title: Penalized-likelihood region-of-interest CT reconstruction by local object supersampling.
abstract: We present an iterative 2D tomographic reconstruction procedure for a 2D region of interest (ROI), in which high resolution is required. This method is based on an irregular sampling of the image, the ROI being defined on a fine grid while the rest of the image--the "background"--is sampled on a much coarser grid. The background and the ROI are reconstructed simultaneously from the full set of acquired line integrals. This approach significantly reduces the computational cost of projection and backprojection operations. We also show that this procedure yields images of quality equivalent to full high-resolution reconstruction within the ROI, with dramatic runtime savings.
authors.affiliation: Institut de Génie Biomédical, Ecole Polytechnique de Montréal, Canada.
authors.forename: Benoit
authors.initials: B
authors.lastname: Hamelin
authors.forename: Yves
authors.initials: Y
authors.lastname: Goussard
authors.forename: Jean-Pierre
authors.initials: JP
authors.lastname: Dussault
mesh_terms: D007091:Image Processing, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed