Effects of total variation regularization noise reduction algorithm in improved K-edge log-subtraction X-ray images with photon-counting cadmium telluride detectors

X-ray systems with photon-counting cadmium telluride (CdTe) detectors can achieve greatly improved images by using the K-edge log-subtraction (KELS) imaging method. This paper discusses methods for acquiring KELS images with photon-counting CdTe detectors and applying the modeled total variation (TV) regularization noise reduction algorithm to the obtained images. Monte Carlo simulation using the Geant4 Application for Tomographic Emission platform was used to model the system. To demonstrate the usefulness of the proposed TV algorithm, we investigated the normalized noise power spectrum, contrast to noise ratio, and no reference-based assessment parameter using a natural image quality evaluator. The results demonstrate that the proposed TV noise reduction regularization algorithm can better preserve image details than the conventional denoising methods in all the evaluation parameters.