Python software for patient exposure analysis in radiology

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Abstract Introduction: The values describing the patient’s exposure in X-ray diagnostics and interventional radiology should be compared with several independent criteria: diagnostic reference levels, dose levels associated with potential unintended exposure, and dose levels requiring specific action in interventional radiology. Radiation dose monitoring software (RDMS) is not always available, and may not perform all the tasks or cover all radiological devices. The aim of the work was to create software that would allow the analysis of dose data from various sources. Material and Methods: Python programming language was chosen due to the availability of libraries enabling access to information stored in DICOM files (pydicom, rdsr-navigator) and data analysis functions (pandas). The software was created, able to import data from various sources (Excel files, DICOM files, RDSR dosimetry reports), reject data based on the indicated criterion, multiply selected numerical data by the indicated factor, compare dose information with preset criteria (including diagnostic reference levels), and create summary reports. Data describing how to analyse data from a specific source has been placed in easy-to-modify external configuration files (Excel spreadsheets). The results of the dose analysis are saved in Excel spreadsheets. Results: The software was tested on DICOM files from mammography and radiography devices (obtained from PACS), RDSR dosimetry reports from interventional radiology units (obtained from PACS or exported directly from the radiology device), Excel files with data exported from a commercially available RDMS system. It has been confirmed that it is possible to change the data analysis method (e.g. add a new radiological device) by modifying the configuration file without changing the program code. Conclusion: The software allows users to analyse patient exposure data from various sources, compare it with different dose levels, and prepare reports useful in internal clinical audits.