Algorithmic scatter correction based on physical model and statistical iterative reconstruction for dual energy cone beam CT

Shaojie Chang; Ti Bai; Xi Chen; Xuanqin Mou

doi:10.1117/12.2293470

9 March 2018 Algorithmic scatter correction based on physical model and statistical iterative reconstruction for dual energy cone beam CT

Shaojie Chang, Ti Bai, Xi Chen, Xuanqin Mou

Author Affiliations +

Proceedings Volume 10573, Medical Imaging 2018: Physics of Medical Imaging; 105734P (2018) https://doi.org/10.1117/12.2293470
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Dual energy cone beam computed tomography (DE-CBCT) can provide more accurate material characterization than conventional CT by taking advantages of two sets of projections with high and low energies. X-ray scatter leads to erroneous values of the DE-CBCT reconstructed images. Moreover, the reconstructed image of DECT is extremely sensitive to noise. Iterative reconstruction methods using regularization are capable to suppress the noise effects and hence improve the image quality. In this paper, we develop an algorithmic scatter correction based on physical model and statistical iterative reconstruction for DE-CBCT. With the assumption that the attenuation coefficients of the soft tissues are relatively stable and uniform and the scatter component is dominated by low frequency signal, scatter components were calculated while updating the reconstructed images in each iteration. Finally, the CBCT image was reconstructed by scatter corrected projections using statistical iterative reconstruction algorithm. Experiment shows that the proposed method can effectively remove the artifacts caused by x-ray scatter. The CT value accuracy in the reconstructed images has been improved.

Citation Download Citation

Shaojie Chang, Ti Bai, Xi Chen, and Xuanqin Mou "Algorithmic scatter correction based on physical model and statistical iterative reconstruction for dual energy cone beam CT", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105734P (9 March 2018); https://doi.org/10.1117/12.2293470

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