Deep-Learning-Based Feature Encoding of Clinical Parameters for Patient Specific CTA Dose Optimization

Marja Fleitmann, Hristina Uzunova, Andreas Martin Stroth, Jan Gerlach, Alexander Fürschke, Jörg Barkhausen, Arpad Bischof, Heinz Handels

In: Juan Ye, Michael J. O'Grady, Gabriele Civitarese, Kristina Yordanova (Hrsg.). Proceedings of the 10th EAI International Conference on Wireless Mobile Communication and Healthcare. International Conference on Wireless Mobile Communication and Healthcare (MobiHealth-2021) November 13-14 Chongqing/Virtual China Seiten 315-322 ISBN 978-3-030-70569-5 Springer International Publishing 2021.


The use of contrast agents in CT angiography examinations holds a potential health risk for the patient. Despite this, often unintentionally an excessive contrast agent dose is administered. Our goal is to provide a support system for the medical practitioner that advises to adjust an individually adapted dose. We propose a comparison between different means of feature encoding techniques to gain a higher accuracy when recommending the dose adjustment. We apply advanced deep learning approaches and standard methods like principle component analysis to encode high dimensional parameter vectors in a low dimensional feature space. Our experiments showed that features encoded by a regression neural network provided the best results. Especially with a focus on the 90% precision for the ``excessive dose'' class meaning that if our system classified a case as ``excessive dose'' the ground truth is most likely accordingly. With that in mind a recommendation for a lower dose could be administered without the risk of insufficient contrast and therefore a repetition of the CT angiography examination. In conclusion we showed that Deep-Learning-based feature encoding on clinical parameters is advantageous for our aim to prevent excessive contrast agent doses.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence