Room: Exhibit Hall | Forum 1
Purpose: Scatter correction is crucial to the quality of reconstructed images in x-ray cone-beam computed tomography (CBCT). However, a scatter correction alone does not provide satisfactory image quality. This paper proposes a new method for reducing the noise in CBCT images after scattering correction efficiently with the important diagnosis structures preserved.
Methods: CBCT images were transformed into wavelet domain with dyadic wavelet transform. According to the inter-scale relationship of wavelet coefficient magnitude sum in cone of influence, wavelet coefficients were classified into 2 categories: irregular coefficients, and edge-related and regular coefficients. For edge-related and regular coefficients, only those located at the lowest decomposition level are further processed, while no changes are made on coefficients located at other decomposition levels to preserve the edges; Irregular coefficients are denoised at all levels. Both kinds of coefficient were denoised by a new wiener filter. In this filter, a new noise variation estimating method more suitable for CBCT images was proposed and the subband of different directions is processed by windows with different shapes. We obtained Monte Carlo simulation CBCT images and real CBCT images to verify the validity of the experimental results.
Results: Monte Carlo simulation results on an evaluation phantom were quantitatively measured by peak signal-to-noise ratio (PSNR). The PSNR of 16.9 for a reconstruction image was increased to 25.6 for scatter corrected images and was further increased to 28.2 using the proposed algorithm. Real reconstructed CBCT image results show that our proposed algorithm can dramatically remove CBCT image noise, and the image boundaries are seen more clearly.
Conclusion: This algorithm can reduce noise further in scatter corrected CBCT images effectively with important structure details and provide a new approach for denoising CBCT images.
Funding Support, Disclosures, and Conflict of Interest: This work was funded by the National Natural Science Foundation of China (NO. 61471226), Natural Science Foundation for Distinguished Young Scholars of Shandong Province (NO. JQ201516), and the Taishan scholar project of Shandong Province.