SU-E-I-08: An Inpaint-Based Interpolation Technique to Recover Blocked Information for Cone Beam CT with a Synchronized Moving Grid (SMOG).

PURPOSE: Synchronized moving grid (SMOG) is a promising technique to reduce scatter and ghost artifacts in cone beam computed tomography (CBCT). However, the grid blocks part of image information in each projection, and multiple projections at the same gantry angle have to been taken to obtain full information. Because of the continuity of a patient's anatomy in the projection, the blocked information may be estimated by interpolation. This study aims to evaluate an inpainting-based interpolation approach to recover the missing information for CBCT reconstruction. METHOD: We used a simple region-based inpainting approach to interpolate the missing information. For a pixel to be interpolated, we divided the nearby regions having image information into 6 sub-regions: up-left, up-middle, up-right, down-left, down-middle, and down-right, each with 9 pixels. The average pixel value of each sub-region was calculated. These average values, along with the pixel location, were used to determine the interpolated pixel value. We compared our approach with the Criminisi Exemplar (CE) and total variation (TV) based inpainting techniques. Projection images of Catphan and a head phantom were used for the comparison. The SMOG was simulated by erasing the information (filling with "0") of the areas in each projection corresponding to the grid. RESULTS: For the Catphan, the processing time was 178, 45 and 0.98 minutes for CE, TV and our approach, respectively. The signal to noise ratio (SNR) was 19.4, 18.5 and 26.4 db, correspondingly. For the head phantom, the processing time was 222, 45 and 0.93 minutes for CE, TV and our approach, respectively. The SNR was 24.6, 20.2 and 26.2db correspondingly. CONCLUSION: We have developed a simple inpainting based interpolation approach, which can recover some of the image information for the SMOG-based CBCT imaging. This study is supported by NIH/NCI grant 1R01CA166948-01.

Duke Scholars

Author Lei Ren Radiation Oncology