A Multi-Pronged Evaluation For Image Normalization Techniques

While quantitative image features (radiomic) can be employed as informative indicators of disease progression, they are sensitive to variations in acquisition and reconstruction. Prior studies have demonstrated the ability to normalize heterogeneous scans using per-pixel metrics (e.g., mean squared error) and qualitative reader studies. However, the generalizability of these techniques and the impact of normalization on downstream tasks (e.g., classification) have been understudied. We present a multi-pronged evaluation by assessing image normalization techniques using 1) per-pixel image quality and perceptual metrics, 2) variability in radiomic features, and 3) task performance differences using a machine learning (ML) model. We evaluated a previously reported 3D generative adversarial network-based (GAN) approach, investigating its performance on low-dose computed tomography (CT) scans acquired at a different institution with varying dose levels and reconstruction kernels. While the 3D GAN achieved superior metric results, its impact on quantitative image features and downstream task performance did not result in universal improvement. These results suggest a more complicated relationship between CT acquisition and reconstruction parameters and their effect on radiomic features and ML model performance, which are not fully captured using per-pixel metrics alone. Our approach provides a more comprehensive picture of the effect of normalization.