CT Image Harmonization for Enhancing Radiomics Studies
This addresses a domain-specific issue for medical imaging researchers by improving CT image analysis reproducibility, though it appears incremental as it builds on existing GAN and standardization methods.
The paper tackled the problem of low reproducibility in CT radiomics due to non-standardized imaging protocols by developing RadiomicGAN, which outperformed state-of-the-art models in harmonizing images, as shown by evaluation on 1401 radiomic features.
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.