Abstract: We present an iterative method for reducing artifacts in computed tomography (CT) images. First, a filter is applied to the experimental projection data that adaptively expands the detector element size in regions with low photon counts, until the desired number of photons are detected. The initial image is then calculated using an existing reconstruction technique. In each iteration, artifacts and noise in the current image are reduced by using an edge-preserving blur filter. Metal pixels (determined from the initial image) are replaced with smaller values. The resulting image is forward projected. Rays that go through metal are replaced with the forward projected values. Rays that do not pass near metal are kept at the experimental values. Filtered backprojection is then performed on the new projection data to determine the updated image. Finally, after the last iteration, metal pixels are copied from the initial image.

Abstract: We present an iterative method for reducing artifacts in computed tomography (CT) images. First, a filter is applied to the experimental projection data that adaptively expands the detector element size in regions with low photon counts, until the desired number of photons are detected. The initial image is then calculated using an existing reconstruction technique. In each iteration, artifacts and noise in the current image are reduced by using an edge-preserving blur filter. Metal pixels (determined from the initial image) are replaced with smaller values. The resulting image is forward projected. Rays that go through metal are replaced with the forward projected values. Rays that do not pass near metal are kept at the experimental values. Filtered backprojection is then performed on the new projection data to determine the updated image. Finally, after the last iteration, metal pixels are copied from the initial image.

Abstract: We present an iterative method for reducing artifacts in computed tomography (CT) images. In each iteration, constraints such as non-negativity are applied, then the image is blurred to guide convergence to a smoother image. Next, the image is modified using an algebraic reconstruction algorithm to try to match the projection data to within the experimental error. A mask is calculated which specifies which parts of the image to update during each iteration. The mask allows us to first solve regions of the image that are determined by rays with low photon counts (and thus high error). Then, regions of the image determined by rays with higher photon counts (and thus lower error), are solved using those ray sums. Reducing CT scan artifacts results in clearer and higher resolution images, faster scan times, and less radiation use.