title: Cloud-Based CT Dose Monitoring using the DICOM-Structured Report: Fully Automated Analysis in Regard to National Diagnostic Reference Levels.
abstract: OBJECTIVE
  To implement automated CT dose data monitoring using the DICOM-Structured Report (DICOM-SR) in order to monitor dose-related CT data in regard to national diagnostic reference levels (DRLs).
  
  
  METHODS
  We used a novel in-house co-developed software tool based on the DICOM-SR to automatically monitor dose-related data from CT examinations. The DICOM-SR for each CT examination performed between 09/2011 and 03/2015 was automatically anonymized and sent from the CT scanners to a cloud server. Data was automatically analyzed in accordance with body region, patient age and corresponding DRL for volumetric computed tomography dose index (CTDIvol) and dose length product (DLP).
  
  
  RESULTS
  Data of 36,523 examinations (131,527 scan series) performed on three different CT scanners and one PET/CT were analyzed. The overall mean CTDIvol and DLP were 51.3% and 52.8% of the national DRLs, respectively. CTDIvol and DLP reached 43.8% and 43.1% for abdominal CT (n=10,590), 66.6% and 69.6% for cranial CT (n=16,098) and 37.8% and 44.0% for chest CT (n=10,387) of the compared national DRLs, respectively. Overall, the CTDIvol exceeded national DRLs in 1.9% of the examinations, while the DLP exceeded national DRLs in 2.9% of the examinations. Between different CT protocols of the same body region, radiation exposure varied up to 50% of the DRLs.
  
  
  CONCLUSIONS
  The implemented cloud-based CT dose monitoring based on the DICOM-SR enables automated benchmarking in regard to national DRLs. Overall the local dose exposure from CT reached approximately 50% of these DRLs indicating that DRL actualization as well as protocol-specific DRLs are desirable. The cloud-based approach enables multi-center dose monitoring and offers great potential to further optimize radiation exposure in radiological departments.
  
  
  CONCLUSIONS
  • The newly developed software based on the DICOM-Structured Report enables large-scale cloud-based CT dose monitoring • The implemented software solution enables automated benchmarking in regard to national DRLs • The local radiation exposure from CT reached approximately 50 % of the national DRLs • The cloud-based approach offers great potential for multi-center dose analysis.
authors.affiliation: University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Boos
authors.affiliation: Cerner Health Services, Idstein, Germany.
authors.forename: A
authors.initials: A
authors.lastname: Meineke
authors.affiliation: University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany.
authors.forename: C
authors.initials: C
authors.lastname: Rubbert
authors.affiliation: University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany.
authors.forename: P
authors.initials: P
authors.lastname: Heusch
authors.affiliation: University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany.
authors.forename: R S
authors.initials: RS
authors.lastname: Lanzman
authors.affiliation: University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Aissa
authors.affiliation: University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany.
authors.forename: G
authors.initials: G
authors.lastname: Antoch
authors.affiliation: University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Dusseldorf, Germany.
authors.forename: P
authors.initials: P
authors.lastname: Kröpil
mesh_terms: D019985:Benchmarking / Q000379:methods / Q000592:standards; D000067917:Cloud Computing; D057225:Data Mining / Q000379:methods; D005858:Germany; D017408:Guidelines as Topic; D000069550:Machine Learning; D008452:Maximum Allowable Concentration; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D011829:Radiation Dosage; D000069079:Radiation Exposure / Q000592:standards / Q000706:statistics & numerical data*; D011834:Radiation Monitoring / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data; D011873:Radiology Information Systems / Q000592:standards* / Q000706:statistics & numerical data*; D012016:Reference Values; D014057:Tomography, X-Ray Computed / Q000592:standards / Q000706:statistics & numerical data*