Quantitative evaluation of a deep learning-based noise reduction algorithm in digital radiography using noise power spectrum analysis.

The dose-dependent performance of the DL-based algorithm suggests sensitivity to the characteristics of the training data used to develop the DL model. The findings demonstrate distinct differences in the noise suppression behavior between DL-based and conventional methods in DR and underscore the importance of detailed frequency-domain evaluation for understanding advanced image processing. Further research is warranted to integrate noise analysis with diagnostic performance metrics to comprehensively assess clinical utility.