While breast density is known as one of the critical risk factors of breast cancer, Digital breast tomosynthesis (DBT)-based diagnostic performance is known to have a strong dependence on breast density. As a potential solution to increase the diagnostic performance of DBT, we are investigating dual-energy DBT imaging techniques. We estimated partial path lengths of an x-ray through water, lipid, and protein from the measured dual-energy projection data and the object thickness information. We reconstructed material-selective DBT images for the material-decomposed projection. The feasibility of the proposed dual-energy DBT scheme has been demonstrated by using physical phantoms.
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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