Optimization-Based 3d Variable Resolution Image Reconstruction In Cone-Beam Ct

In cone-beam computed tomography (CBCT) applications, one may be interested in detailed information only within a region of interest (ROI), while rough knowledge outside the ROI may be sufficient. Therefore, it is of practical value to develop algorithms that are capable of reconstructing an image with variable resolution: an image consisting of high-resolution voxels within the ROI and low-resolution voxels outside the ROI. In this work, we investigate optimization-based algorithms for 3D CBCT image reconstruction with variable resolution, and apply them to patient data collected with a dental CBCT scanner. The results of our study demonstrate that optimization-based algorithms can be developed for yielding an image with different levels of spatial resolution. The work may have implications for the reduction of computation memory and acceleration of computational speed. In the presence of data truncation, it may also provide an approach to obtaining an ROI image of high resolution with reduced truncation artifacts.