Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations.

Ion beam therapy enables a highly accurate dose conformation delivery to the tumor due to the finite range of charged ions in matter (i.e. Bragg peak (BP)). Consequently, the dose profile is very sensitive to patients anatomical changes as well as minor mispositioning, and so it requires improved dose control techniques. Proton interaction vertex imaging (IVI) could offer an online range control in carbon ion therapy. In this paper, a statistical method was used to study the sensitivity of the IVI technique on experimental data obtained from the Heidelberg Ion-Beam Therapy Center. The vertices of secondary protons were reconstructed with pixelized silicon detectors. The statistical study used the [Formula: see text] test of the reconstructed vertex distributions for a given displacement of the BP position as a function of the impinging carbon ions. Different phantom configurations were used with or without bone equivalent tissue and air inserts. The inflection points in the fall-off region of the longitudinal vertex distribution were computed using different methods, while the relation with the BP position was established. In the present setup, the resolution of the BP position was about 4-5 mm in the homogeneous phantom under clinical conditions (10 incident carbon ions). Our results show that the IVI method could therefore monitor the BP position with a promising resolution in clinical conditions.

Longitudinal prompt-gamma ray profiles have been measured with a multi-slit multidetector configuration at a 75 MeV/u 13 C beam and with a PMMA target. Selections in time-offlight and energy have been applied in order to discriminate prompt-gamma rays produced in the target from background events. The ion ranges which have been extracted from each individual detector module agree amongst each other and are consistent with theoretical expectations. In a separate dedicated experiment with 200 MeV/u 12 C ions the fraction of inter-detector scattering has been determined to be on the 10%-level via a combination of experimental results and simulations. At the same experiment different collimator configurations have been tested and the shielding properties of tungsten and lead for prompt-gamma rays have been measured.

International audienceThe goal of this study is to tune the design of the absorber detector of a Compton camera for prompt γ-ray imaging during ion beam therapy. The response of the Compton camera to a photon point source with a realistic energy spectrum (corresponding to the prompt γ-ray spectrum emitted during the carbon irradiation of a water phantom) is studied by means of Geant4 simulations. Our Compton camera consists of a stack of 2 mm thick silicon strip detectors as a scatter detector and of a scintillator plate as an absorber detector. Four scintillators are considered: LYSO, NaI, LaBr3 and BGO. LYSO and BGO appear as the most suitable materials, due to their high photo-electric cross-sections, which leads to a high percentage of fully absorbed photons. Depth-of-interaction measurements are shown to have limited influence on the spatial resolution of the camera. In our case, the thickness which gives the best compromise between a high percentage of photons that are fully absorbed and a low parallax error is about 4 cm for the LYSO detector and 4.5 cm for the BGO detector. The influence of the width of the absorber detector on the spatial resolution is not very pronounced as long as it is lower than 30 cm

Monte Carlo simulations based on the Geant4 toolkit (version 9.1) were performed to study the emission of secondary prompt-gamma rays produced by nuclear reactions during carbon ion-beam therapy. These simulations were performed along with an experimental program and instrumentation developments which aim at designing a prompt-gamma ray device for real-time control of hadrontherapy. The objective of the present study is twofold: firstly, to present the features of the prompt-gamma radiation in the case of carbon ion irradiation; secondly, to simulate the experimental setup and to compare measured and simulated counting rates corresponding to four different experiments. For each experiment, we found that simulations overestimate prompt-gamma ray detection yields by a factor of 12. Uncertainties in fragmentation cross sections and binary cascade model cannot explain such discrepancies. The so-called "photon evaporation" model is therefore questionable and its modification is currently in progress.

No abstract

Gliomas are infiltrative brain tumors with a margin difficult to identify. 5-ALA induced PpIX fluorescence measurements are a clinical standard, but expert-based classification models still lack sensitivity and specificity. Here a fully automatic clustering method is proposed to discriminate glioma margin. This is obtained from spectroscopic fluorescent measurements acquired with a recently introduced intraoperative set up. We describe a data-driven selection of best spectral features and show how this improves results of margin prediction from healthy tissue by comparison with the standard biomarkerbased prediction. This pilot study based on 10 patients and 50 samples shows promising results with a best performance of 77% of accuracy in healthy tissue prediction from margin tissue. Gliomas account for more than fifty percent of primitive brain tumors. They are infiltrative tumors, with a margin difficult to identify and discriminate from the surrounding healthy tissues. The world health organization (WHO) classifies gliomas in 4 grades 1 , but most studies commonly consider two separate groups: High-Grade Gliomas (HGG) and Low-Grade Gliomas (LGG). Studies have shown that in 85% cases, recurrences of HGG are localized less than 2 centimeters away from the initial tumor 2. Then, improving the extent of resection is relevant to prevent recurrence and improve life quality and expectancy 3-5. Pre-operative MRI combined with neuro-navigation is currently used in the operating theater 6,7 but shows strong limitations 8-10. 5-aminolevulinic acid (5-ALA) induced protoporphyrin IX (PpIX) fluorescence microscopy has shown its relevance in neuro-oncology 11. PpIX absorbs light at 405 nm and emits fluorescence with a main peak centered at 634 nm. This technique is the actual clinical standard for PpIX-based surgical assistance. However, its sensitivity is still limited when applied to low-density infiltrative parts of HGG 12,13 or to LGG 14. Various 5-ALA induce PpIX fluorescence spectroscopy methods have been proposed to overcome these sensitivity issues. Previous works 6,15-24 , focus on the extraction of biomarkers from the measurements, based on a priori information on the link between the biomarkers and the microenvironment of PpIX. These approaches are known as expert-based, and various biomarker models have been proposed in the literature. Quantification of PpIX concentration 15 show enhanced sensitivity either in HGG 16 or in LGG 17. Normalization procedures of biomarkers can also increase their robustness 6,18,19. Other works suggest that relevant models could be obtained based on the shape of the PpIX emission spectrum 18-26. These works show that the PpIX fluorescence emission spectral complexity in tissue is closely linked with the pathological status. However, the still unsolved origin of this complexity impairs the extraction of the best features with an expert-based related method, thus preventing the classification of measurements into relevant pathological status.

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