M. Baptista

Optimal photon energy comparison between digital breast tomosynthesis and mammography: A case study

A comparison, in terms of the optimal energy that maximizes the image quality between digital breast tomosynthesis (DBT) and digital mammography (DM) was performed in a MAMMOMAT Inspiration system (Siemens) based on amorphous selenium flat panel detector. In this paper we measured the image quality by the signal difference-to-noise ratio (SDNR), and the patient risk by the mean glandular dose (MGD). Using these quantities we compared the optimal voltage that maximizes the image quality both in breast tomosynthesis and standard mammography acquisition mode. The comparison for the two acquisition modes was performed for a W/Rh anode filter combinations by using a 4.5 cm tissue equivalent mammography phantom. Moreover, in order to check if the used equipment was quantum noise limited, the relation of the relative noise with respect to the detector dose was evaluated. Results showed that in the tomosynthesis acquisition mode the optimal voltage is 28 kV, whereas in standard mammography the optimal voltage is 30 kV. The automatic exposure control (AEC) of the system selects 28 kV as optimal voltage both for DBT and DM. Monte Carlo simulations showed a qualitative agreement with the AEC selection system, since an optimal monochromatic energy of 20 keV was found both for DBT and DM. Moreover, the check about the noise showed that the system is not completely quantum noise limited, and this issue could explain the experimental slight difference in terms of optimal voltage between DBT and DM. According to these results, the use of higher voltage settings is not justified for the improvement of the image quality during a DBT examination.

Medical staff extremity dosimetry in CT fluoroscopy: an anthropomorphic hand voxel phantom study.

This work aims to contribute to the study of the radiation dose distribution delivered to the hands of medical staff members during a general computed tomographic (CT) fluoroscopic guided procedure. In this study, both Monte Carlo simulations and measurements were performed. For free-in-air and computed tomography dose index (CTDI) body phantom measurements, a standard pencil ionization chamber (IC) 100 mm long was used. The CT scanner model was implemented using MCNPX (Monte Carlo N-Particle eXtended) and was successfully validated by comparing the simulated results with measurements. Subsequently, CT images of a hand, together with an anthropomorphic phantom, were voxelized and used with the MCNPX code for dose calculations. The hand dose distribution study was performed both by using thermo-luminescent detector measurements and Monte Carlo simulations. The validated simulation tool provides a new perspective for detailed investigations of CT-irradiation scenarios. Simulations show that there is a strong dose gradient, namely the even zones of the hand that are in precise vicinity to the x-ray beam only receive about 4% of the maximum dose delivered to adjacent areas which are directly exposed to the primary x-ray beam. Finally, the scatter contribution of the patient was also studied through MC simulations. The results show that for directly exposed parts of the hand surface, the dose is reduced by the body of the patient (due to the shielding), whereas the dose is increased by scattered radiation from the patient for parts of the skin that receive scattered radiation only.

Comparative case study of life usage and data-driven prognostics techniques using aircraft fault messages

Abstract Prognostics are a key activity in repair and maintenance operations. A recent approach to condition-based maintenance is the data-driven approach. This approach has been mostly based on past failure time measures, and sensed measurements of component degradation to derive estimates of the remaining useful life of equipment. An alternative source of data, rarely used in these models, is the stream of automatic messages derived from diagnostics systems, which consist of fault codes indicating abnormal events or deviations from optimal operation. Despite the richness and concise nature of these messages, their difficult interpretation poses significant challenges to its use in prognostics. This paper aims to show that data-driven prognostics based on this type of messages can be better suited to maintenance than time-based approaches. We illustrate this comparison with an industrial case study involving the removal times of a bleed valve from the aircraft air management system. Our experimental results reveal a significant accuracy improvement over the contrasting time-based models. We also establish the contribution to this improvement of the data-driven methods and message-related predictors.

Cancer risk estimation in Digital Breast Tomosynthesis using GEANT4 Monte Carlo simulations and voxel phantoms

The aim of this work was to estimate the risk of radiation induced cancer following the Portuguese breast screening recommendations for Digital Mammography (DM) when applied to Digital Breast Tomosynthesis (DBT) and to evaluate how the risk to induce cancer could influence the energy used in breast diagnostic exams. The organ doses were calculated by Monte Carlo simulations using a female voxel phantom and considering the acquisition of 25 projection images. Single organ cancer incidence risks were calculated in order to assess the total effective radiation induced cancer risk. The screening strategy techniques considered were: DBT in Cranio-Caudal (CC) view and two-view DM (CC and Mediolateral Oblique (MLO)). The risk of cancer incidence following the Portuguese screening guidelines (screening every two years in the age range of 50-80years) was calculated by assuming a single CC DBT acquisition view as standalone screening strategy and compared with two-view DM. The difference in the total effective risk between DBT and DM is quite low. Nevertheless in DBT an increase of risk for the lung is observed with respect to DM. The lung is also the organ that is mainly affected when non-optimal beam energy (in terms of image quality and absorbed dose) is used instead of an optimal one. The use of non-optimal energies could increase the risk of lung cancer incidence by a factor of about 2.

Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling

Abstract Presently, time-based airline maintenance scheduling does not take fault predictions into account, but happens at fixed time-intervals. This may result in unnecessary maintenance interventions and also in situations where components are not taken out of service despite exceeding their designed risk of failure. To address this issue we propose a framework that can predict when a component/system will be at risk of failure in the future, and therefore, advise when maintenance actions should be taken. In order to facilitate such prediction, we employ an auto-regressive moving average (ARMA) model along with data-driven techniques, and compare the performance of multiple data-driven techniques. The ARMA model adds a new feature that is used within the data-driven model to give the final prediction. The novelty of our work is the integration of the ARMA methodology with data-driven techniques to predict fault events. This study reports on a real industrial case of unscheduled removals of a critical valve of the aircraft engine. Our results suggest that the support vector regression model can outperform the life usage model on the evaluation measures of sample standard deviation, median error, median absolute error, and percentage error. The generalized linear model provides an effective approach for predictive maintenance with comparable results to the baseline. The remaining data-driven models have a lower overall performance.

Assessment of the occupational exposure in real time during interventional cardiology procedures

Interventional cardiology (IC) procedures can be complex, requiring the operators to work near the patient, during long exposure times. Owing to scattered radiation in the patient and the fluoroscopic equipment, the medical staff are exposed to a non-uniform radiation field and can receive high radiation doses. In this study, it is proposed to analyse staff doses obtained in real time, during IC procedures. A system for occupational dosimetry in real time was used. In order to identify some parameters that may affect the staff doses, Monte Carlo (MC) calculations, using MCNPX v.2.7.0 code and voxel phantoms, were performed. The data obtained from measurements, together with MC simulations, allowed the identification of actions and behaviours of the medical staff that could be considered a risk under routine working conditions. The implementation of this monitoring system for exposure of personnel may have a positive effect on optimisation of radiological protection in fluoroscopically guided cardiac procedures.

Paediatric CT exposures: comparison between CTDIvol and SSDE methods using measurements and Monte Carlo simulations

Computed tomography (CT) is one of the most used techniques in medical diagnosis, and its use has become one of the main sources of exposure of the population to ionising radiation. This work concentrates on the paediatric patients, since children exhibit higher radiosensitivity than adults. Nowadays, patient doses are estimated through two standard CT dose index (CTDI) phantoms as a reference to calculate CTDI volume (CTDI vol) values. This study aims at improving the knowledge about the radiation exposure to children and to better assess the accuracy of the CTDI vol method. The effectiveness of the CTDI vol method for patient dose estimation was then investigated through a sensitive study, taking into account the doses obtained by three methods: CTDI vol measured, CTDI vol values simulated with Monte Carlo (MC) code MCNPX and the recent proposed method Size-Specific Dose Estimate (SSDE). In order to assess organ doses, MC simulations were executed with paediatric voxel phantoms.

A Framework for Robust Address Assignment in WSNs Whispering to Avoid Intruders

Wireless sensor networks (WSNs) are becoming bigger, and with this growth comes the need for new automatic mechanisms for initializations done by hand. One of those mechanisms is the assignment of addresses to nodes. Several solutions were already proposed for mobile ad hoc networks but they either (i) do not scale well for WSN; (ii) have no energy constraints; (iii) have no security considerations; (iv) or have no mechanisms to handle fusion of network partitions. We proposed an address self-assignment protocol which uses negative acknowledgements and an improved version of a flood control mechanism to minimize the energy spent; uses a technique named whispering to achieve robustness against malicious nodes; is able to detect dynamic network re-joint and dynamic node addition without exchanging specific messages; and handles both dynamic events without compromising routing tables.

Response of the REWARD detection system to the presence of a Radiological Dispersal Device

The objective of the REWARD project consisted in building a mobile system for real time, wide area radiation surveillance, using a CdZnTe detector for gamma radiation and a neutron detector based on novel silicon technologies. The sensing unit includes a GPS system and a wireless communication interface to send the data remotely to a monitoring base station, where it will be analyzed in real time and correlated with historical data from the tag location, in order to generate an alarm when an abnormal situation is detected. The main objective of this work consisted in making predictions regarding the behavior of the REWARD system in the presence of a Radiological Dispersion Device (RDD), one of the reference scenarios foreseen for REWARD, using the Monte Carlo simulation program MCNP6. Experimental tests were performed at the Fire Brigades Facilities in Rome and at the Naples Fire Brigades. The response of the REWARD detection system to the presence of an RDD is predicted and discussed.

Determination of backscatter factors in breast tomosynthesis using MCNPX simulations and measurements

The perspective of adding digital breast tomosynthesis (DBT) to standard mammography in screening raises concerns regarding the dose absorbed by the fibroglandular breast tissue. Thus, it is important to estimate accurately the mean glandular dose (MGD), although there are no standard protocols for dosimetry, concerning DBT. This study aims at introducing backscatter factors (BSF) to calculate the entrance surface air kerma (ESAK), directly on patients or phantoms, in order to be introduced in the formalism proposed by Dance et al. MCNPX simulations were performed, to mimic a DBT acquisition, for a wide range of X-ray spectra. A homogeneous breast phantom with 50 % of glandular tissue was considered and several thicknesses were evaluated. Dose measurements were performed, to validate and support the simulation results. The BSF may indicate a real MGD estimation in vivo for DBT examinations and contribute for the improvement of the current guidelines used in these applications.