Performance of LGAD strip detectors for particle counting of therapeutic proton beams.

Objective. The performance of silicon detectors with moderate internal gain, named low-gain avalanche diodes (LGADs), was studied to investigate their capability to discriminate and count single beam particles at high fluxes, in view of future applications for beam characterization and on-line beam monitoring in proton therapy.Approach. Dedicated LGAD detectors with an active thickness of 55µm and segmented in 2 mm2strips were characterized at two Italian proton-therapy facilities, CNAO in Pavia and the Proton Therapy Center of Trento, with proton beams provided by a synchrotron and a cyclotron, respectively. Signals from single beam particles were discriminated against a threshold and counted. The number of proton pulses for fixed energies and different particle fluxes was compared with the charge collected by a compact ionization chamber, to infer the input particle rates.Main results. The counting inefficiency due to the overlap of nearby signals was less than 1% up to particle rates in one strip of 1 MHz, corresponding to a mean fluence rate on the strip of about 5 × 107p/(cm2·s). Count-loss correction algorithms based on the logic combination of signals from two neighboring strips allow to extend the maximum counting rate by one order of magnitude. The same algorithms give additional information on the fine time structure of the beam.Significance. The direct counting of the number of beam protons with segmented silicon detectors allows to overcome some limitations of gas detectors typically employed for beam characterization and beam monitoring in particle therapy, providing faster response times, higher sensitivity, and independence of the counts from the particle energy.

Artificial Neural Networks approach to pharmacokinetic model selection in DCE-MRI studies.

PURPOSE: In pharmacokinetic analysis of Dynamic Contrast Enhanced MRI data, a descriptive physiological model should be selected properly out of a set of candidate models. Classical techniques suggested for this purpose suffer from issues like computation time and general fitting problems. This article proposes an approach based on Artificial Neural Networks (ANNs) for solving these problems. METHODS: A set of three physiologically and mathematically nested models generated from the Tofts model were assumed: Model I, II and III. These models cover three possible tissue types from normal to malignant. Using 21 experimental arterial input functions and 12 levels of noise, a set of 27,216 time traces were generated. ANN was validated and optimized by the k-fold cross validation technique. An experimental dataset of 20 patients with glioblastoma was applied to ANN and the results were compared to outputs of F-test using Dice index. RESULTS: Optimum neuronal architecture ([6:7:1]) and number of training epochs (50) of the ANN were determined. ANN correctly classified more than 99% of the dataset. Confusion matrices for both ANN and F-test results showed the superior performance of the ANN classifier. The average Dice index (over 20 patients) indicated a 75% similarity between model selection maps of ANN and F-test. CONCLUSIONS: ANN improves the model selection process by removing the need for time-consuming, problematic fitting algorithms; as well as the need for hypothesis testing.