바로가기  >

Abstract
PURPOSE:
Polychromatic x-rays are used in most computed tomography scanners. In this case, a beam-hardening effect occurs, which degrades the image quality and distorts the shapes of objects in the reconstructed images. When the beam-hardening artifact is not severe, conventional correction methods can reduce the artifact reasonably well. However, highly dense materials, such as iron and titanium, can produce more severe beam-hardening artifacts, which often cannot be corrected by conventional methods. Moreover, when the size of the metal is large, severe darks bands due to photon starvation as well as beam-hardening are generated. The purpose of our study was to develop a new method for correcting severe beam-hardening artifacts and severe dark bands using a high-order polynomial correction function and a prior-image-based linearization method.

METHODS:
The initial estimate of an image free of beam-hardening (a prior image) was constructed from the initial reconstruction of the original projection data. Its corresponding beam-hardening-free projection data (a prior projection) were calculated by a projection operator onto the prior image. A new beam-hardening correction function G(praw ) with many high-order terms was effectively determined via a simple minimization process applied to the difference between the original projection data and the prior projection data. Using the determined correction function G(praw ), a corrected linearized sinogram pcorr can be obtained, which became effectively linear for the line integrals of the object. Final beam-hardening corrected images can be reconstructed from the linearized sinogram. The proposed method was evaluated in both simulation and real experimental studies.

RESULTS:
All investigated cases in both simulations and real experiments showed that the proposed method effectively removed not only streaks for moderate beam-hardening artifacts but also dark bands for severe beam-hardening artifacts without causing structural and contrast distortion.

CONCLUSIONS:
The prior-image-based linearization method exhibited better correction performance than conventional methods. Because the proposed method did not require time-consuming iterative reconstruction processes to obtain the optimal correction function, it can expedite the correction procedure and incorporate more high-order terms in the linearization correction function in comparison to the conventional methods.

Author information

Oh D1, Kim S1, Park D1, Choi S2, Song H2, Choi Y2, Moon S2, Baek J2, Hwang D1.
1
School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
2
School of Integrated Technology, Yonsei Institute of Convergence Technology, Yonsei University, 85 Songdo-gwahak-ro, Yeonsu-gu, Incheon, 21983, South Korea