Su, Dingjie; Van Schaik, Katherine D.; Remedios, Lucas W.; Li, Thomas; Maldonado, Fabien; Sandler, Kim L.; Dawant, Benoit M.; Landman, Bennett A. Proceedings – International Symposium on Biomedical Imaging (2025). .听
Contrast-enhanced CT scans听use special dyes (called radiocontrast agents) that highlight blood vessels by making them appear brighter than the surrounding tissue. To get the best images, it’s important to time the scan correctly鈥攔ight when the contrast is at its strongest in the area being examined.听
This study introduces a new method for predicting the听optimal timing of contrast听during a CT scan using a type of machine learning called a听circular regression model. Instead of treating contrast timing as one of a few fixed stages (as many previous methods do), this technique treats timing as a听continuous听value. That allows for more precise predictions and better adjustment to differences between patients鈥攅specially how contrast flows through each person鈥檚 blood vessels.听
The model uses听2D convolutional neural networks听(a kind of AI that processes image data) to learn patterns from earlier time points in a scan and predict the best contrast timing. It was trained on 877 CT scans and tested on 112 new scans, achieving听93.8% accuracy, which is on par with the best current methods. The results show that this new approach, which focuses on prediction rather than classification, performs better than existing 2D and 3D models that try to label scans into fixed categories.听
The study also investigates how the听location of each CT slice in the body听relates to contrast timing, suggesting that this information could help make predictions even more accurate鈥攁 new idea that hasn鈥檛 been explored before.听
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Fig. 1.听听
Four contrast phases used as anchor points in our re-gression model. Organs (e.g. kidneys) are enhanced differently in each phase. The difference between some phases are subtle, e.g., between Nand D, making automatic identification challenging.听
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