Affiliations: [a] Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China
| [b]
Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China
Correspondence:
[*]
Correspondence to: Hui Tang, Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, 210096, Nanjing, China. E-mail: corinna@seu.edu.cn.
Abstract: OBJECTIVE:To reduce secondary artifactes generated by the current interpolation-based metal artifact reduction (MAR) methods, this study proposes and tests a new Poisson fusion sinogram based metal artifact reduction (FS-MAR) method. METHODS:The proposed FS-MAR method consists of (1) generating the prior image, (2) forward projecting this prior image and applying the Poisson blending technique to seamlessly replace the metal-affected sinogram of the original projection in the metal projection region (MPR) by the prior image projection to get the corrected metal-free sinogram, and (3) performing the filtered back projection (FBP) on the corrected sinogram and filling the metal image back to the metal-free corrected image to get the final artifact reduced image. Simulated images are calculated by taking clinical metal-free CT images as phantoms and inserting metals during the simulated projection process to get the corresponding metal-affected images by the FBP. After the simulated images are processed by the proposed MAR method, two metrics structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) are used to evaluate image quality. Finally, visual evaluation is also performed using several real clinical metal-affected images obtained from the Revision Radiology group. RESULTS:In two testing samples, using FS-MAR method yields the highest SSIM and PSNR of 0.8912 and 30.6693, respectively. Visual evaluation results on both simulated and clinical images also show that using FS-MAR method generates less image artifacts than using the interpolation-based algorithm. CONCLUSIONS:This study demonstrated that with the same prior image, applying the proposed Poisson FS-MAR method can achieve the higher image quality than using the interpolation-based algorithm.
Keywords: Computed tomography, metal artifact reduction, image fusion, Poisson blending
