This work aims to recognize the patient individual possibility of contrast dose reduction in CT angiography. This system should help to identify whether the dose of contrast agent in CT angiography can be reduced to avoid side effects. In a clinical study, 263 CT angiographies were performed and, in addition, 21 clinical parameters were recorded for each patient before contrast agent administration. The resulting images were labeled according to their contrast quality. It is assumed that the contrast dose could be reduced for CT angiography images with excessive contrast. These data was used to develop a model for predicting excessive contrast based on the clinical parameters using logistic regression, random forest, and gradient boosted trees. In addition, the minimization of clinical parameters required was investigated to reduce the overall effort. Therefore, models were tested with all subsets of clinical parameters and each parameter's importance was examined. In predicting excessive contrast in CT angiography images covering the aortic region, a maximum accuracy of 0.84 was achieved by a random forest with 11 clinical parameters; for the leg-pelvis region data, an accuracy of 0.87 was achieved by a random forest with 7 parameters; and for the entire data set, an accuracy of 0.74 was achieved by gradient boosted trees with 9 parameters.

Original languageEnglish
JournalStudies in Health Technology and Informatics
Pages (from-to)952-956
Number of pages5
Publication statusPublished - 18.05.2023

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


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