Non-homogeneous ICD optimization for targeted reconstruction of volumetric CT

Medical imaging typically requires the reconstruction of a limited region of interest (ROI) to obtain a high resolution image of the anatomy of interest. Although targeted reconstruction is straightforward for analytical reconstruction methods, it is more complicated for statistical iterative techniques, which must reconstruct all objects in the field of view (FOV) to account for all sources of attenuation along the ray paths from x-ray source to detector. A brute force approach would require the reconstruction of the full field of view in high-resolution, but with prohibitive computational cost. In this paper, we propose a multi-resolution approach to accelerate targeted iterative reconstruction using the non-homogeneous ICD (NH-ICD) algorithm. NH-ICD aims at speeding up convergence of the coordinate descent algorithm by selecting preferentially those voxels most in need of updating. To further optimize ROI reconstruction, we use a multi-resolution approach which combines three separate improvements. First, we introduce the modified weighted NH-ICD algorithm, which weights the pixel selection criteria according to the position of the voxel relative to the ROI to speed up convergence within the ROI. Second, we propose a simple correction to the error sinogram to correct for inconsistencies between resolutions when the forward model is not scale invariant. Finally, we leverage the flexibility of the ICD algorithm to add selected edge pixels outside the ROI to the ROI reconstruction in order to minimize transition artifacts at the ROI boundary. Experiments on clinical data illustrate how each component of the method improves convergence speed and image quality.