Devising a novel evaluation method for computed tomography images containing metal artifacts from titanium seed implants: Application to virtual monochromatic imaging energy optimization.

INTRODUCTION

Metal artifact reduction (MAR) technology cannot fully eliminate metal artifacts from metallic devices in computed tomography (CT) images. Hence, it is important to investigate the optimal acquisition parameters and post-processing techniques. This study aimed to devise a novel evaluation method for images containing metal artifacts from titanium seed implants and identify the optimal energy level for virtual monochromatic imaging (VMI) to reduce metal artifacts and enhance signal detectability.

METHODS

Post-brachytherapy CT scans are a clinical example of the effects of metal artifacts. Therefore, we focused on the pelvic region, including the prostate, and created a phantom with simulated radioactive seeds that were inserted into the prostate region. We investigated the relationship between metal artifacts and monochromatic energy levels (35-200 keV at 5 keV intervals) using a dual-energy CT system with deep learning (DL) and MAR algorithms. Metal artifacts were investigated using the Gumbel evaluation method, which quantitatively evaluates artifacts, and contrast detectability was assessed using the contrast-to-noise ratio (CNR) and a newly devised contrast-to-artifact ratio (CAR).

RESULTS

The location parameter, representing the physical index of metal artifacts, was the lowest at 65 keV. CNR and CAR achieved the highest signal detectability at 70 and 65 keV, respectively. VMI at 65 keV provided an optimal balance. When two images with similar CNR values were assessed using CAR, the resulting difference aligned consistently with the visual evaluation findings.

CONCLUSIONS

VMI at 65 keV with DL and MAR reconstructions is the optimal acquisition parameter for reducing metal artifacts and improving signal detectability. Additionally, CAR can be used to evaluate images affected by metal artifacts.

IMPLICATIONS FOR PRACTICE: CAR is useful for evaluating the effect of metal artifacts on signal detection.