Iterative Reconstruction for Quantitative Tissue Decomposition in Dual-Energy CT
Abstract
Quantitative tissue classification using dual-energy CT has the potential to improve accuracy in radiation therapy dose planning as it provides more information about material composition of scanned objects than the currently used methods based on single-energy CT. One problem that hinders successful application of both single- and dual-energy CT is the presence of beam hardening and scatter artifacts in reconstructed data. Current pre- and post-correction methods used for image reconstruction often bias CT numbers and thus limit their applicability for quantitative tissue classification.
Here we demonstrate simulation studies with a novel iterative algorithm that decomposes every soft tissue voxel into three base materials: water, protein and adipose. The results demonstrate that beam hardening artifacts can effectively be removed and accurate estimation of mass fractions of all base materials can be achieved.
In the future, the algorithm may be developed further to include segmentation of soft and bone tissue and subsequent bone decomposition, extension from 2-D to 3-D and scatter correction.
Keywords
Iterative reconstruction Dual energy CT Tissue classification Tissue composition Tissue decompositionReferences
- 1.Kak, A.C., Slaney, M.: Principles of Computerized Tomographic Imaging. IEEE Press, Los Alamitos (1988)zbMATHGoogle Scholar
- 2.Stierstorfer, K., Rauscher, A., Boese, J., Bruder, H., Schaller, S., Flohr, T.: Weighted FBP - a simple approximate 3D FBP algorithm for multislice spiral CT with good dose usage for arbitrary pitch. Phys. Med. Biol. 49, 2209–2218 (2004)CrossRefGoogle Scholar
- 3.Persson, A., Jackowski, C., Engström, E., Zachrisson, H.: Advances of dual source, dual-energy imaging in postmortem CT. Eur. J. Radiol. 68, 446–455 (2008)CrossRefGoogle Scholar
- 4.Schneider, W., Bortfeld, T., Schlegel, W.: Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions. Phys. Med. Biol. 45, 459–478 (2000)CrossRefGoogle Scholar
- 5.Bazalova, M., Beaulieu, L., Palefsky, S., Verhaegen, F.: Tissue segmentation in Monte Carlo treatment planning: a simulation study using dual-energy CT images. Radiother Oncol. 86, 93–98 (2007)CrossRefGoogle Scholar
- 6.Liu, X., Yu, L., Primak, A.N., McCollough, C.H.: Quantitative imaging of element composition and mass fraction using dual-energy CT: Three-material decomposition. Med. Phys. 36(5), 1602–1609 (2009)CrossRefGoogle Scholar
- 7.Yu, L., Liu, X., McCollough, C.H.: Pre-reconstruction three-material decomposition in dual-energy CT. In: Proc. of SPIE, vol. 7258, pp. 72583V–1 (2009)Google Scholar
- 8.International Commission on Radiation Units and Measurements: Tissue Substitutes in Radiation Dosimetry and Measurement, ICRU Report No. 44 (1989)Google Scholar
- 9.De Man, B., Nuyts, J., Dupont, P., Marchal, G., Suetens, P.: An Iterative Maximum-Likelihood Polychromatic Algorithm for CT. IEEE Trans. on Med. Imaging 20(10) (2001)Google Scholar
- 10.Seger, O., Magnusson Seger, M.: The MATLAB/C program take - a program for simulation of X-ray projections from 3D volume data. Demonstration of beam-hardening artifacts in subsequent CT reconstruction, Technical Report LiTH-ISY-R-2682 (2005), http://liu.diva-portal.org
- 11.Joseph, P.: An Improved Algorithm for reprojectiong Rays Through Pixel Images. IEEE Trans. on Med. Imaging 1(3), 1992–1996 (1982)CrossRefGoogle Scholar
- 12.National Institute of Standards and Technology, http://physics.nist.gov/PhysRefData/XrayMassCoef/cover.html