrated that the localization error using the proposed method is competitive with that of fMRI.