Investigating the Propagation of CT Acquisition Artifacts along the Medical Imaging Pipeline

We propose a framework to support the simulation, exploration, and analysis of uncertainty propagation in 
the medical imaging pipeline—exemplified with artifacts arising during CT acquisition. Uncertainty in the 
acquired data can affect multiple subsequent stages of the medical imaging pipeline, as artifacts propagate 
and accumulate along the latter, influencing the diagnostic power of CT and potentially introducing biases in 
eventual decision-making processes. We designed and developed an interactive visual analytics framework that 
simulates real-world CT artifacts using mathematical models, and empowers users to manipulate parameters 
and observe their effects on segmentation outcomes. By extracting radiomics features from artifact-affected 
segmented images and analyzing them using dimensionality reduction, we uncover distinct patterns related to 
individual artifacts or combinations thereof. We demonstrate our proposed framework on use cases simulating 
the effects of individual and combined artifacts on segmentation outcomes. Our application supports the 
effective and flexible exploration and analysis of the impact of uncertainties on the outcomes of the medical 
imaging pipeline. Initial insights into the nature and patterns of the simulated artifacts could also be derived.