Diffuse optical tomography (DOT) is a promising noninvasive imaging modality capable of providing the functional characteristics (oxygen saturation and hemodynamic states) of thick biological tissue by quantifying the optical parameters. The parameter recovery problem in DOT is a nonlinear, ill-posed and ill conditioned inverse problem. The non-linear iterative methods are usually employed for image reconstruction in DOT by utilizing Tikhonov based regularization approach. These methods employ l2-norm based regularization where the constant regularization parameter is determined either empirically or generalized cross validation methods or L curve method. The reconstructed images look smoother or noisy depending on the chosen value of the regularization constant. Moreover the edges information of the inclusions appeared to be blurred in such constant regularization methods. In this study we proposed a method to retrieve and utilized a non-zero support (possible tumor location) to generate a spatially varying regularization map. The inclusions locations were determined by considering the imaging problem as a multiple measurements vector (MMV) problem. Based on the recovered inclusion positions spatially regularization map was generated to be used in non-linear image reconstruction framework. The results retrieved with such spatially varying priors shows slightly improved image reconstruction in terms of better contrast recovery, reduction in background noise and preservation of edge information of inclusions compared with the constant regularization approach.
We present a methodology for the optimization of sampling schemes in diffuse optical tomography (DOT). The proposed method exploits singular value decomposition (SVD) of the sensitivity matrix, or weight matrix, in DOT. Two mathematical metrics are introduced to assess and determine the optimum source–detector measurement configuration in terms of data correlation and image space resolution. The key idea of the work is to weight each data measurement, or rows in the sensitivity matrix, and similarly to weight each unknown image basis, or columns in the sensitivity matrix, according to their contribution to the rank of the sensitivity matrix, respectively. The proposed metrics offer a perspective on the data sampling and provide an efficient way of optimizing the sampling schemes in DOT. We evaluated various acquisition geometries often used in DOT by use of the proposed metrics. By iteratively selecting an optimal sparse set of data measurements, we showed that one can design a DOT scanning protocol that provides essentially the same image quality at a much reduced sampling.
The optimization of experimental design prior to deployment, not only for cost effective solution but also for computationally efficient image reconstruction has taken up for this study. We implemented the iterative method also known as effective independence (EFI) method for optimization of source/detector pair configuration. The notion behind for adaptive selection of minimally correlated measurements was to evaluate the information content passed by each measurement for estimation of unknown parameter. The EFI method actually ranks measurements according to their contribution to the linear independence of unknown parameter basis. Typically, to improve the solvability of ill conditioned system, regularization parameter is added, which may affect the source/detector selection configuration. We show that the source/detector pairs selected by EFI method were least prone to vary with sub optimal regularization value. Moreover, through series of simulation studies we also confirmed that sparse source/detector pair measurements selected by EFI method offered similar results in comparison with the dense measurement configuration for unknown parameters qualitatively as well as quantitatively. Additionally, EFI method also allow us to incorporate the prior knowledge, extracted in multimodality imaging cases, to design source/detector configuration sensitive to specific region of interest. The source/detector ranking method was further analyzed to derive the automatic cut off number for iterative scheme.
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