Augmented Likelihood Image Reconstruction

Abstract

The presence of high-density objects remains an open problem in medical CT imaging. Data of projections passing through objects of high density, such as metal implants, are dominated by noise and are highly affected by beam hardening and scatter. Reconstructed images become less diagnostically conclusive because of pronounced artifacts that manifest as dark and bright streaks. A new reconstruction algorithm is proposed with the aim to reduce these artifacts by incorporating information about shape and known attenuation coefficients of a metal implant. Image reconstruction is considered as a variational optimization problem. The afore-mentioned prior knowledge is introduced in terms of equality constraints. An augmented Lagrangian approach is adapted in order to minimize the associated log-likelihood function for transmission CT. During iterations, temporally appearing artifacts are reduced with a bilateral filter and new projection values are calculated, which are used later on for the reconstruction. A detailed evaluation in cooperation with radiologists is performed on software and hardware phantoms, as well as on clinically relevant patient data of subjects with various metal implants. Results show that the proposed reconstruction algorithm is able to outperform contemporary metal artifact reduction methods such as normalized metal artifact reduction.

The effects of metal artifact reduction on the retrieval of attenuation values

“…In order to reconstruct an image without the usage of x rays that run through a metal object, the ALIR reconstruction divides the set of projections indices M  = {1, …,  M } into a set of indices for projections that are not affected by metal, M 1 , and a set for projections that are affected by metal, M 2 , such that M  =  M 1  ∪  M 2 and ${\mathit{M}}_1\cap {\mathit{M}}_2=\mathit{\varphi }$ 15. More often than not the metal object is much smaller compared to patients anatomy shown in the image, therefore |  M 2  | < |  M 1  | .…”

“…The holes can be used to mount different tissue‐equivalent cylindrical rods with known mass density and an electron density relative to water, denoted by ${\mathit{\rho }}_e^W$. A wide range of tissue‐equivalent inserts, from cortical bone with ${\mathrm{italic\rho }}_e^W=1.707$ to lung 300 with ${\mathrm{italic\rho }}_e^W=0.292$ was used in order to investigate the reconstruction ability of the Augmented Likelihood Image Reconstruction algorithm and the linear interpolation approach 11, 15…”

“…For the reduction of metal artifacts, LI and the recently proposed ALIR algorithm is used 11, 15. The ALIR algorithm is based on an iterative scheme and integrates two different ideas in order to reduce streaking artifacts.…”

“…However, since publication of the linear interpolation (LI) approach in 1987, only a few advanced methods with a high clinical potential have been proposed 8, 11, 12, 13, 14. One particular method of interest is the Augmented Likelihood Image Reconstruction (ALIR) that has proven to outperform current methods for clinically relevant data 15. In order to integrate such a method in the daily routine within a clinical environment, the method needs to be evaluated extensively 5, 7, 16, 17.…”

Improvement of dose calculation in radiation therapy due to metal artifact correction using the augmented likelihood image reconstruction

“…As a second approach, the recently proposed ALIR algorithm is used 15. The method is based on an iterative scheme and integrates two different ideas in order to reduce streaking artifacts.…”