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Automated detection of changes in patient exposure in digital projection radiography using exposure index from DICOM header metadata
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
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2011 (English)In: Acta Oncologica, ISSN 0284-186X, E-ISSN 1651-226X, Vol. 50, no 6, p. 960-965Article in journal (Refereed) Published
Abstract [en]

Purpose. Automated collection of image data from DICOM headers enables monitoring of patient dose and image quality parameters. Manual monitoring is time consuming, owing to the large number of exposure scenarios, thus automated methods for monitoring needs to be investigated. The aim of the present work was to develop and optimise such a method. Material and methods. Exposure index values from digital systems in projection radiography were collected over a period of five years, representing data from 1.2 million projection images. The exposure index values were converted to detector dose and an automated method for detection of sustained level shifts in the resulting detector dose time series was applied using the statistical analysis tool R. The method combined handling of outliers, filtering and estimation of variation in combination with two different statistical rank tests for level shift detection. A set of 304 time series representing central body parts was selected and the level shift detection method was optimised using level shifts identified by ocular evaluation as the gold standard. Results. Two hundred and eighty-one level changes were identified that were deemed in need of further investigation. The majority of these changes were abrupt. The sensitivity and specificity of the optimised and automated detection method concerning the ocular evaluation were 0.870 and 0.997, respectively, for detected abrupt changes. Conclusions. An automated analysis of exposure index values, with the purpose of detecting changes in exposure, can be performed using the R software in combination with a DICOM header metadata repository containing the exposure index values from the images. The routine described has good sensitivity and acceptable specificity for a wide range of central body part projections and can be optimised for more specialised purposes.

Place, publisher, year, edition, pages
2011. Vol. 50, no 6, p. 960-965
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:uu:diva-156931DOI: 10.3109/0284186X.2011.579622ISI: 000292841500029OAI: oai:DiVA.org:uu-156931DiVA, id: diva2:435862
Available from: 2011-08-20 Created: 2011-08-11 Last updated: 2017-12-08Bibliographically approved
In thesis
1. Dose Management in Diagnostic Radiology - application of the DICOM imaging standard and a Monte Carlo dose engine for exposure surveillance
Open this publication in new window or tab >>Dose Management in Diagnostic Radiology - application of the DICOM imaging standard and a Monte Carlo dose engine for exposure surveillance
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Ionizing radiation is used in diagnostic radiology with a large contribution to the health of the patients. The regulations to limit the detrimental effects, e.g. cancer induction, are based on recommendations from the International Commission on Radiological Protection (ICRP). Epidemiological evidence for radiation induced cancer is expressed as a function of absorbed dose in the irradiated organs. The committee for Biological Effects of Ionizing Radiation has favored the use of Lifetime Attributable Risk, a risk estimator applicable to individuals exposed in medical applications. The imaging in radiology complies with a technical standard that potentiates the retrieval of exposure information that can be used in optimization of patient exposure. The information can also be used as input in organ dose calculations.

The aims were to apply the benefits of the technical image standard to radiation safety management by automated collection and analysis of exposure data and to adapt a Therapy Planning System (TPS) for radiotherapy to calculate dose for a Computed Tomography (CT) machine.

An automated workflow for extraction, communication and analysis of exposure data from the image files in the central image archive was defined and implemented at the institution (papers I-II). A source model for Monte Carlo simulation of the CT was developed taking into consideration the energy spectrum of the photons, the spiral movement of the X-ray beam, the beam shaping filter and the tube current modulation (paper III). The source model was used exploring the possibilities to utilize the tissue characterization methods and segmentation tools available in the TPS to devise a strategy to automate organ dose calculations for patients undergoing thorax examinations in a CT (paper IV).

The exposure data workflow was finalized showing that the technical standard for images could supply a framework for automated assembly and analysis of the data, supporting the local implementation of optimization. The CT was modeled with regard to its irradiation characteristics with uncertainties in the dose calculations below 4%. Dose calculations with the tissue characterization methods available in the TPS deviated by less than 2% from measurements and a strategy for automation of organ dose calculations was devised that could facilitate individual risk estimates in CT.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. p. 44
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1407
Keywords
Radiology, metadata, DICOM, radiation safety, Monte Carlo, source model, patient model, optimization, justification
National Category
Other Medical Sciences not elsewhere specified
Research subject
Medical Radiophysics
Identifiers
urn:nbn:se:uu:diva-335575 (URN)978-91-513-0179-2 (ISBN)
Public defence
2018-02-02, Lecture hall, Falu lasarett, Lasarettsvägen 10, Falun, 13:00 (English)
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Supervisors
Available from: 2018-01-08 Created: 2017-12-07 Last updated: 2018-03-07

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Källman, Hans-Erik

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