A methodology to estimate the patient diameter and thickness from thoracic and abdominal projection radiographs of adult patients.

Patient dose management systems can be part of a department's quality management tools to estimate items such as the radiation burden in specific groups or list dose outliers for further follow up. Patient size information could improve both aspects, but is not generally available. A new metric is proposed to estimate patient size for thorax and abdominal projection radiography from parameters available in thedicomheader and therefore accessible by dose management software. The tested hypothesis was that an attenuation metric, related to the ratio of detector air-kerma to incident air-kerma, inversely correlates with patient size. Such a metric was defined and then worked out for thorax and abdomen projection radiography. Input material consisted of the thorax or abdominal radiographs of 137 cases, completed with a recent CT scan as ground truth size. From the CT, the water equivalent diameter (WED) and water equivalent thickness (WET) were calculated. The correlation between the attenuation metric and the patient size was established separately for thorax and abdomen. Validation of the attenuation metric predicting the patient size was performed using extra sets of examinations on three more radiographic x-ray devices, with available CT scan. The attenuation metric had a good correlation (R2) of 0.91 and 0.84 with the WED for thorax and abdomen respectively. The corresponding values for the WET were 0.89 and 0.78. Validation of the methodology on the devices with standardized exposure index in thedicomheaders showed that the WED could be estimated within ±15% and the WET within ±30% for thorax and abdomen examinations. The ground truth and estimated size were found statistically equivalent. An attenuation metric based ondicomtags allows to estimate the patient size in projection radiography. This could now be implemented in patient dose management systems.

Patient dose monitoring systems: A new way of managing patient dose and quality in the radiology department.

PURPOSE: Due to the upcoming European Directive (2013/59/EURATOM) and the increased focus on patient safety in international guidelines and regulations, Patient Dose Monitoring Systems, also called Dose Management Systems (DMS), are introduced in medical imaging departments. This article focusses on the requirements for a DMS, its benefits and the necessary implementation steps. METHOD: The implementation of a DMS can be perceived as a lengthy, yet worthy, procedure: users have to select the appropriate system for their applications, prepare data collection, validate, perform configuration, and start using the results in quality improvement projects. RESULTS: A state of the art DMS improves the quality of service, ensures patient safety and optimizes the efficiency of the department. The gain is multifaceted: the initial goal is compliance monitoring against diagnostic reference levels. At a higher level, the user gets an overview of the performance of the devices or centers that are under his supervision. Error identification, generation of alerts and workflow analysis are additional benefits. It can also enable a more patient-centric approach with personalized dosimetry. Skin dose, size-specific dose estimates and organ doses can be calculated and evaluated per patient. CONCLUSION: A DMS is a powerful tool and essential for improved quality and patient care in a radiology department. It can be configured to the needs of medical physicists, radiologists, technologists, even for the management of the hospital. Collaboration between all health professionals and stakeholders, input-output validation and communication of findings are key points in the process of a DMS implementation.

Monte Carlo simulation of breast imaging using synchrotron radiation.

PURPOSE: Synchrotron radiation (SR), being the brightest artificial source of x-rays with a very promising geometry, has raised the scientific expectations that it could be used for breast imaging with optimized results. The "in situ" evaluation of this technique is difficult to perform, mostly due to the limited available SR facilities worldwide. In this study, a simulation model for SR breast imaging was developed, based on Monte Carlo simulation techniques, and validated using data acquired in the SYRMEP beamline of the Elettra facility in Trieste, Italy. Furthermore, primary results concerning the performance of SR were derived. METHODS: The developed model includes the exact setup of the SR beamline, considering that the x-ray source is located at almost 23 m from the slit, while the photon energy was considered to originate from a very narrow Gaussian spectrum. Breast phantoms, made of Perspex and filled with air cavities, were irradiated with energies in the range of 16-28 keV. The model included a Gd(2)O(2)S detector with the same characteristics as the one available in the SYRMEP beamline. Following the development and validation of the model, experiments were performed in order to evaluate the contrast resolution of SR. A phantom made of adipose tissue and filled with inhomogeneities of several compositions and sizes was designed and utilized to simulate the irradiation under conventional mammography and SR conditions. RESULTS: The validation results of the model showed an excellent agreement with the experimental data, with the correlation for contrast being 0.996. Significant differences only appeared at the edges of the phantom, where phase effects occur. The initial evaluation experiments revealed that SR shows very good performance in terms of the image quality indices utilized, namely subject contrast and contrast to noise ratio. The response of subject contrast to energy is monotonic; however, this does not stand for contrast to noise ratio, since there is a range of optimal performance for SR (18-21 keV). In comparison to conventional mammography, SR shows improved subject contrast for energies lower than the mean energy of each spectrum. CONCLUSIONS: The comparison of the results of the two models, conventional and SR, proved that SR exhibits better performance in the majority of cases. The proposed simulation model offers the possibility to perform exhaustive search to evaluate the performance of SR in clinical applications such as breast imaging.