While remarkable advances have been made in Computed Tomography (CT), capturing CT images with non-standardized protocols causes low reproducibility regarding radiomic features, forming a barrier on CT image analysis in a large scale. RadiomicGAN is developed to effectively mitigate the discrepancy caused by using non-standard reconstruction kernels. RadiomicGAN consists of hybrid neural blocks including both pre-trained and trainable layers adopted to learn radiomic feature distributions efficiently. A novel training approach, called Dynamic Window-based Training, has been developed to smoothly transform the pre-trained model to the medical imaging domain. Model performance evaluated using 1401 radiomic features show that RadiomicGAN clearly outperforms the state-of-art image standardization models.
@article{arxiv.2107.01337,
title = {CT Image Harmonization for Enhancing Radiomics Studies},
author = {Md Selim and Jie Zhang and Baowei Fei and Guo-Qiang Zhang and Jin Chen},
journal= {arXiv preprint arXiv:2107.01337},
year = {2021}
}