Metal artifact correction for x-ray computed tomography using kV and selective MV imaging

Abstract

Purpose: The overall goal of this work is to improve the computed tomography (CT) image quality for patients with metal implants or fillings by completing the missing kilovoltage (kV) projection data with selectively acquired megavoltage (MV) data that do not suffer from photon starvation. When both of these imaging systems, which are available on current radiotherapy devices, are used, metal streak artifacts are avoided, and the soft-tissue contrast is restored, even for regions in which the kV data cannot contribute any information.

Methods: Three image-reconstruction methods, including two filtered back-projection (FBP)-based analytic methods and one iterative method, for combining kV and MV projection data from the two on-board imaging systems of a radiotherapy device are presented in this work. The analytic reconstruction methods modify the MV data based on the information in the projection or image domains and then patch the data onto the kV projections for a FBP reconstruction. In the iterative reconstruction, the authors used dual-energy (DE) penalized weighted least-squares (PWLS) methods to simultaneously combine the kV/MV data and perform the reconstruction.

Results: The authors compared kV/MV reconstructions to kV-only reconstructions using a dental phantom with fillings and a hip-implant numerical phantom. Simulation results indicated that dual-energy sinogram patch FBP and the modified dual-energy PWLS method can successfully suppress metal streak artifacts and restore information lost due to photon starvation in the kV projections. The root-mean-square errors of soft-tissue patterns obtained using combined kV/MV data are 10-15 Hounsfield units smaller than those of the kV-only images, and the structural similarity index measure also indicates a 5%-10% improvement in the image quality. The added dose from the MV scan is much less than the dose from the kV scan if a high efficiency MV detector is assumed.

Conclusions: The authors have shown that it is possible to improve the image quality of kV CTs for patients with metal implants or fillings by completing the missing kV projection data with selectively acquired MV data that do not suffer from photon starvation. Numerical simulations demonstrated that dual-energy sinogram patch FBP and a modified kV/MV PWLS method can successfully suppress metal streak artifacts and restore information lost due to photon starvation in kV projections. Combined kV/MV images may permit the improved delineation of structures of interest in CT images for patients with metal implants or fillings.

FIG. 1.

Collimated MV data acquisition.

FIG. 2.

Schematic of filtered back-projection (FBP)-based kV/MV image-reconstruction methods. The analytic reconstruction methods modify the MV data based on the information contained in the projection or image domains, which are then patched onto the kV projections for the FBP reconstruction. (a) The linear sinogram patch method uses the interpolated ratios and differences between the kV and MV sinograms from neighboring pixels. (b) The DE patch method uses pixel differences in the forward projections from estimated images at kV and MV energies.

FIG. 3.

(a) and (b) show the segmented numerical phantoms with added metal objects (indicated by circles) used in the simulations. For the purpose of visualization only, the pixel values in the (a) and (b) are integers, where 1 = fat, 2 = muscle, 3 = bone, 4 = gold or titanium. The two regions of interest (ROIs) for soft-tissue patterns of each phantom are outlined by the rectangles. A small ROI (in small rectangle) away from them metal object(s) is used for compute standard deviation. (c) and (d) show the FBP reconstructed images with linear interpolation correction (display window level [−400, 800] HU).

FIG. 4.

Normalized x-ray spectra and material attenuation coefficients used in the simulations. (a) 120-kVp kV spectrum and (b) 2.5-MVp MV spectrum for hip phantom; (c) 6-MVp spectrum for dental phantom; (d) energy-dependent attenuation coefficients of soft tissue, bone, titanium, amalgam, and gold.

FIG. 5.

Results for the dental test case. The “Low” and “High” denote the low dose and high dose cases of the simulated MV data. The images are displayed at the window level [ − 400, 800] HU, and ROI images are displayed at the window level [ − 200, 200] HU.

FIG. 6.

Results for the hip test case. The Low and High denote the low dose and high dose cases of the simulated MV data. The images are displayed at the window level [ − 400, 800] HU, and ROI images are displayed at the window level [ − 200, 200] HU.

FIG. 7.

Quantitative evaluations of ROIs for various reconstruction methods using low and high dose MV data.

FIG. 8.

The error images of the MAR, DE Patch, and DE PWLS methods for the dental and hip phantoms using the high MV dose data. The images are displayed at the window level [-500, 500] HU. In the dental case, there are errors in the teeth and bones that are caused by the partial-volume effect.

FIG. 9.

SSIM measurements of the two soft-tissue ROIs in the dental (left) and hip (right) phantoms obtained using the PWLS method for different values of β and γ in Eq. (22). The high-dose data were used in this simulation.

FIG. 10.

The kV and MV dose-distribution maps generated using Geant4 Monte Carlo simulations.