Insufficient CT Data Reconstruction Based on MLEM- DTV Method

摘要

Insufficient data tomography is an efficient technique which saves time as well as minimizes cost. However, due to few angular data it implies the image reconstruction problem as ill-posed. In the ill posed problem, even with exact data constraints, the inversion cannot be uniquely performed. Therefore, selection of suitable method to optimize the reconstruction problems plays an important role in insufficient data CT. Use of regularization function is a well-known method to control the artifacts in limited angle data acquisition. In this work, we propose directional total variation (DTV) regularized ordered subset (OS) type CT reconstruction method using limited projections. Total variation (TV) regularization works as edge preserving regularization which not only preserves the sharp edge but also reduces many of the artifacts that are very common in limited data CT. However TV itself is not direction dependent. Therefore, TV is not very suitable for images with a dominant direction. The images with dominant direction, it is important to know the total variation at certain direction. Hence, here a directional TV (DTV) is used as prior term. Along with this regularized function (DTV) the maximum likelihood expectation maximization (MLEM) function is adapted as objective function. To optimize this objective function the OS type algorithm is used. This work proposes OS type directional TV regularized likelihood reconstruction method which yields fast convergence as well as good quality image. Initial iteration starts with the filtered back projection (FBP) reconstructed image. The quality of the image is assessed by showing the line profile of the reconstructed image. The proposed method is compared with the commonly used FBP, MLEM, and MLEM-TV algorithm. In order to verify the performance of the proposed algorithm a Shepp-Logan head phantom is used to demonstrate the feasibility of the algorithm for the applicability of insufficient CT data reconstruction.