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Image Reconstruction of Sinogram Restoration using Inpainting method in Sparse View CT

Sparse view CT에서 inpainting 방법을 이용한 사이노그램 복원의 영상 재구성

  • Kim, Daehong (Department of Radiological Science, Eulji University) ;
  • Baek, Cheol-Ha (Department of Radiological Science, Dongseo University)
  • 김대홍 (을지대학교 방사선학과) ;
  • 백철하 (동서대학교 보건의료계열 방사선학과)
  • Received : 2017.11.28
  • Accepted : 2017.12.31
  • Published : 2017.12.31

Abstract

Sparse view CT has been widely used to reduce radiation dose to patient in radiation therapy. In this work, we performed sinogram restoration from sparse sampling data by using inpainting method for simulation and experiment. Sinogram restoration was performed in accordance with sampling angle and restoration method, and their results were validated with root mean square error (RMSE) and image profiles. Simulation and experiment are designed to fan beam scan for various projection angles. Sparse data in sinogram were restored by using linear interpolation and inpainting method. Then, the restored sinogram was reconstructed with filtered backprojection (FBP) algorithm. The results showed that RMSE and image profiles were depended on the projection angles and restoration method. Based on the simulation and experiment, we found that inpainting method could be improved for sinogram restoration in comparison to linear interpolation method for estimating RMSE and image profiles.

방사선 치료 전 환자 위치 확인을 위해 수행하는 콘빔 CT 촬영에서 환자 선량 감소를 위해 Sparse view CT가 사용되고 있다. 본 연구는 시뮬레이션과 실험을 통해 선형보간법과 inpainting 방법을 이용하여 사이노그램의 sparse 데이터 복원하고 평가하는 것이다. 사이노그램 복원은 여러 간격의 각도로 획득된 영상에 적용되었다. 복원된 사이노그램은 역투영재구성법으로 재구성되었고, 그 결과를 평균제곱근오차와 영상의 프로파일로 나타내었다. 결과에 따르면, 평균제곱근오차와 영상 프로파일은 투영 각도와 복원법에 의존하였다. 시뮬레이션과 실험 결과에서 inpainting 복원법은 선형보간법에 비해 사이노그램의 복원 측면에서 개선된 결과를 보여주었다. 따라서, inpainting 방법은 환자 선량을 감소시키면서 영상화질을 유지시키는데 기여할 수 있을 것이다.

Keywords

References

  1. M. Kalke, S. Siltance, "Sinogram interpolation method for sparse-angle tomography", Applied Mathematics, Vol. 5, pp. 423-441, 2014. https://doi.org/10.4236/am.2014.53043
  2. R. A. Brooks, G. H. Weiss, A. J. Talbert, "A new approach to interpolation in computed, J. Comput. Assisted Tomography", Vol. 2, No. 5, pp. 577-585, 1978. https://doi.org/10.1097/00004728-197811000-00010
  3. E. Y. Sidky, Y. Duchin, X. Pan, C. Ullberq, "A constrained, total-variation minimization algorithm for low-intensity x-ray CT", Medical. Physics. Vol. 38, pp. 117-125, 2011. https://doi.org/10.1118/1.3560887
  4. H. Wang, A Kaestner, Y. Zou, Y. Lu, Z. Guo, "Sparse-view reconstruction of dynamic processes by neutron tomography", Physics Procedia, Vol. 88, pp. 290-298, 2017. https://doi.org/10.1016/j.phpro.2017.06.040
  5. H. Zhang, M. Kruis, JJ. Sonke, "Directional sinogram interpolation for motion weighted 4D cone-beam CT reconstruction", Physics in Medicine & Biology, Vol. 62, No. 6, pp. 2254-2275, 2017. https://doi.org/10.1088/1361-6560/aa5b6e
  6. C. Zhe, B. Parker, D. Feng, "Temporal compression for dynamic positron emission tomography via principal component analysis in the sinogram domain," IEEE nuclear science symposium conference record, 2003.
  7. H. Kostler, M. Prummer, U. Rude, J. Hornegger, "A daptive variational sinogram interpolation of sparsely sampled CT data", The 18thInternational conference on Pattern recognition, 2006.
  8. A. Rosa, "Study and evaluation of text inpainting techniques for images and video", https://fenix.tecnico.ulisboa.pt/downloadFile/1126295043834175/ResumoAdelcioRosa.pdf
  9. M. Bertalmio et al., Image inpainting, 27th Conf. on Computer graphics and interactive techniques. USA, 2000.