Abstract

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.
Original languageGerman
Title of host publicationLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Number of pages8
PublisherSpringer Science and Business Media Deutschland GmbH
Publication date2021
Pages315-322
ISBN (Print)9783030705688
DOIs
Publication statusPublished - 2021

Research Areas and Centers

  • Academic Focus: Biomedical Engineering
  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

  • 205-30 Radiology, Nuclear Medicine, Radiation Therapy and Radiobiology
  • 205-07 Medical Informatics and Medical Bioinformatics
  • 205-32 Medical Physics, Biomedical Engineering
  • 409-05 Interactive and Intelligent Systems, Image and Language Processing, Computer Graphics and Visualisation

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