A model-based iterative reconstruction algorithm DIRA using patient-specific tissue classification via DECT for improved quantitative CT in dose planning

Abstract

The simulations indicated that DIRA is capable of determining elemental composition of tissues, which are needed in brachytherapy with low energy (< 50 keV) photons and proton therapy. The algorithm provided quantitative monoenergetic images with beam hardening artifacts removed. Its convergence was fast, image sharpness expressed via the modulation transfer function was maintained, and image noise did not increase with the number of iterations.

Optimal Selection of Base Materials for Accurate Dual-Energy Computed Tomography: Comparison Between the Alvarez–macovski Method and Dira

“…True mass attenuation coefficients, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mu _{\mathrm{m,tab}}$\end{document} , for these materials were either taken directly from the EPDL97 library ( 12 ) or derived from elemental compositions taken from. ( 2 ) Corresponding true linear attenuation coefficients were obtained as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\mu _{\mathrm{tab}} = \rho \mu _{\mathrm{m,tab}}$\end{document} , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\rho $\end{document} is the mass density of the material. The coefficients \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$w_1$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$w_2$\end{document} in equation ( 2 ) are linearly proportional to the density of the decomposed material.…”

“…The Alvarez–Macovski method (AM) ( 1 ) and the Dual-energy Iterative Reconstruction Algorithm (DIRA) ( 2 ) are image reconstruction algorithms in dual-energy computed tomography (DECT) using a mathematical decomposition of the linear attenuation coefficient (LAC) into energy-dependent basis functions. Both algorithms use energy spectra of photons emitted from the x-ray tube, and both algorithms can produce virtual monoenergetic images at any energy.…”

Segmentation of bones in medical dual-energy computed tomography volumes using the 3D U-Net

“…Applications in radiation therapy can benefit from the use of dual-energy CT (DECT) as it can provide more information about the material than the single-energy CT [11][12][13][14]. For instance the model-based image reconstruction (MBIR) algorithm DIRA [15] developed by the authors combines automated segmentation with material decomposition for the estimation of elemental composition and mass densities of tissues. These data can be used to derive electron densities and I-values.…”

“…Advanced approaches based on multi-material decomposition [16] or the use of segmented-tissue-specific material bases [15] have been proposed. Nevertheless, good results can also be achieved with bone tissue and soft tissue as material bases.…”

“…The network should be easily adapted to the segmentation of multi-energy CT data. Such solution can be used for instance in the MBIR algorithm DIRA [15], which focuses on applications in radiotherapy.…”