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A New Metal Artifact Reduction Algorithm Using Edge Preserving Smoothing Filter

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J KIM1*, H Nam2, S An1, Y OH1, y Ji1, R Lee1,3, (1) Department of medical science. Ewha womans university, Seoul/KOREA, (2) Department of radiation oncology stanford university, stanford, CA 94305 (3) Department of Radiation Oncology, Ewha womans university medical center,Seoul/KOREA

SU-E-I-62 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose: Metallic materials such as tooth supplement or surgical clip lead to metal artifacts on the X-ray computed tomographic (CT) image. In this study, we have developed a new metal artifact reduction algorithm. To improve the image quality from metal artifacted data, our algorithm uses edge preserving interpolation method with BM3D smoothing algorithm. For evaluation, we used both real human dental data with metal artifacts and metal implemented phantom.

Methods: Real metal artifacted data was obtained at Ewha womans university Mokdong hospital. Metal implemented phantom containing air, water, teflon, metalis made to evaluate the performance of the proposed algorithm. With obtained projection data, metal part is extracted from the back-projection image. Once the metal location and size is obtained, all corrupted non-metal part is recovered using edge preserving wavelet interpolation. With interpolated data, reconstruct image with filtered back-projection. Apply BM3D to smooth the reconstructed image with edge preserving. BM3D is a state-of-art smoothing algorithm, which uses block information to get the similarity of the image. Replace the metal data from the smoothed image and continue doing this forward-and backward projection iteratively. Our algorithm is based on the iterative reconstruction algorithm combined with the wavelet based interpolation with edge preserving smoothing filter.

Results: We compared the proposed algorithm with Filtered back-projection and iterative reconstruction algorithm with bilinear interpolation and Gaussian smoothing filter. For the evaluation, we used visual comparison, and quantitative comparison using student t-test. For the real data, there is no exact solution. Thus only visual comparison can be applied. For the phantom data, we did the quantitative error comparison as well as visual comparison. The proposed algorithm outperformed results in both real and phantom data.

Conclusion: We have developed a new CT reconstruction algorithm for metal artifact reduction and demonstrated its superiority compared with other algorithms.

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