E. Schmitt

Bio: E. Schmitt is an academic researcher from Istituto Nazionale di Fisica Nucleare. The author has contributed to research in topics: Superposition principle & Sobp. The author has an hindex of 2, co-authored 2 publications receiving 27 citations.

A novel algorithm for the calculation of physical and biological irradiation quantities in scanned ion beam therapy: the beamlet superposition approach

Abstract: The calculation algorithm of a modern treatment planning system for ion-beam radiotherapy should ideally be able to deal with different ion species (e.g. protons and carbon ions), to provide relative biological effectiveness (RBE) evaluations and to describe different beam lines. In this work we propose a new approach for ion irradiation outcomes computations, the beamlet superposition (BS) model, which satisfies these requirements. This model applies and extends the concepts of previous fluence-weighted pencil-beam algorithms to quantities of radiobiological interest other than dose, i.e. RBE- and LET-related quantities. It describes an ion beam through a beam-line specific, weighted superposition of universal beamlets. The universal physical and radiobiological irradiation effect of the beamlets on a representative set of water-like tissues is evaluated once, coupling the per-track information derived from FLUKA Monte Carlo simulations with the radiobiological effectiveness provided by the microdosimetric kinetic model and the local effect model. Thanks to an extension of the superposition concept, the beamlet irradiation action superposition is applicable for the evaluation of dose, RBE and LET distributions. The weight function for the beamlets superposition is derived from the beam phase space density at the patient entrance. A general beam model commissioning procedure is proposed, which has successfully been tested on the CNAO beam line. The BS model provides the evaluation of different irradiation quantities for different ions, the adaptability permitted by weight functions and the evaluation speed of analitical approaches. Benchmarking plans in simple geometries and clinical plans are shown to demonstrate the model capabilities.

Analysis of the reliability of the local effect model for the use in carbon ion treatment planning systems.

Abstract: In radiotherapy with carbon ions, biological effects of treatments have to be predicted. For this purpose, one of the most used models is the local effect model (LEM) developed at the Gesellschaft fur Schwerionenforschung (GSI), Germany. At the Istituto Nazionale di Fisica Nucleare, Italy, the reliability of the last published version of LEM (LEM III) in reproducing radiobiological data has been checked under both monoenergetic and spread-out Bragg peak (SOBP) carbon-ion irradiation. The reproduction of the monoenergetic measurements with the LEM was rather successful for some cell lines, while it failed for the less-radioresistant ones. The SOBP experimental trend was predicted by the LEM, but a large shift between model curves and measured points was observed.

Prompt-gamma monitoring in hadrontherapy: A review

Abstract: Secondary radiation emission induced by nuclear reactions is correlated to the path of ions in matter. Therefore, such penetrating radiation can be used for in vivo control of hadrontherapy treatments, for which the primary beam is absorbed inside the patient. Among secondary radiations, prompt-gamma rays were proposed for real-time verification of ion range. Such a verification is a desired condition to reduce uncertainties in treatment planning. For more than a decade, efforts have been undertaken worldwide to promote prompt-gamma-based devices to be used in clinical conditions. Dedicated cameras are necessary to overcome the challenges of a broad- and high-energy distribution, a large background, high instantaneous count rates, and compatibility constraints with patient irradiation. Several types of prompt-gamma imaging devices have been proposed, that are either physically-collimated or electronically collimated (Compton cameras). Clinical tests are now undergoing. Meanwhile, other methods than direct prompt-gamma imaging were proposed, that are based on specific counting using either time-of-flight or photon energy measurements. In the present article, we make a review and discuss the state of the art for all techniques using prompt-gamma detection to improve the quality assurance in hadrontherapy.

Fred: a GPU-accelerated fast-Monte Carlo code for rapid treatment plan recalculation in ion beam therapy.

Abstract: Ion beam therapy is a rapidly growing technique for tumor radiation therapy. Ions allow for a high dose deposition in the tumor region, while sparing the surrounding healthy tissue. For this reason, the highest possible accuracy in the calculation of dose and its spatial distribution is required in treatment planning. On one hand, commonly used treatment planning software solutions adopt a simplified beam-body interaction model by remapping pre-calculated dose distributions into a 3D water-equivalent representation of the patient morphology. On the other hand, Monte Carlo (MC) simulations, which explicitly take into account all the details in the interaction of particles with human tissues, are considered to be the most reliable tool to address the complexity of mixed field irradiation in a heterogeneous environment. However, full MC calculations are not routinely used in clinical practice because they typically demand substantial computational resources. Therefore MC simulations are usually only used to check treatment plans for a restricted number of difficult cases. The advent of general-purpose programming GPU cards prompted the development of trimmed-down MC-based dose engines which can significantly reduce the time needed to recalculate a treatment plan with respect to standard MC codes in CPU hardware. In this work, we report on the development of fred, a new MC simulation platform for treatment planning in ion beam therapy. The code can transport particles through a 3D voxel grid using a class II MC algorithm. Both primary and secondary particles are tracked and their energy deposition is scored along the trajectory. Effective models for particle-medium interaction have been implemented, balancing accuracy in dose deposition with computational cost. Currently, the most refined module is the transport of proton beams in water: single pencil beam dose-depth distributions obtained with fred agree with those produced by standard MC codes within 1-2% of the Bragg peak in the therapeutic energy range. A comparison with measurements taken at the CNAO treatment center shows that the lateral dose tails are reproduced within 2% in the field size factor test up to 20 cm. The tracing kernel can run on GPU hardware, achieving 10 million primary [Formula: see text] on a single card. This performance allows one to recalculate a proton treatment plan at 1% of the total particles in just a few minutes.

Fast robust dose calculation on GPU for high-precision 1 H, 4 He, 12 C and 16 O ion therapy: the FRoG platform

Abstract: Radiotherapy with protons and heavier ions landmarks a novel era in the field of high-precision cancer therapy. To identify patients most benefiting from this technologically demanding therapy, fast assessment of comparative treatment plans utilizing different ion species is urgently needed. Moreover, to overcome uncertainties of actual in-vivo physical dose distribution and biological effects elicited by different radiation qualities, development of a reliable high-throughput algorithm is required. To this end, we engineered a unique graphics processing unit (GPU) based software architecture allowing rapid and robust dose calculation. FRoG, Fast Recalculation on GPU, currently operates with four particle beams available at Heidelberg Ion Beam Therapy center, i.e., raster-scanning proton (1H), helium (4He), carbon (12C) and oxygen ions (16O). FRoG enables comparative analysis of different models for estimation of physical and biological effective dose in 3D within minutes and in excellent agreement with the gold standard Monte Carlo (MC) simulation. This is a crucial step towards development of next-generation patient specific radiotherapy.

NanOx, a new model to predict cell survival in the context of particle therapy

Abstract: Particle therapy is increasingly attractive for the treatment of tumors and the number of facilities offering it is rising worldwide. Due to the well-known enhanced effectiveness of ions, it is of utmost importance to plan treatments with great care to ensure tumor killing and healthy tissues sparing. Hence, the accurate quantification of the relative biological effectiveness (RBE) of ions, used in the calculation of the biological dose, is critical. Nevertheless, the RBE is a complex function of many parameters and its determination requires modeling. The approaches currently used have allowed particle therapy to thrive, but still show some shortcomings. We present herein a short description of a new theoretical framework, NanOx, to calculate cell survival in the context of particle therapy. It gathers principles from existing approaches, while addressing some of their weaknesses. NanOx is a multiscale model that takes the stochastic nature of radiation at nanometric and micrometric scales fully into account, integrating also the chemical aspects of radiation-matter interaction. The latter are included in the model by means of a chemical specific energy, determined from the production of reactive chemical species induced by irradiation. Such a production represents the accumulation of oxidative stress and sublethal damage in the cell, potentially generating non-local lethal events in NanOx. The complementary local lethal events occur in a very localized region and can, alone, lead to cell death. Both these classes of events contribute to cell death. The comparison between experimental data and model predictions for the V79 cell line show a good agreement. In particular, the dependence of the typical shoulders of cell survival curves on linear energy transfer are well described, but also the effectiveness of different ions, including the overkill effect. These results required the adjustment of a number of parameters compatible with the application of the model in a clinical scenario thereby showing the potential of NanOx. Said parameters are discussed in detail in this paper.