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- W2922307277 abstract "The basic mathematical problem in computed tomography (CT) is defined as finding a nonnegative solution to a rectangular linear system with a sparse coefficient vector and matrix. The coefficient vector and matrix respectively correspond to acquired projection data and a projection operator based on a discrete Radon transform. As an approach to find a nonnegative solution corresponding to a tomographic image, a continuous method using simultaneous ordinary differential equations has been proposed. The continuous method can produce high-quality images even from an insufficient number of projection data. However, it requires a huge computational cost to obtain a high-quality image because of numerical integration. In this paper, to reduce the image-reconstruction time, we proposed two iterative methods without numerical integration that enable us to reconstruct high-quality CT images without negative pixels. The methods were produced on the basis of a hierarchical alternating least-squares algorithm that is known as a fast solver for nonnegative matrix factorization and conjugate gradient methods that are effective for a linear system. Through numerical experiments, we also discussed the performances of the proposed methods in terms of both the quality of obtained images and the time required to obtain high-quality images." @default.
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- W2922307277 date "2019-03-15" @default.
- W2922307277 modified "2023-10-16" @default.
- W2922307277 title "Reconstruction of CT Images Using Iterative Least-Squares Methods with Nonnegative Constraint" @default.
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- W2922307277 doi "https://doi.org/10.2299/jsp.23.41" @default.
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