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• W2140207979 abstract "Diffuse optical tomography (DOT) poses a typical ill-posed inverse problem with limited number of measurements and inherently low spatial resolution. In this paper, we propose a hierarchical Bayesian approach to improve spatial resolution and quantitative accuracy by using a priori information provided by a secondary high resolution anatomical imaging modality, such as magnetic resonance (MR) or X-ray. The proposed hierarchical Bayesian approach allows incorporation of partial a priori knowledge about the noise and unknown optical image models, thereby capturing the function-anatomy correlation effectively. Numerical simulations demonstrate that the proposed method avoids undesirable bias towards anatomical prior information and leads to significantly improved spatial resolution and quantitative accuracy" @default.
• W2140207979 created "2016-06-24" @default.
• W2140207979 creator A5002481140 @default.
• W2140207979 date "2006-03-30" @default.
• W2140207979 modified "2023-09-26" @default.
• W2140207979 title "Hierarchical Bayesian Algorithm for Diffuse Optical Tomography" @default.
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• W2140207979 doi "https://doi.org/10.1109/aipr.2005.30" @default.
• W2140207979 hasPublicationYear "2006" @default.
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