CT Contrast Phase Identification by Predicting the Temporal Angle Using Circular Regression

• Published in: Proceedings (International Symposium on Biomedical Imaging) Vol. 2025; pp. 1 - 5
• Main Authors: Su, Dingjie, Van Schaik, Katherine D., Remedios, Lucas W., Li, Thomas, Maldonado, Fabien, Sandler, Kim L., Dawant, Benoit M., Landman, Bennett A.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.04.2025
• Subjects: Accuracy; Biological system modeling; Biomedical imaging; body part regression; Computational modeling; Computed tomography; contrast enhancement; deep regression model; Portals; Predictive models; Solid modeling; Timing; Visualization; computed tomography; deep regression model; body part regression; contrast enhancement
• ISSN: 1945-7928; 1945-8452
• DOI: 10.1109/ISBI60581.2025.10980877

Contrast enhancement is widely used in computed tomogra-phy (CT) scans, where radiocontrast agents circulate through the bloodstream and accumulate in the vasculature, creating visual contrast between blood vessels and surrounding tissues. This work introduces a technique to predict the timing of contrast in a CT scan, a key factor influencing the contrast effect, using circular regression models. Specifically, we represent the contrast timing as unit vectors on a circle and employ 2D convolutional neural networks to predict it based on predefined anchor time points. Unlike previous methods that treat contrast timing as discrete phases, our approach is the first method that views it as a continuous variable, of-fering a more fine-grained understanding of contrast differences, particularly in relation to patient-specific vascular effects. We train the model on 877 CT scans and test it on 112 scans from different subjects, achieving a classification accuracy of 93.8%, which is similar to state-of-the-art results re-ported in the literature. We compare our method to other 2D and 3D classification-based approaches, demonstrating that our regression model have overall better performance than the classification models. Additionally, we explore the relation-ship between contrast timing and the anatomical positions of CT slices, aiming to leverage positional information to improve the prediction accuracy, which is a promising direction that has not been studied.