Helical Artifact Reduction Method Using Image Segmentation With CNN Denoising Technique

Helical computed tomography (CT) scans are often performed to obtain three-dimensional images of an object that is longer than the detector. However, existing quasi-exact and exact reconstruction methods, such as re-binning Katsevich algorithm, generate interpolation errors or require high computational power. In this work, we propose a method reconstruct helical CT projections by iteratively reducing artifacts. each iteration, convolutional neural network (CNN)-based denoising technique used accurately segment prior image (bone soft tissue image). The results indicate proposed algorithm reduces artifacts significantly greater extent single slice (SSR) weighted filtered backprojection (W-FBP) methods.