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Computer-Assisted Quality Assessment of Aortic CT Angiographies for Patient-Individual Dose Adjustment.
Fleitmann, Marja; Soika, Kira; Stroth, Andreas Martin; Gerlach, Jan; Fürschke, Alexander; Hunold, Peter; Barkhausen, Jörg; Bischof, Arpad; Handels, Heinz.
Affiliation
  • Fleitmann M; Institute of Medical Informatics, University of Lübeck.
  • Soika K; Institute of Medical Informatics, University of Lübeck.
  • Stroth AM; Department of Radiology and Nuclear Medicine, UKSH Lübeck.
  • Gerlach J; Department of Radiology and Nuclear Medicine, UKSH Lübeck.
  • Fürschke A; Department of Radiology and Nuclear Medicine, UKSH Lübeck.
  • Hunold P; Department of Radiology and Nuclear Medicine, UKSH Lübeck.
  • Barkhausen J; Department of Radiology and Nuclear Medicine, UKSH Lübeck.
  • Bischof A; Department of Radiology and Nuclear Medicine, UKSH Lübeck.
  • Handels H; IMAGE Information Systems Europe, Rostock.
Stud Health Technol Inform ; 270: 123-127, 2020 Jun 16.
Article in En | MEDLINE | ID: mdl-32570359
Iodine-containing contrast agents (CA) are important for enhanced image contrast in CT imaging especially in CT angiography (CTA). CA however poses a risk to the patient since it can e.g. harm the kidneys. In clinical routine often a standard dose is applied that does not take differences between individual patients into account. We propose a method that as a preliminary stage determines excessive image contrast and CA overdosing by assessing the image contrast in CTA images obtained with the ulrich medical CT motion contrast media injector with RIS/PACS interface. A resulting CA dose recommendation is linked to a set of clinical parameters collected for each assessed patient. We used the established data set to implement an automatic classification for individual CA dose adjustment. The classification determines similar cases of new patients to take on the associated CA dose adjustment recommendation. The computation of similar patient data is based on the previously collected patient-individual parameters. The study shows that as basis for a recommendations the largest proportion of patients receive too much CA. A first evaluation of the automatic classification showed an overall error rate of 22% to recognize the correct class for CA dose adjustments using a k-NN-Classifier and a leave-one-out method. The classification's positive predictive value for correctly assigning a CA overdosing was 85.71%.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Computed Tomography Angiography Type of study: Guideline / Prognostic_studies Limits: Humans Language: En Journal: Stud Health Technol Inform Journal subject: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Year: 2020 Document type: Article Country of publication: Netherlands

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Computed Tomography Angiography Type of study: Guideline / Prognostic_studies Limits: Humans Language: En Journal: Stud Health Technol Inform Journal subject: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Year: 2020 Document type: Article Country of publication: Netherlands