We compare the performances of various three-dimensional reconstruction algorithms for situations where only a few conical projections of a vascular tree are available. This problem is ill-posed and prior information must therefore be used to regularize the solution. We restrict ourselves to methods that are able to handle the sparseness and the nonnegativity that characterize an iodinated vascular structure: the Extreme Value Technique and related methods, and the Algebraic Reconstruction Technique. The results we obtained led us to derive a new method based on a two-step detection-estimation scheme.
Keywords: 3D tomographic reconstruction, vascular resconstruction, regularization, a priori information, extreme value technique, algebraic reconstruction technique (ART), nonnegativity constraint
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