Usefulness of copper filters in digital chest radiography based on the relationship between effective detective quantum efficiency and deep learning-based segmentation accuracy of the tumor area.

Abstract

This study aimed to determine the optimal radiographic conditions for detecting lesions on digital chest radiographs using an indirect conversion flat-panel detector with a copper (Cu) filter. First, we calculated the effective detective quantum efficiency (DQE) by considering clinical conditions to evaluate the image quality. We then measured the segmentation accuracy using a U-net convolutional network to verify the effectiveness of the Cu filter. We obtained images of simulated lung tumors using 10-mm acrylic spheres positioned at the right lung apex and left middle lung of an adult chest phantom. The Dice coefficient was calculated as the similarity between the output and learning images to evaluate the accuracy of tumor area segmentation using U-net. Our results showed that effective DQE was higher in the following order up to the spatial frequency of 2 cycles/mm: 120kV + no Cu, 120kV + Cu 0.1mm, and 120kV + Cu 0.2mm. The segmented region was similar to the true region for mass-area extraction in the left middle lobe. The lesion segmentation in the upper right lobe with 120kV + no Cu and 120kV + Cu 0.1mm was less successful. However, adding a Cu filter yielded reproducible images with high Dice coefficients, regardless of the tumor location. We confirmed that adding a Cu filter decreases the X-ray absorption efficiency while improving the signal-to-noise ratio (SNR). Furthermore, artificial intelligence accurately segments low-contrast lesions.