Staff dose evaluation by application of radiation protection during endoscopic retrograde cholangiopancreatography (ERCP) procedures performed with a mobile C-arm.

BACKGROUND: The radiation dose to staff performing endoscopic retrograde cholangiopancreatography (ERCP) is not negligible. PURPOSE: To evaluate the shielding effect of a table-suspended lower-body radiation shield for the positions in the room occupied by the operator, assisting nurse, and anesthesiologist, used during ERCP procedures with a mobile C-arm. MATERIAL AND METHODS: Eye lens dose, whole body dose, and extremity dose were measured with and without a table-suspended lower-body radiation shield in a phantom model and in clinical routine work. The effect of the shield was evaluated for each scenario and compared, and a projection was made for when shielding should be required from a regulatory point of view. RESULTS: In the phantom measurements, the shield provided significant shielding effects on the body and lower extremities for the operator but no significant shielding of the eye lens. The shielding effect for the assisting nurse was limited to the lower extremity. The clinical measurements yielded the same general result as the phantom measurements, with the major difference that the shield provided no significant reduction in the whole-body dose to the operator. CONCLUSION: The table-suspended shield has a significant shielding effect for the lower extremities of the operator and assisting nurse. For annual dose-area product values >300,000 cGycm2, the protection of the operator should be reinforced with a ceiling-suspended shield to avoid doses to the eye lens and body in excess of regulatory dose restrictions.

Toward automated and personalized organ dose determination in CT examinations - A comparison of two tissue characterization models for Monte Carlo organ dose calculation with a Therapy Planning System.

PURPOSE: Computed tomography (CT) is a versatile tool in diagnostic radiology with rapidly increasing number of examinations per year globally. Routine adaption of the exposure level for patient anatomy and examination protocol cause the patients' exposures to become diversified and harder to predict by simple methods. To facilitate individualized organ dose estimates, we explore the possibility to automate organ dose calculations using a radiotherapy treatment planning system (TPS). In particular, the mapping of CT number to elemental composition for Monte Carlo (MC) dose calculations is investigated. METHODS: Organ dose calculations were done for a female thorax examination test case with a TPS (Raystation™, Raysearch Laboratories AB, Stockholm, Sweden) utilizing a MC dose engine with a CT source model presented in a previous study. The TPS's inherent tissue characterization model for mapping of CT number to elemental composition of the tissues was calibrated using a phantom with known elemental compositions and validated through comparison of MC calculated dose with dose measured with Thermo Luminescence Dosimeters (TLD) in an anthropomorphic phantom. Given the segmentation tools of the TPS, organ segmentation strategies suitable for automation were analyzed for high contrast organs, utilizing CT number thresholding and model-based segmentation, and for low contrast organs utilizing water replacements in larger tissue volumes. Organ doses calculated with a selection of organ segmentation methods in combination with mapping of CT numbers to elemental composition (RT model), normally used in radiotherapy, were compared to a tissue characterization model with organ segmentation and elemental compositions defined by replacement materials [International Commission on Radiological Protection (ICRP) model], frequently favored in imaging dosimetry. RESULTS: The results of the validation with the anthropomorphic phantom yielded mean deviations from the dose to water calculated with the RT and ICRP model as measured with TLD of 1.1% and 1.5% with maximum deviations of 6.1% and 8.7% respectively over all locations in the phantom. A strategy for automated organ segmentation was evaluated for two different risk organ groups, that is, low contrast soft organs and high contrast organs. The relative deviation between organ doses calculated with the RT model and with the ICRP model varied between 0% and 20% for the thorax/upper abdomen risk organs. CONCLUSIONS: After calibration, the RT model in the TPS provides accurate MC dose results as compared to measurements with TLD and the ICRP model. Dosimetric feasible segmentation of the risk organs for a female thorax demonstrates a possibility for automation using the segmentation tool available in a TPS for high contrast organs. Low contrast soft organs can be represented by water volumes, but organ dose to the esophagus and thyroid must be determined using standardized organ shapes. The uncertainties of the organ doses are small compared to the overall uncertainty, at least an order of magnitude larger, in the estimates of lifetime attributable risk (LAR) based on organ doses. Large-scale and automated individual organ dose calculations could provide an improvement in cancer incidence estimates from epidemiological studies.

The European Federation of Organisations for Medical Physics (EFOMP) White Paper: Big data and deep learning in medical imaging and in relation to medical physics profession.

Big data and deep learning will profoundly change various areas of professions and research in the future. This will also happen in medicine and medical imaging in particular. As medical physicists, we should pursue beyond the concept of technical quality to extend our methodology and competence towards measuring and optimising the diagnostic value in terms of how it is connected to care outcome. Functional implementation of such methodology requires data processing utilities starting from data collection and management and culminating in the data analysis methods. Data quality control and validation are prerequisites for the deep learning application in order to provide reliable further analysis, classification, interpretation, probabilistic and predictive modelling from the vast heterogeneous big data. Challenges in practical data analytics relate to both horizontal and longitudinal analysis aspects. Quantitative aspects of data validation, quality control, physically meaningful measures, parameter connections and system modelling for the future artificial intelligence (AI) methods are positioned firmly in the field of Medical Physics profession. It is our interest to ensure that our professional education, continuous training and competence will follow this significant global development.

Source modeling for Monte Carlo dose calculation of CT examinations with a radiotherapy treatment planning system.

PURPOSE: Radiation dose to patients undergoing examinations with Multislice Computed Tomography (MSCT) as well as Cone Beam Computed Tomography (CBCT) is a matter of concern. Risk management could benefit from efficient replace rational dose calculation tools. The paper aims to verify MSCT dose calculations using a Treatment Planning System (TPS) for radiotherapy and to evaluate four different variations of bow-tie filter characterizations for the beam model used in the dose calculations. METHODS: A TPS (RayStation™, RaySearch Laboratories, Stockholm, Sweden) was configured to calculate dose from a MSCT (GE Healthcare, Wauwatosa, WI, USA). The x-ray beam was characterized in a stationary position the by measurements of the Half-Value Layer (HVL) in aluminum and kerma along the principal axes of the isocenter plane perpendicular to the beam. A Monte Carlo source model for the dose calculation was applied with four different variations on the beam-shaping bow-tie filter, taking into account the different degrees of HVL information but reconstructing the measured kerma distribution after the bow-tie filter by adjusting the photon sampling function. The resulting dose calculations were verified by comparison with measurements in solid water as well as in an anthropomorphic phantom. RESULTS: The calculated depth dose in solid water as well as the relative dose profiles was in agreement with the corresponding measured values. Doses calculated in the anthropomorphic phantom in the range 26-55 mGy agreed with the corresponding thermo luminescence dosimeter (TLD) measurements. Deviations between measurements and calculations were of the order of the measurement uncertainties. There was no significant difference between the different variations on the bow-tie filter modeling. CONCLUSIONS: Under the assumption that the calculated kerma after the bow-tie filter replicates the measured kerma, the central specification of the HVL of the x-ray beam together with the kerma distribution can be used to characterize the beam. Thus, within the limits of the study, a flat bow-tie filter with an HVL specified by the vendor suffices to calculate the dose distribution. The TPS could be successfully configured to replicate the beam movement and intensity modulation of a spiral scan with dose modulation, on the basis of the specifications available in the metadata of the digital images and the log file of the CT.

Automated detection of changes in patient exposure in digital projection radiography using exposure index from DICOM header metadata.

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.

DICOM Metadata repository for technical information in digital medical images.

UNLABELLED: The diagnostic medical image contains, apart from the pixel data, a detailed description of how the image was produced. The information reveals details on image geometry, radiation data as well as more complex quality index in a varying degree, mostly dependent on the age of the equipment. There is no simple way to retrieve, process and display this data in a general image workstation. MATERIAL AND METHODS: Since November 2004 a DICOM metadata repository has been used to record image header parameters. The automated data extraction, storage and display are based on simple standard programming and have performed without malfunction since the start, today containing metadata from 18 million images. RESULTS: The data in the metadata repository has been used in dose optimization for a Computed Radiography image plate system, analyzing the exposure index and making use of the possibilities to organize the data in examinations, projections as well as examination rooms. Analysis of exposure index in the context of these parameters shows promising qualities as it makes detection of dosimetric problems as well as follow-up of dose adjustments simpler. Current work is aimed at creating a vendor independent platform and to further develop methods to support dose optimization for flat panel direct digital detectors and computed tomography (CT) systems. The possibilities to detect equipment malfunction will be further investigated.