Pencil beam kernel-based dose calculations on CT data for a mixed neutron-gamma fission field applying tissue correction factors.

Objective.For fast neutron therapy with mixed neutron and gamma radiation at the fission neutron therapy facility MEDAPP at the research reactor FRM II in Garching, no clinical dose calculation software was available in the past. Here, we present a customized solution for research purposes to overcome this lack of three-dimensional dose calculation.Approach.The applied dose calculation method is based on two sets of decomposed pencil beam kernels for neutron and gamma radiation. The decomposition was performed using measured output factors and simulated depth dose curves and beam profiles in water as reference medium. While measurements were performed by applying the two-chamber dosimetry method, simulated data was generated using the Monte Carlo code MCNP. For the calculation of neutron dose deposition on CT data, tissue-specific correction factors were generated for soft tissue, bone, and lung tissue for the MEDAPP neutron spectrum. The pencil beam calculations were evaluated with reference to Monte Carlo calculations regarding accuracy and time efficiency.Main results.In water, dose distributions calculated using the pencil beam approach reproduced the input from Monte Carlo simulations. For heterogeneous media, an assessment of the tissue-specific correction factors with reference to Monte Carlo simulations for different tissue configurations showed promising results. Especially for scenarios where no lung tissue is present, the dose calculation could be highly improved by the applied correction method.Significance.With the presented approach, time-efficient dose calculations on CT data and treatment plan evaluations for research purposes are now available for MEDAPP.

Identification and quantification of anomalies in environmental gamma dose rate time series using artificial intelligence.

Gamma dose rate (GDR) monitors are the most widely used tool for continuous monitoring of environmental radioactivity. They are inexpensive to procure and operate, and generally require little maintenance. However, since no spectral information is available, the detection limit for irregularities is correspondingly high; A value around 20 nSv/h is often called out. By adding weather data to the GDR measurement and a sequence of machine learning algorithms, the anomaly detection sensitivity can be significantly increased while simultaneously decreasing the number of false positives. The algorithms were designed such that an integrated safety net prevents false negatives. First, the precipitation-induced GDR peaks from washed-out Radon progeny are removed by means of regression, provided that a check of the regression parameters shows sufficient agreement with past data at the measurement site. A neural network then calculates the expected value of the remaining GDR baseline for the prevailing conditions. Finally, an anomaly detection is carried out on the remainder between the expected and actual GDR baseline value. Extreme value theory is used to detect point anomalies, and hierarchical clustering of subsequences for slower processes. By combining the two detection methods, the full spectrum of irregularities is covered. The algorithms were implemented in Python and trained with real measurement data from the German GDR monitoring network. For verification, the data were enriched with results from JRODOS simulations of a nuclear power plant accident. Altogether, the presented methodology can lower the detection limit of irregularities to about 4 nSv/h, i. e. about a factor of 5 below the previous consensus value. The algorithm detects as well as quantifies the anomaly in the GDR, allowing for additional conclusions like potentially involved isotopes. Most important, it allows to refrain from the current practice of defining fixed alarming thresholds between the two contradicting goals of high sensitivity and low false alarm rate. Instead, it allows to transition to the more natural alarming on deviations from the expectation.

The Munich Fission Neutron Therapy Facility MEDAPP at the research reactor FRM II.

PURPOSE: At the new research reactor FRM II of the Technical University of Munich (TUM), the facility for Medical Applications (MEDAPP) was installed where fast neutrons are available as a beam for medical use. MATERIAL AND METHODS: Thermal neutrons induce fission in a pair of uranium converter plates and generate fast neutrons which are guided to the patient by a beam tube. The maximum opening of the multi leaf collimator (MLC) is 30x20 cm2 WxH. The beam is characterized by neutron-photon mixed beam phantom dosimetry. Specific safety measures are outlined. RESULTS: The neutron and gamma dose rates are 0.52 Gy/min and 0.20 Gy/min, respectively, in 2 cm depth of a water phantom. The half maximum depth of the neutron dose rate in water is 5.4 cm (mean neutron energy 1.9+/-0.1 MeV). Conformity with the European Medical Devices Directive (MDD) 93/42/EEG, was proven so that MEDAPP has a CE mark and since February 2007 also the license for clinical operation. CONCLUSION: The clinical neutron irradiations of malignant tumors, which were performed at the former research reactor FRM until 2000, can be continued at FRM II under improved conditions. First patients were irradiated in June 2007.