Multi-scale iterative reconstruction

Abstract

Zoomed ROI reconstruction is a natural challenge for iterative reconstruction (IR), since the reprojection step requires knowledge of the entire object attenuating the x-ray beam. In contrast, analytic FBP can reconstruct images within the zoomed ROI without the need of the full FOV reconstruction, provided projection data is not truncated. To overcome this challenge in IR, the following approaches have been suggested: 1) Remove the part of the projection data corresponding to the outside-of-ROI part of the image volume. 2) Use two volume grids, one for full FOV and one for desired ROI. In the case when the size of desired ROI becomes very small, resolution of the fine grid becomes dominated by the coarse grid, and small features cannot be resolved. Our approach is inspired by multi-resolution framework. Image reconstruction is done in several steps. At each step we reduce the size of reconstruction FOV, for example by half, thus refining the scale, since matrix size remains constant. The proposed approach provides the following advantages compared to the standard 2-grid approach 1) Spatial resolution is preserved in zoomed ROI reconstructions. 2) Computational burden and computer memory requirement are reduced. 3) Parameter optimization is simplified, since only one volume is reconstructed at each iteration.