Joint estimation of tissue types and linear attenuation coefficients for photon counting CT

Medical Physics
Kento NakadaKenji Amaya

Abstract

Newly developed spectral computed tomography (CT) such as photon counting detector CT enables more accurate tissue-type identification through material decomposition technique. Many iterative reconstruction methods, including those developed for spectral CT, however, employ a regularization term whose penalty transition is designed using pixel value of CT image itself. Similarly, the tissue-type identification methods are then applied after reconstruction; thus, it is impossible to take into account probability distribution obtained from projection likelihood. The purpose of this work is to develop comprehensive image reconstruction and tissue-type identification algorithm which improves quality of both reconstructed image and tissue-type map. The authors propose a new framework to jointly perform image reconstruction, material decomposition, and tissue-type identification for photon counting detector CT by applying maximum a posteriori estimation with voxel-based latent variables for the tissue types. The latent variables are treated using a voxel-based coupled Markov random field to describe the continuity and discontinuity of human organs and a set of Gaussian distributions to incorporate the statistical relation between the...Continue Reading

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Citations

Feb 1, 2017·Radiological Physics and Technology·Katsuyuki Taguchi
Mar 9, 2018·Medical Physics·Buxin ChenXiaochuan Pan
Dec 30, 2020·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Buxin ChenXiaochuan Pan
Jan 27, 2021·Physics in Medicine and Biology·Shusen ZhaoXing Zhao
Jul 28, 2021·Physics in Medicine and Biology·Mang FengKe Li

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