| 摘要 |
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.
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