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The major aim of radiation therapy is to provide curative or palliative treatment to cancerous malignancies while minimizing damage to healthy tissues. Charged particle radiotherapy utilizing carbon ions or protons is uniquely suited for this task due to its ability to achieve highly conformal dose distributions around the tumor volume. For these treatment modalities, uncertainties in the localization of patient anatomy due to inter- and intra-fractional motion present a heightened risk of undesired dose delivery. A diverse range of mitigation strategies have been developed and clinically implemented in various disease sites to monitor and correct for patient motion, but much work remains. This review provides an overview of current clinical practices for inter and intra-fractional motion management in charged particle therapy, including motion control, current imaging and motion tracking modalities, as well as treatment planning and delivery techniques. We also cover progress to date on emerging technologies including particle-based radiography imaging, novel treatment delivery methods such as tumor tracking and FLASH, and artificial intelligence and discuss their potential impact towards improving or increasing the challenge of motion mitigation in charged particle therapy.

Pretreatment computed tomography (CT) imaging is an essential component of the particle therapy treatment planning chain. Treatment planning and optimization with charged particles require accurate and precise estimations of ion beam range in tissues, characterized by the stopping power ratio (SPR). Reduction of range uncertainties arising from conventional CT‐number‐to‐SPR conversion based on single‐energy CT (SECT) imaging is of importance for improving clinical practice. Here, the application of a novel imaging and computational methodology using dual‐layer spectral CT (DLCT) was performed toward refining patient‐specific SPR estimates. A workflow for DLCT‐based treatment planning was devised to evaluate SPR prediction for proton, helium, and carbon ion beam therapy planning in the brain. DLCT‐ and SECT‐based SPR predictions were compared in homogeneous and heterogeneous anatomical regions. This study included eight patients scanned for diagnostic purposes with a DLCT scanner. For each patient, four different treatment plans were created, simulating tumors in different parts of the brain. For homogeneous anatomical regions, mean SPR differences of about 1% between the DLCT‐ and SECT‐based approaches were found. In plans of heterogeneous anatomies, relative (absolute) proton range shifts of 0.6% (0.4 mm) in the mean and up to 4.4% (2.1 mm) at the distal fall‐off were observed. In the investigated cohort, 12% of the evaluated organs‐at‐risk (OARs) presented differences in mean or maximum dose of more than 0.5 Gy (RBE) and up to 6.8 Gy (RBE) over the entire treatment. Range shifts and dose differences in OARs between DLCT and SECT in helium and carbon ion treatment plans were similar to protons. In the majority of investigated cases (75th percentile), SECT‐ and DLCT‐based range estimations were within 0.6 mm. Nonetheless, the magnitude of patient‐specific range deviations between SECT and DLCT was clinically relevant in heterogeneous anatomical sites, suggesting further study in larger, more diverse cohorts. Results indicate that patients with brain tumors may benefit from DLCT‐based treatment planning.

The 'clinical target distribution' (CTD) has recently been introduced as a promising alternative to the binary clinical target volume (CTV). However, a comprehensive study that considers the CTD, together with geometric treatment uncertainties, was lacking. Because the CTD is inherently a probabilistic concept, this study proposes a fully probabilistic approach that integrates the CTD directly in a robust treatment planning framework. First, the CTD is derived from a reported microscopic tumor infiltration model such that it explicitly features the probability of tumor cell presence in its target definition. Second, two probabilistic robust optimization methods are proposed that evaluate CTD coverage under uncertainty. The first method minimizes the expected-value (EV) over the uncertainty scenarios and the second method minimizes the sum of the expected value and standard deviation (EV-SD), thereby penalizing the spread of the objectives from the mean. Both EV and EV-SD methods introduce the CTD in the objective function by using weighting factors that represent the probability of tumor presence. The probabilistic methods are compared to a conventional worst-case approach that uses the CTV in a worst-case optimization algorithm. To evaluate the treatment plans, a scenario-based evaluation strategy is implemented that combines the effects of microscopic tumor infiltrations with the other geometric uncertainties. The methods are tested for five lung tumor patients, treated with intensity-modulated proton therapy. The results indicate that for the studied patient cases, the probabilistic methods favor the reduction of the esophagus dose but compensate by increasing the high-dose region in a low conflicting organ such as the lung. These results show that a fully probabilistic approach has the potential to obtain clinical benefits when tumor infiltration uncertainties are taken into account directly in the treatment planning process. ispartof: PHYSICS IN MEDICINE AND BIOLOGY vol:66 issue:15 ispartof: location:England status: published

Background and purpose: A planning target volume (PTV) in photon treatments aims to ensure that the clinical target volume (CTV) receives adequate dose despite treatment uncertainties. The underlying static dose cloud approximation (the assumption that the dose distribution is invariant to errors) is problematic in intensity modulated proton treatments where range errors should be taken into account as well. The purpose of this work is to introduce a robustness evaluation method that is applicable to photon and proton treatments and is consistent with (historic) PTV-based treatment plan evaluations.Materials and methods: The limitation of the static dose cloud approximation was solved in a multi-scenario simulation by explicitly calculating doses for various treatment scenarios that describe possible errors in the treatment course. Setup errors were the same as the CTV-PTV margin and the underlying theory of 3D probability density distributions was extended to 4D to include range errors, maintaining a 90% confidence level. Scenario dose distributions were reduced to voxel-wise minimum and maximum dose distributions; the first to evaluate CTV coverage and the second for hot spots. Acceptance criteria for CTV D98 and D2 were calibrated against PTV-based criteria from historic photon treatment plans.Results: CTV D98 in worst case scenario dose and voxel-wise minimum dose showed a very strong correlation with scenario average D98 (R-2 > 0.99). The voxel-wise minimum dose visualised CTV dose conformity and coverage in 3D in agreement with PTV-based evaluation in photon therapy. Criteria for CTV D98 and D2 of the voxel-wise minimum and maximum dose showed very strong correlations to PTV D98 and D2 (R-2 > 0.99) and on average needed corrections of -0.9% and +2.3%, respectively.Conclusions: A practical approach to robustness evaluation was provided and clinically implemented for PTV-less photon and proton treatment planning, consistent with PTV evaluations but without its static dose cloud approximation. (C) 2019 The Authors. Published by Elsevier B.V.

INTRODUCTION: Proton therapy is very sensitive to treatment uncertainties. These uncertainties can induce proton range variations and may lead to severe dose distortions. However, most commercial tools only offer a limited integration of these uncertainties during treatment planning. In order to verify the robustness of a treatment plan, this study aims at developing a comprehensive Monte Carlo simulation of the treatment delivery, including the simulation of setup and range errors, variation of the breathing motion, and interplay effect. METHOD: Most clinically relevant uncertainties have been modeled and implemented in the fast Monte Carlo dose engine MCsquare. Especially, variation of the breathing motion is taken into account by deforming the initial Four-dimensional computed tomography (4DCT) series and generating multiple new 4DCT series with scaled motion. Systematic and random errors are randomly sampled, following a Monte Carlo approach, to generate individual erroneous treatment scenarios. The robustness of treatment plans is analyzed and reported with dose-volume histogram (DVH) bands. The statistical uncertainty coming from the Monte Carlo scenario sampling is studied. RESULTS: A validation demonstrated the ability of the motion model to generate new 4DCT series with scaled motion amplitude and improved image quality in comparison to the initial 4DCT. The robustness analysis is applied to a lung tumor treatment. Considering the proposed uncertainty model, the simulation of 300 treatment scenarios was necessary to reach an acceptable level of statistical uncertainty on the DVH band. CONCLUSION: A comprehensive and statistically sound method of treatment plan robustness verification is proposed. The uncertainty model presented in this paper is not specific to protons and can also be applied to photon treatments. Moreover, the generated 4DCT series, with scaled motion, can be imported in commercial TPSs. ispartof: MEDICAL PHYSICS vol:46 issue:10 pages:4676-4684 ispartof: location:United States status: published

Proton radiotherapy has a potential to provide an effective cancer treatment while sparing greater volume of healthy tissue than the conventional X-ray based radiotherapy. However, in lungs this potential is hindered by motion due to breathing. An important quantity in treatment verification is the correlation between the respiratory phases (RP) and the timing of pencil beam scanning (PBS). In this note, we demonstrate how the RP can be estimated using Prompt gamma (PG) detection profiles collected during a treatment. We utilized a 4D-CT of a patient with lung cancer, a treatment plan and a PG simulator. The treatment plan consisted of ten layers corresponding to ten proton energies. The RPs of the 4D-CT were interpolated using a deformable registration algorithm, so as to have fifty RPs in total. Deviations from regular breathing were introduced via time dependent frequency modulation. Fifty unique breathing patterns were generated, for which PG profiles were simulated for each pencil beam. Poisson noise was added to each PG profile to account for photon statistics. The RPs were estimated by comparing the PG profiles with and without Poisson noise via three different methods: the RP associated with each layer was estimated 1) independently of the other layers, 2) using a linear correlation between the layers, and 3) using a quadratic correlation between the layers. The best model, the quadratic model, yielded an average error in RP estimation relative to the breathing period of 5% of the breathing period or less with a 90% confidence interval. ispartof: PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS vol:64 pages:33-39 ispartof: location:Italy status: published

Accurate knowledge of the exact stopping location of ions inside the patient would allow full exploitation of their ballistic properties for patient treatment. The localized energy deposition of a pulsed particle beam induces a rapid temperature increase of the irradiated volume and leads to the emission of ionoacoustic (IA) waves. Detecting the time-of-flight (ToF) of the IA wave allows inferring information on the Bragg peak location and can henceforth be used for in-vivo range verification. A challenge for IA is the poor signal-to-noise ratio at clinically relevant doses and viable machines. We present a frequency-based measurement technique, labeled as ionoacoustic tandem phase detection (iTPD) utilizing lock-in amplifiers. The phase shift of the IA signal to a reference signal is measured to derive the ToF. Experimental IA measurements with a 3.5 MHz lead zirconate titanate (PZT) transducer and lock-in amplifiers were performed in water using 22 MeV proton bursts. A digital iTPD was performed in-silico at clinical dose levels on experimental data obtained from a clinical facility and secondly, on simulations emulating a heterogeneous geometry. For the experimental setup using 22 MeV protons, a localization accuracy and precision obtained through iTPD deviates from a time-based reference analysis by less than 15 μm. Several methodological aspects were investigated experimentally in systematic manner. Lastly, iTPD was evaluated in-silico for clinical beam energies indicating that iTPD is in reach of sub-mm accuracy for fractionated doses < 5 Gy. iTPD can be used to accurately measure the ToF of IA signals online via its phase shift in frequency domain. An application of iTPD to the clinical scenario using a single pulsed beam is feasible but requires further development to reach

This study evaluates the suitability of convolutional neural networks (CNNs) to automatically process proton radiography (PR)-based images. CNNs are used to classify PR images impaired by several sources of error affecting the proton range, more precisely setup and calibration curve errors. PR simulations were performed in 40 head and neck cancer patients, at three different anatomical locations (fields A, B and C, centered for head and neck, neck and base of skull coverage). Field sizes were 26 × 26cm2 for field A and 4.5 × 4.5cm2 for fields B and C. Range shift maps were obtained by comparing an unperturbed reference PR against a PR where one or more sources of error affected the proton range. CT calibration curve errors in soft, bone and fat tissues and setup errors in the anterior–posterior and inferior–superior directions were simulated individually and in combination. A CNN was trained for each type of PR field, leading to three CNNs trained with a mixture of range shift maps arising from one or more sources of range error. To test the full/partial/wrong agreement between predicted and actual sources of range error in the range shift maps, exact, partial and wrong match percentages were computed for an independent test dataset containing range shift maps arising from isolated or combined errors, retrospectively. The CNN corresponding to field A showed superior capability to detect isolated and combined errors, with exact matches of 92% and 71% respectively. Field B showed exact matches of 80% and 54%, and field C resulted in exact matches of 77% and 41%. The suitability of CNNs to classify PR-based images containing different sources of error affecting the proton range was demonstrated. This procedure enables the detection of setup and calibration curve errors when they appear individually or in combination, providing valuable information for the interpretation of PR images.

Real-time range verification of particle beams is important for optimal exploitation of the tissue-sparing advantages of particle therapy. Positron Emission Tomography (PET) of the beam-induced positron emitters such as 15O (T1/2 = 122 s) and 11C (T1/2 = 1223 s) has been used for monitoring of therapy in both clinical and preclinical studies. However, the half-lives of these nuclides preclude prompt feedback, i.e., on a sub-second timescale, on dose delivery. The in vivo verification technique relying on the in-beam PET imaging of very short-lived positron emitters such as 12N (T1/2 = 11 ms), recently proposed and investigated in feasibility experiments with a proton beam, provides millimeter precision in range measurement a few tens of milliseconds after the start of an irradiation. With the increasing interest in helium therapy, it becomes relevant to study the feasibility of prompt feedback using PET also for helium beams. A recent study has demonstrated the production of very short-lived nuclides (T1/2 = 10 ms attributed to 12N and/or 13O) during irradiation of water and graphite with helium ions. This work is aimed at investigating the range verification potential of imaging these very short-lived nuclides. PMMA targets were irradiated with a 90 AMeV 4He pencil beam consisting of a series of pulses of 10 ms beam-on and 90 ms beam-off. Two modules of a modified Siemens Biograph mCT PET scanner (21 × 21 cm2), installed 25 cm apart, were used to image the beam-induced PET activity during the beam-off periods. For the irradiation of PMMA, we identify the very short-lived activity earlier observed to be 12N (T1/2 = 11.0 ms). The range precision determined from the 12N activity profile that is measured after just one beam pulse was found to be 9.0 and 4.1 mm (1σ) with 1.3 × 1074He ions per pulse and 6.6 × 1074He ions per pulse, respectively. When considering 4.0 × 1074He ions, which is about the intensity of the most intense distal layer spot in a helium therapy plan, a range verification precision in PMMA of 5.7 mm (1σ) can be realized. The range precision scales approximately with the inverse square root of the number of 4He ions, i.e., the relative statistical accuracy of the number of coincidence events. Thus, when summing data over about 10 distal layer spots, this study shows good prospects for obtaining 1.8 mm (1σ) precision in range verification, within 50 ms after the start of a helium irradiation by in-beam PET imaging (scanner 29% solid angle) of 12N.

PURPOSE: Robust optimization is a computational expensive process resulting in long plan computation times. This issue is especially critical for moving targets as these cases need a large number of uncertainty scenarios to robustly optimize their treatment plans. In this study, we propose a novel worst-case robust optimization algorithm, called dynamic minimax, that accelerates the conventional minimax optimization. Dynamic minimax optimization aims at speeding up the plan optimization process by decreasing the number of evaluated scenarios in the optimization. METHODS: For a given pool of scenarios (e.g., 63 = 7 setup × 3 range × 3 breathing phases), the proposed dynamic minimax algorithm only considers a reduced number of candidate-worst scenarios, selected from the full 63 scenario set. These scenarios are updated throughout the optimization by randomly sampling new scenarios according to a hidden variable P, called the "probability acceptance function," which associates with each scenario the probability of it being selected as the worst case. By doing so, the algorithm favors scenarios that are mostly "active," that is, frequently evaluated as the worst case. Additionally, unconsidered scenarios have the possibility to be re-considered, later on in the optimization, depending on the convergence towards a particular solution. The proposed algorithm was implemented in the open-source robust optimizer MIROpt and tested for six four-dimensional (4D) IMPT lung tumor patients with various tumor sizes and motions. Treatment plans were evaluated by performing comprehensive robustness tests (simulating range errors, systematic setup errors, and breathing motion) using the open-source Monte Carlo dose engine MCsquare. RESULTS: The dynamic minimax algorithm achieved an optimization time gain of 84%, on average. The dynamic minimax optimization results in a significantly noisier optimization process due to the fact that more scenarios are accessed in the optimization. However, the increased noise level does not harm the final quality of the plan. In fact, the plan quality is similar between dynamic and conventional minimax optimization with regard to target coverage and normal tissue sparing: on average, the difference in worst-case D95 is 0.2 Gy and the difference in mean lung dose and mean heart dose is 0.4 and 0.1 Gy, respectively (evaluated in the nominal scenario). CONCLUSIONS: The proposed worst-case 4D robust optimization algorithm achieves a significant optimization time gain of 84%, without compromising target coverage or normal tissue sparing. ispartof: MEDICAL PHYSICS vol:47 issue:7 pages:2746-2754 ispartof: location:United States status: published

Proton therapy is a fast growing treatment modality for cancer and is in selected cases preferred over conventional radiotherapy with photons because of the highly conformal dose distribution that can be achieved with protons due to their steep dose gradients. However, these steep gradients also make proton therapy sensitive to range uncertainties. Proton ranges are calculated from proton stopping powers relative to that in water (Relative Stopping Power, RSP). The RSPs needed for a treatment plan can be estimated from CT (Computed Tomography) data of a patient. High accuracy reference values of RSPs are required to assess the accuracy of these CT based estimates. In this paper we present a water phantom that enables accurate measurement of depth dose profiles in water. Experimental RSPs with a relative standard uncertainty smaller than 0.4% (1 σ ) for samples with a water equivalent thickness of about 2 cm can be derived from the measured depth dose distributions. Most CT based RSP estimates use an approximate RSP model based on the Bethe-Bloch formula without the shell, density, Barkas and Bloch correction. In the Geant4 Monte Carlo code these corrections are included and RSP calculations with this code are expected to be more accurate. In this work, a set of 32 well defined (composition and density), mostly clinically relevant materials is used to assess the correspondence between RSPs that were measured, that were estimated from the approximate RSP model and that were calculated from Monte Carlo simulations. With the measured RSPs we provide a ground-truth bench mark to test the validity of RSPs derived from CT imaging and Monte Carlo simulations.

PURPOSE: In proton therapy planning, the accuracy of the Stopping Power Ratios (SPR) calculated in the stoichiometric CT calibration is affected by, among others, uncertainties on the mean excitation energies (I-values) of human tissues and water. Traditionally, the contribution of these uncertainties on the SPR has been conservatively estimated of the order of 1% or more for a reference tissue of known composition. This study provides a methodology that enables a finer estimation of this uncertainty, eventually showing that the traditional estimates of the uncertainty are too conservative. METHODS: Since human tissues contain water, a correlation exists between the I-values of tissues and water. As the SPR is the ratio of the tissue stopping power to that of water, this correlation decreases the uncertainty of the SPR. Our formalism considers this by expressing the I-value of the tissue as a function of the water weight fraction and the I-value of water, while applying Bragg's additivity rule only to the nonaqueous mixture of the tissue. For 22 reference tissue compositions, the SPR uncertainty was estimated by randomly sampling Gaussian distributions, based on ICRU data, for the I-values of water and the nonaqueous mixture, as well as for the water weight fraction. RESULTS: The relative standard deviation of the SPR, estimated at 150 MeV, is in the range of 0.1%-0.3% for soft tissues with an average water weight percentage of at least 60%. For tissues with a low water content (e.g., adipose and bones), this uncertainty is in the range of 0.5%-0.7%. CONCLUSION: Uncertainties on the I-values of human tissues and water appear to have a significantly lower impact on the SPR uncertainty than traditionally expected. In the future, this may provide a rationale for using smaller distal and proximal margins on the target volume, provided that all other range uncertainty components are correctly estimated too. ispartof: MEDICAL PHYSICS vol:45 issue:7 pages:3361-3370 ispartof: location:United States status: published

Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy. (C) 2020 The Authors. Published by Elsevier B.V.

PURPOSE: Robust optimization is becoming the gold standard for generating robust plans against various kinds of treatment uncertainties. Today, most robust optimization strategies use a pragmatic set of treatment scenarios (the so-called uncertainty set) consisting of combinations of maximum errors, of each considered uncertainty source (such as tumor motion, setup and image-conversion errors). This approach presents two key issues. First, a subset of considered scenarios is unnecessarily improbable which could potentially compromise the plan quality. Second, the resulting large uncertainty set leads to long plan computation times, which limits the potential for robust optimization as a standard clinical tool. In order to address these issues, a method is introduced which is able to preselect a limited set of relevant treatment error scenarios. METHODS: Uncertainties due to systematic setup errors, image-conversion errors and respiratory tumor motion are considered. A four-dimensional (4D)-equiprobability hypersurface is defined, which takes into account the joint probabilities of the above-mentioned uncertainty sources. Only scenarios that lie on the predefined 4D hypersurface are considered, guaranteeing statistical consistency of the uncertainty set. In this regard, twelve scenarios are selected that cover maximum spatial displacements of the tumor during breathing. Subsequently, additional scenarios are considered (sampled from the aforementioned 4D hypersurface) in order to cover any estimated residual range errors. Two different scenario-selection procedures were tested: (a) the maximum displacements (MD) method that only considers twelve scaled maximum displacement scenarios and (b) maximum displacements and residual range (MDR) method which, in addition to the scaled maximum displacement scenarios, considers additional maximum range uncertainty scenarios. The methods were tested for five lung cancer patients by performing comprehensive Monte Carlo robustness evaluations. RESULTS: A plan computation time gain of 78% is achieved by applying the MD method, whilst obtaining a target robustness of D 95 larger than 95% of the prescribed dose, for the worst-case scenario. Additionally, the MD method has the potential to be fully automatic which makes it a promising candidate for fast automatic planning workflows. The MDR method produced plans with excellent target robustness (D 99 larger than 95% of the prescribed dose, even for the worst-case scenario), whilst still obtaining a significant plan computation time gain of 57%. CONCLUSIONS: Two scenario-selection procedures were developed which achieved significant reduction of plan computation time and memory consumption, without compromising plan quality or robustness. ispartof: MEDICAL PHYSICS vol:46 issue:12 pages:5434-5443 ispartof: location:United States status: published

To improve the quality of cancer treatment with protons, a translation of X-ray Computed Tomography (CT) images into a map of the proton stopping powers needs to be more accurate. Proton stopping powers determined from CT images have systematic uncertainties in the calculated proton range in a patient of typically 3-4\% and even up to 10\% in region containing bone~\cite{USchneider1995,USchneider1996,WSchneider2000,GCirrone2007,HPaganetti2012,TPlautz2014,GLandry2013,JSchuemann2014}. As a consequence, part of a tumor may receive no dose, or a very high dose can be delivered in healthy ti\-ssues and organs at risks~(e.g. brain stem)~\cite{ACKnopf2013}. A transmission radiograph of high-energy protons measuring proton stopping powers directly will allow to reduce these uncertainties, and thus improve the quality of treatment. The best way to obtain a sufficiently accurate radiograph is by tracking individual protons traversing the phantom (patient)~\cite{GCirrone2007,TPlautz2014,VSipala2013}. In our simulations we have used an ideal position sensitive detectors measuring a single proton before and after a phantom, while the residual energy of a proton was detected by a BaF$_{2}$ crystal. To obtain transmission radiographs, diffe\-rent phantom materials have been irradiated with a 3x3~cm$^{2}$ scattered proton beam, with various beam energies. The simulations were done using the Geant4 simulation package~\cite{SAgostinelli2003}. In this study we focus on the simulations of the energy loss radiographs for various proton beam energies that are clinically available in proton radiotherapy., Comment: 6 pages, 6 figures, Presented at Jagiellonian Symposium on Fundamental and Applied Subatomic Physics, 7-12 June, 2015, Krak\'ow, Poland

Due to anticipated clinical benefits, moving targets are potential future indications for pencil beam scanned proton therapy (PBS-PT). However, currently they are not widely treated at PBS-PT facilities due to dosimetric uncertainties caused by motion. We developed a method, the 4D robustness evaluation method (4DREM), to realistically and efficiently assess all possible events impacting PBS-PT treatments in the thorax. Using the 4DREM in large cohorts of lung and oesophageal cancer patients, it will become possible to illustrate, in clinical practice, how to trigger robustness settings for plan optimisation and to select and apply motion mitigation techniques. (C) 2019 Elsevier B.V. All rights reserved. Radiotherapy and Oncology

Background The benefits of proton therapy in the treatment of patients with Hodgkin lymphoma (HL) are controversially discussed. Therefore we compared intensitiy modulated proton therapy (IMPT) with intensity modulated radiotherapy (IMRT), in the form of volumetric modulated arc therapy (VMAT) in patients with Hodgkin lymphoma (HL), through a comparative treatment planning study. Methods Radiation plans for 21 patients with Hodgkin Lymphoma (HL) were computed for IMPT and deep inspiration breath hold (DIBH) VMAT. Plans were optimized and computed assuming deep inspiration breath holding conditions. Dosimetric comparison on standard metrics from dose volume histograms was performed to appraise the relative merits of the two techniques, while proton plan robustness was assessed by re-computing the dose distribution of each plan by varying the Hounsfield Units to stopping power calibration by applying a ± 3 and 4% error. Results DIBH-VMAT and IMPT both provided excellent coverage, conformity and heterogeneity of the clinical target volume (CTV) and planning target volume (PTV). IMPT reduced mean doses to the breasts, lungs, heart and normal tissue by 38–83%. IMPT significantly reduced mean doses to the heart to

We present a methodology which can be utilized to select proton or photon radiotherapy in prostate cancer patients. Four state-of-the-art competing treatment modalities were compared (by way of an in silico trial) for a cohort of 25 prostate cancer patients, with and without correction strategies for prostate displacements. Metrics measured from clinical image guidance systems were used. Three correction strategies were investigated; no-correction, extended-no-action-limit, and online-correction. Clinical efficacy was estimated via radiobiological models incorporating robustness (how probable a given treatment plan was delivered) and stability (the consistency between the probable best and worst delivered treatments at the 95% confidence limit). The results obtained at the cohort level enabled the determination of a threshold for likely clinical benefit at the individual level. Depending on the imaging system and correction strategy; 24%, 32% and 44% of patients were identified as suitable candidates for proton therapy. For the constraints of this study: Intensity-modulated proton therapy with online-correction was on average the most effective modality. Irrespective of the imaging system, each treatment modality is similar in terms of robustness, with and without the correction strategies. Conversely, there is substantial variation in stability between the treatment modalities, which is greatly reduced by correction strategies. This study provides a 'proof-of-concept' methodology to enable the prospective identification of individual patients that will most likely (above a certain threshold) benefit from proton therapy.

The relative stopping power (RSP) uncertainty is the largest contributor to the range uncertainty in proton therapy. The purpose of this work was to develop a systematic method that yields accurate and patient-specific RSPs by combining (1) pre-treatment x-ray CT and (2) daily proton radiography of the patient. The method was formulated as a penalized least squares optimization problem (argmin([Formula: see text])). The parameter A represents the cumulative path-length crossed by the proton in each material, separated by thresholding on the HU. The material RSPs (water equivalent thickness/physical thickness) are denoted by x. The parameter b is the list-mode proton radiography produced using Geant4 simulations. The problem was solved using a non-negative linear-solver with [Formula: see text]. A was computed by superposing proton trajectories calculated with a cubic or linear spline approach to the CT. The material's RSP assigned in Geant4 were used for reference while the clinical HU-RSP calibration curve was used for comparison. The Gammex RMI-467 phantom was first investigated. The standard deviation between the estimated material RSP and the calculated RSP is 0.45%. The robustness of the techniques was then assessed as a function of the number of projections and initial proton energy. Optimization with two initial projections yields precise RSP (⩽1.0%) for 330 MeV protons. 250 MeV protons have shown higher uncertainty (⩽2.0%) due to the loss of precision in the path estimate. Anthropomorphic phantoms of the head, pelvis, and lung were subsequently evaluated. Accurate RSP has been obtained for the head ([Formula: see text]), the lung ([Formula: see text]) and the pelvis ([Formula: see text]). The range precision has been optimized using the calibration curves obtained with the algorithm, yielding a mean [Formula: see text] difference to the reference of 0.11 ±0.09%, 0.28 ± 0.34% and [Formula: see text] in the same order. The solution's accuracy is limited by the assumed HU/RSP bijection, neglecting inherent degeneracy. The proposed formulation of the problem with prior knowledge x-ray CT demonstrates potential to increase the accuracy of present RSP estimates.

Proton radiography is a novel imaging modality that allows direct measurement of the proton energy loss in various tissues. Currently, due to the conversion of so-called Hounsfield units from X-ray Computed Tomography (CT) into relative proton stopping powers (RPSP), the uncertainties of RPSP are 3-5% or higher, which need to be minimized down to 1% to make the proton treatment plans more accurate.In this work, we simulated a proton radiography system, with position-sensitive detectors (PSDs) and a residual energy detector (RED). The simulations were built using Geant4, a Monte Carlo simulation toolkit. A phantom, consisting of several materials was placed between the PSDs of various Water Equivalent Thicknesses (WET), corresponding to an ideal detector, a gaseous detector, silicon and plastic scintillator detectors. The energy loss radiograph and the scattering angle distributions of the protons were studied for proton beam energies of 150 MeV, 190 MeV and 230 MeV. To improve the image quality deteriorated by the multiple Coulomb scattering (MCS), protons with small angles were selected. Two ways of calculating a scattering angle were considered using the proton's direction and position.A scattering angle cut of 8.7 mrad was applied giving an optimal balance between quality and efficiency of the radiographic image. For the three proton beam energies, the number of protons used in image reconstruction with the direction method was half the number of protons kept using the position method. (C) 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd.

A pencil beam proton radiography (PR) method, using a commercial multilayer ionization chamber (MLIC) integrated with a treatment planning system (TPS) was developed.A Giraffe (IBA Dosimetry) MLIC (+/- 0.5 mm accuracy) was used to obtain pencil beam PR by delivering spots uniformly positioned at a 5.0 mm distance in a 9 x 9 square of spots. PRs of an electron-density (with tissue-equivalent inserts) phantom and a head phantom were acquired. The integral depth dose (IDD) curves of the delivered spots were computed by the TPS in a volume of water simulating the MLIC, and virtually added to the CT at the exit side of the phantoms. For each spot, measured and calculated IDD were overlapped in order to compute a map of range errors. On the head-phantom, the maximum dose from PR acquisition was estimated. Additionally, on the head phantom the impact on the range errors map was estimated in case of a 1 mm position misalignment.In the electron-density phantom, range errors were within 1 mm in the soft-tissue rods, but greater in the dense-rod. In the head-phantom the range errors were -0.9 +/- 2.7 mm on the whole map and within 1 mm in the brain area. On both phantoms greater errors were observed at inhomogeneity interfaces, due to sensitivity to small misalignment, and inaccurate TPS dose computation. The effect of the 1 mm misalignment was clearly visible on the range error map and produced an increased spread of range errors (-1.0 +/- 3.8 mm on the whole map). The dose to the patient for such PR acquisitions would be acceptable as the maximum dose to the head phantom wasBy the described 2D method, allowing to discriminate misalignments, range verification can be performed in selected areas to implement an in vivo quality assurance program.

Despite extensive research in dual-energy computed tomography (CT), single-energy CT (SECT) is still the standard imaging modality in proton therapy treatment planning and, in this context, the stoichiometric calibration method is considered to be the most accurate to establish a relationship between CT numbers and proton stopping power. This work revisits the SECT calibration for proton therapy treatment planning, with special emphasis on the stoichiometric method. Three different sets of tissue-substitutes of known elemental composition (Gammex, CIRS and Catphan) were scanned with the same scanning protocol. A stoichiometric fit was performed for each set of tissue-substitutes. Based on that, the CT number, relative electron density and relative proton stopping power were calculated for ICRU 46 biological tissues and the different sets of tissue-substitutes. Despite common belief, it was found that the stoichiometric fit depends on the elemental composition of the tissue-substitutes used in the calibration, leading to differences in relative stopping power up to 3.5% for cortical bone. In addition, according to Rutherford et al (1976 Neuroradiology 11 15-21) parametrization of the atomic cross-section, CT numbers of Gammex tissue-substitutes and ICRU 46 biological tissues were found to be similar within the whole energy range relevant to computed tomography. Consequently, it was found that, for Gammex tissue-substitutes, the CT calibration curve resulting from the stoichiometric method agrees with that obtained by simple interpolation of experimental data. In conclusion, the stoichiometric method for SECT calibration seems to depend on the tissue-substitutes used for calibration-which could be regarded as an additional source of uncertainty in proton range for bone tissues. Furthermore, Gammex tissue-substitutes appear to be a good representative of biological tissues within the energy range relevant to computed tomography-making the stoichiometric method unnecessary. ispartof: Physics In Medicine And Biology vol:63 issue:23 ispartof: location:England status: published

The quality of cancer treatment with protons critically depends on an accurate prediction of the proton stopping powers for the tissues traversed by the protons. Today, treatment planning in proton radiotherapy is based on stopping power calculations from densities of X-ray Computed Tomography (CT) images. This causes systematic uncertainties in the calculated proton range in a patient of typically 3-4%, but can become even 10% in bone regions [1-8]. This may lead to no dose in parts of the tumor and too high dose in healthy tissues [9]. A direct measurement of proton stopping powers with high-energy protons will allow reducing these uncertainties and will improve the quality of the treatment.Several studies have shown that a sufficiently accurate radiograph can be obtained by tracking individual protons traversing a phantom (patient) [4, 6, 10]. Our studies benefit from the gas-filled time projection chambers based on GridPix technology [11], developed at Nikhef, capable of tracking a single proton. A BaF2 crystal measuring the residual energy of protons was used. Proton radiographs of phantom consisting of different tissue-like materials were measured with a 30 x 30 mm(2) 150 MeV proton beam. Measurements were simulated with the Geant4 toolkit.First experimental and simulated energy radiographs are in very good agreement [12]. In this paper we focus on simulation studies of the proton scattering angle as it affects the position resolution of the proton energy loss radiograph. By selecting protons with a small scattering angle, the image quality can be improved significantly.

Proton therapy promises higher dose conformality in comparison with regular radiotherapy techniques. Also, image guidance has an increasing role in radiotherapy and MRI is a prime candidate for this imaging. Therefore, in this paper the dosimetric feasibility of Intensity Modulated Proton Therapy (IMPT) in a magnetic field of 1.5 T and the effect on the generated dose distributions compared to those at 0 T is evaluated, using the Monte Carlo software TOol for PArticle Simulation (TOPAS). For three different anatomic sites IMPT plans are generated. It is shown that the generation of an IMPT plan in a magnetic field is feasible, the impact of the magnetic field is small, and the resulting dose distributions are equivalent for 0 T and 1.5 T. Also, the framework of Monte Carlo simulation combined with an inverse optimization method can be used to generate IMPT plans. These plans can be used in future dosimetric comparisons with e.g. IMRT and conventional IMPT. Finally, this study shows that IMPT in a 1.5 T magnetic field is dosimetrically feasible.

Background The benefits of proton therapy in the treatment of patients with Hodgkin lymphoma (HL) are controversially discussed. Therefore we compared intensitiy modulated proton therapy (IMPT) with intensity modulated radiotherapy (IMRT), in the form of volumetric modulated arc therapy (VMAT) in patients with Hodgkin lymphoma (HL), through a comparative treatment planning study. Methods Radiation plans for 21 patients with Hodgkin Lymphoma (HL) were computed for IMPT and deep inspiration breath hold (DIBH) VMAT. Plans were optimized and computed assuming deep inspiration breath holding conditions. Dosimetric comparison on standard metrics from dose volume histograms was performed to appraise the relative merits of the two techniques, while proton plan robustness was assessed by re-computing the dose distribution of each plan by varying the Hounsfield Units to stopping power calibration by applying a ± 3 and 4% error. Results DIBH-VMAT and IMPT both provided excellent coverage, conformity and heterogeneity of the clinical target volume (CTV) and planning target volume (PTV). IMPT reduced mean doses to the breasts, lungs, heart and normal tissue by 38–83%. IMPT significantly reduced mean doses to the heart to