Kento Nakada1, Katsuyuki Taguchi2, George S K Fung2, Kenji Amaya1. 1. Department of Mechanical and Environmental Informatics, Tokyo Institute of Technology School of Information Science and Engineering, Meguro 152-8550, Japan. 2. Division of Medical Imaging Physics, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287.
Abstract
PURPOSE: 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. METHODS: 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 tissue types and their attenuation characteristics. The performance of the proposed method is quantitatively compared to that of filtered backprojection and a quadratic penalized likelihood method by 100 noise realization. RESULTS: Results showed a superior trade-off between image noise and resolution to current reconstruction methods. The standard deviation (SD) and bias of reconstructed image were improved from quadratic penalized likelihood method: bias, -0.9 vs -0.1 Hounsfield unit (HU); SD, 46.8 vs 27.4 HU. The accuracy of tissue-type identification was also improved from quadratic penalized likelihood method: 80.1% vs 86.9%. CONCLUSIONS: The proposed method makes it possible not only to identify tissue types more accurately but also to reconstruct CT images with decreased noise and enhanced sharpness owing to the information about the tissue types.
PURPOSE: 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. METHODS: 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 tissue types and their attenuation characteristics. The performance of the proposed method is quantitatively compared to that of filtered backprojection and a quadratic penalized likelihood method by 100 noise realization. RESULTS: Results showed a superior trade-off between image noise and resolution to current reconstruction methods. The standard deviation (SD) and bias of reconstructed image were improved from quadratic penalized likelihood method: bias, -0.9 vs -0.1 Hounsfield unit (HU); SD, 46.8 vs 27.4 HU. The accuracy of tissue-type identification was also improved from quadratic penalized likelihood method: 80.1% vs 86.9%. CONCLUSIONS: The proposed method makes it possible not only to identify tissue types more accurately but also to reconstruct CT images with decreased noise and enhanced sharpness owing to the information about the tissue types.
Authors: Katsuyuki Taguchi; Thomas J Sauer; W Paul Segars; Eric C Frey; Jingyan Xu; Eleni Liapi; J Webster Stayman; Kelvin Hong; Ferdinand K Hui; Mathias Unberath; Yong Du Journal: Med Phys Date: 2020-10-22 Impact factor: 4.071