Local Correction of Non-Periodic Motion in Computed Tomography

This paper presents a new iterative motion correction technique composed of motion estimation in projection space, motion segmentation in image space, and motion compensation within an analytical filtered-backprojection (FBP) image reconstruction algorithm. The motion is estimated by elastic registration of acquired projections on reference projections. Reference projections are sampled from the image, reconstructed in a previous iteration step. To apply the motion compensation locally, the image regions significantly affected by motion are segmented. First the perceived motion is identified in projection space by computing the absolute difference between acquired line integrals and reference line integrals. Then, differences are reconstructed in image space, and the image is regularized with a pipeline of standard image processing operators. The result of this procedure is a normalized motion map, associating each image element with a measure of the local motion detected there. The estimated displacement vectors in projection space and the reconstructed motion map in image space are then used by an adaptive motion-compensated FBP algorithm to reconstruct a sharper image. Results are shown qualitatively and quantitatively for reconstructions from realistic projections, simulated from clinical patient data. Since the method does not assume any periodicity of the motion model, it can correct reconstruction artifacts due to unstructured patient motion, such as breath-hold failure, abdominal contractions, and nervous movements.