A Method for Narrow Field-Of-View Region-Of-Interest Computed Tomography

Abstract

This paper presents a method for reconstructing of an internal tomographic image for a region-of-interest (RoI) within a section, using narrowed radiation beams. This reduces radiation exposure, and with the aid of an iterative image reconstruction algorithm, RoI images are reconstructed with a quality comparable to conventional computed tomography (CT) full field-of-view images. Region-of-interest image reconstruction is formulated as a discrete problem to avoid the truncated (incomplete) problem associated with the conventional analytic filtered backprojection method. This in turn allows local reconstruction of RoI images without any prior information or constraints. A coarse image of the entire section is first reconstructed with the aid of a modified convex maximum likelihood (MCML) algorithm. The coarse image is then used to account for the effect of the RoI surroundings. With RoI-specific projections, an RoI image is then reconstructed with the MCML method at the desired pixel size. The proposed method is evaluated using an anthropomorphic thorax phantom, with the heart as its RoI, showing an image quality comparable to that of a conventional CT, but with a about 72% reduction in radiation exposure. Unlike existing approaches that require some a prior knowledge of a segment of the image, this approach does not require any prior image information or any constrains on the solution.