Optimization of the field-of-view in a modelbased iterative reconstruction for CT

Abstract

CT imaging typically requires a reconstruction of small region-of-interest (ROI) at high resolution. It is straightforward using analytical algorithms, however iterative techniques require the reconstruction of all sources of attenuation that fall in the path of the X-ray beam. Reconstructing the desired ROI therefore implies reconstructing the whole scanned object in the full field of view (FFOV) with the desired resolution for the ROI at a much higher computational cost. Alternatively, a multi-resolution reconstruction is performed in which the FFOV is first reconstructed on a coarse grid, followed by reconstructing the ROI with a smaller grid at the desired resolution. The FFOV can be fixed to the bore size (700 mm) for all purposes. However, this is inefficient when the object is smaller than the pre-determined FFOV. An algorithm is proposed to compute the diameter of the FFOV based on the object size and content. The approach uses the sinogram data to estimate the diameter of the necessary FFOV for artifact-free reconstruction of the target. The approach significantly improves the computation time and may also improve the convergence of the algorithm. The efficacy of the algorithm is demonstrated using phantom studies.