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Gray detector, a dose monitoring software for radiology departments

Authors :
Ivan Izzo
Sorin Virban
Alessandro Brondi
Michela Dotta
Source :
Physica Medica. 32:955
Publication Year :
2016
Publisher :
Elsevier BV, 2016.

Abstract

The aim of this work is to present the gray detector, a web-based patient radiation dose monitoring software developed to answer on health protection of individuals against the dangers of ionizing radiation in relation to medical exposure, (Council Directive 2013/59/Euratom) This software provides to capture, track and report radiation dose directly from any imaging device or PACS, completely vendor neutral solution, and it is the simple yet scalable way to document dose, set reference levels, benchmark performance, manage protocols and identify outliers. The solution gives the possibility to integrate all the information about the dose with the data in RIS: in this way there is the possibility to context the dosimetric data and to follow the optimization of the radiologic risk management and the ratio risk/benefit of the image. Gray detector manages all diagnostic modality tracking and archiving all dosimetry data: CT, Mammography, Interventional, Cardiovascular, Radiography and Nuclear Medicine. It is compatible with DICOM SR and DICOM MPPS messages, Query & Retrieve DICOM image header (for non MSSP/SR DICOM modality), to extract all relevant information from dose and maximize data collection, crossing the fundamental data stored in the RIS database. All data collected can be viewed through customizable charts or exported according to standard formats gray detector has an “alert system”, that gives the possibility to send email and display messages when the modality gives an over dose, based on DRL The integration of the gray detector with information systems, may improve clinical quality performance in diagnostic imaging

Details

ISSN :
11201797
Volume :
32
Database :
OpenAIRE
Journal :
Physica Medica
Accession number :
edsair.doi...........ea33d8491220cfac2055285ff244a8da
Full Text :
https://doi.org/10.1016/j.ejmp.2016.05.034