[en] This work calculates beam quality correction factors () in both modulated and unmodulated proton beams using the Monte Carlo (MC) code . The latest ICRU 90 recommendations on key data for ionizing-radiation dosimetry were adopted to calculate the electronic stopping powers and to select the mean energy to create an ion pair in dry air (). For modulated proton beams, factors were calculated in the middle of a spread-out Bragg peak, while for monoenergetic proton beams they were calculated at the entrance region. Fifteen ionization chambers were simulated. The factors calculated in this work were found to agree within 0.8% or better with the experimental data reported in the literature. For some ionization chambers, the simulation of proton nuclear interactions were found to have an effect on the factors of up to 1%; while for some others, perturbation factors were found to differ from unity by more than 1%. In addition, the combined standard uncertainty in the MC calculated factors in proton beams was estimated to be of the order of 1%. Thus, the results of this work seem to indicate that: (i) the simulation of proton nuclear interactions should be included in the MC calculation of factors in proton beams, (ii) perturbation factors in proton beams should not be neglected, and (iii) the detailed MC simulation of ionization chambers allows for an accurate and precise calculation of factors in clinical proton beams. (paper)

[en] Intensity-modulated proton therapy (IMPT) offers excellent dose conformity and healthy tissue sparing, but it can be substantially compromised in the presence of anatomical changes. A major dosimetric effect is caused by density changes, which alter the planned proton range in the patient. Three different methods, which automatically restore an IMPT plan dose on a daily CT image were implemented and compared: (1) simple dose restoration (DR) using optimization objectives of the initial plan, (2) voxel-wise dose restoration (vDR), and (3) isodose volume dose restoration (iDR). Dose restorations were calculated for three different clinical cases, selected to test different capabilities of the restoration methods: large range adaptation, complex dose distributions and robust re-optimization. All dose restorations were obtained in less than 5 min, without manual adjustments of the optimization settings. The evaluation of initial plans on repeated CTs showed large dose distortions, which were substantially reduced after restoration. In general, all dose restoration methods improved DVH-based scores in propagated target volumes and OARs. Analysis of local dose differences showed that, although all dose restorations performed similarly in high dose regions, iDR restored the initial dose with higher precision and accuracy in the whole patient anatomy. Median dose errors decreased from 13.55 Gy in distorted plan to 9.75 Gy (vDR), 6.2 Gy (DR) and 4.3 Gy (iDR). High quality dose restoration is essential to minimize or eventually by-pass the physician approval of the restored plan, as long as dose stability can be assumed. Motion (as well as setup and range uncertainties) can be taken into account by including robust optimization in the dose restoration. Restoring clinically-approved dose distribution on repeated CTs does not require new ROI segmentation and is compatible with an online adaptive workflow. (paper)

[en] Purpose: To evaluate the impact of intra-fraction motion induced by regular breathing on treatment quality for helical tomotherapy treatments. Material and methods: Four patients treated by simultaneous-integrated boost (SIB) and three by hypo-fractionated stereotactic treatments (hypo-fractionated, 18 Gy/fraction) were included. All patients were coached to ensure regular breathing. For the SIB group, the tumor volume was delineated using CT information only (CTV_CT) and the boost region was based on PET information (GTV_PET, no CTV extension). In the hypo-fractionated group, a GTV based on CT information was contoured. In both groups, ITVs were defined according to 4D data. The PTV included the ITV plus a setup error margin. The treatment was planned using the tomotherapy TPS on 3D CT images. In order to verify the impact of intra-fraction motion and interplay effects, dose calculations were performed using a previously validated Monte Carlo model of tomotherapy (TomoPen): first on the planning 3D CT (“planned dose”) and second, on the 10 phases of the 4D scan. For the latter, two dose distributions, termed “interplay simulated” or “no interplay” were computed with and without beamlet-phase correlation over the 10 phases and combined using deformable dose registration. Results: In all cases, DVHs of “interplay simulated” dose distributions complied within 1% of the original clinical objectives used for planning, defined according to ICRU (report 83) and RTOG (trials 0236 and 0618) recommendations, for SIB and hypo-fractionated groups, respectively. For one patient in the hypo-fractionated group, D_mean to the CTV_CT was 2.6% and 2.5% higher than “planned” for “interplay simulated” and “no interplay”, respectively. Conclusion: For the patients included in this study, assuming regular breathing, the results showed that interplay of breathing and tomotherapy delivery motions did not affect significantly plan delivery accuracy. Hence, accounting for intra-fraction motion through the definition of an ITV volume was sufficient to ensure tumor coverage.

[en] 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. (paper)

[en] In proton therapy, quantification of the proton range uncertainty is important to achieve dose distribution compliance. The promising accuracy of prompt gamma imaging (PGI) suggests the development of a mathematical framework using the range measurements to convert population based estimates of uncertainties into patient specific estimates with the purpose of plan adaptation. We present here such framework using Bayesian inference. The sources of uncertainty were modeled by three parameters: setup bias m , random setup precision r and water equivalent path length bias u. The evolution of the expectation values E (m), E (r) and E (u) during the treatment was simulated. The expectation values converged towards the true simulation parameters after 5 and 10 fractions, for E (m) and E (u), respectively. E (r) settle on a constant value slightly lower than the true value after 10 fractions. In conclusion, the simulation showed that there is enough information in the frequency distribution of the range errors measured by PGI to estimate the expectation values and the confidence interval of the model parameters by Bayesian inference. The updated model parameters were used to compute patient specific lateral and local distal margins for adaptive re-planning. (paper)

[en] The orthovoltage x-ray energy frequently used in radiation research is prone to dosimetry errors due to insufficient backscatter conditions. In many radiobiology studies, especially for cell irradiations, precise dose calculation algorithms such as Convolution-Superposition or Monte Carlo are impractical and as such, less accurate hand calculation methods are used for dose estimation. These dose estimation methods typically assume full backscatter conditions. The purpose of this study is to demonstrate the magnitude of the dose error that results from insufficient backscatter, and to provide lookup tables to account this issue. The beam spectra of several widely used commercial systems (XRAD-225, XRAD-320, SARRP) were used in Monte Carlo (MC) simulations on a series of phantom setups to investigate the impact of varying backscatter conditions on dosimetry. The depth dose curves for different field sizes, water phantom thicknesses and beam qualities were generated. In addition, depth dependent backscatter factors for different field sizes and different beam qualities were calculated. It is demonstrated that as much as a 50% dose difference exists for different backscatter conditions at the beam qualities studied. The choice of cell dish size as well as other changes in the experiment setup can have more than 10% impact on the dose. The impact of backscatter is reduced with a decrease in field size. Further, the thickness needed to provide full backscatter can be approximated as being equal to the field size. It is imperative to ensure full backscatter conditions during system and dosimeter calibration, or to use the look-up table provided in this study. (paper)

[en] Proton radiography imaging was proposed as a promising technique to evaluate internal anatomical changes, to enable pre-treatment patient alignment, and most importantly, to optimize the patient specific CT number to stopping-power ratio conversion. The clinical implementation rate of proton radiography systems is still limited due to their complex bulky design, together with the persistent problem of (in)elastic nuclear interactions and multiple Coulomb scattering (i.e. range mixing). In this work, a compact multi-energy proton radiography system was proposed in combination with an artificial intelligence network architecture (ProtonDSE) to remove the persistent problem of proton scatter in proton radiography. A realistic Monte Carlo model of the Proteus^®One accelerator was built at 200 and 220 MeV to isolate the scattered proton signal in 236 proton radiographies of 80 digital anthropomorphic phantoms. ProtonDSE was trained to predict the proton scatter distribution at two beam energies in a 60%/25%/15% scheme for training, testing, and validation. A calibration procedure was proposed to derive the water equivalent thickness image based on the detector dose response relationship at both beam energies. ProtonDSE network performance was evaluated with quantitative metrics that showed an overall mean absolute percentage error below 1.4% ± 0.4% in our test dataset. For one example patient, detector dose to WET conversions were performed based on the total dose ($I_{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}$), the primary proton dose ($I_{\mathrm{P}\mathrm{r}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{r}\mathrm{y}}$), and the ProtonDSE corrected detector dose ($I_{\mathrm{C}\mathrm{o}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{e}\mathrm{d}}$). The determined WET accuracy was compared with respect to the reference WET by idealistic raytracing in a manually delineated region-of-interest inside the brain. The error was determined 4.3% ± 4.1% for $\mathrm{W}\mathrm{E}\mathrm{T}(I_{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}),$ 2.2% ± 1.4% for $\text{WET}(I_{\mathrm{P}\mathrm{r}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{r}\mathrm{y}}),$ and 2.5% ± 2.0% for $\mathrm{W}\mathrm{E}\mathrm{T}(I_{\mathrm{C}\mathrm{o}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{e}\mathrm{d}}).$ (paper)

[en] Purpose: Using the graphical processing units (GPU) hardware technology, an extremely fast Monte Carlo (MC) code ARCHER_RT is developed for radiation dose calculations in radiation therapy. This paper describes the detailed software development and testing for three clinical TomoTherapy® cases: the prostate, lung, and head and neck. Methods: To obtain clinically relevant dose distributions, phase space files (PSFs) created from optimized radiation therapy treatment plan fluence maps were used as the input to ARCHER_RT. Patient-specific phantoms were constructed from patient CT images. Batch simulations were employed to facilitate the time-consuming task of loading large PSFs, and to improve the estimation of statistical uncertainty. Furthermore, two different Woodcock tracking algorithms were implemented and their relative performance was compared. The dose curves of an Elekta accelerator PSF incident on a homogeneous water phantom were benchmarked against DOSXYZnrc. For each of the treatment cases, dose volume histograms and isodose maps were produced from ARCHER_RT and the general-purpose code, GEANT4. The gamma index analysis was performed to evaluate the similarity of voxel doses obtained from these two codes. The hardware accelerators used in this study are one NVIDIA K20 GPU, one NVIDIA K40 GPU, and six NVIDIA M2090 GPUs. In addition, to make a fairer comparison of the CPU and GPU performance, a multithreaded CPU code was developed using OpenMP and tested on an Intel E5-2620 CPU. Results: For the water phantom, the depth dose curve and dose profiles from ARCHER_RT agree well with DOSXYZnrc. For clinical cases, results from ARCHER_RT are compared with those from GEANT4 and good agreement is observed. Gamma index test is performed for voxels whose dose is greater than 10% of maximum dose. For 2%/2mm criteria, the passing rates for the prostate, lung case, and head and neck cases are 99.7%, 98.5%, and 97.2%, respectively. Due to specific architecture of GPU, modified Woodcock tracking algorithm performed inferior to the original one. ARCHER_RT achieves a fast speed for PSF-based dose calculations. With a single M2090 card, the simulations cost about 60, 50, 80 s for three cases, respectively, with the 1% statistical error in the PTV. Using the latest K40 card, the simulations are 1.7–1.8 times faster. More impressively, six M2090 cards could finish the simulations in 8.9–13.4 s. For comparison, the same simulations on Intel E5-2620 (12 hyperthreading) cost about 500–800 s. Conclusions: ARCHER_RT was developed successfully to perform fast and accurate MC dose calculation for radiotherapy using PSFs and patient CT phantoms

[en] Movement of tumours, mostly by respiration, has been a major problem for treating lung cancer, liver tumours and other locations in the abdomen and thorax. Organ motion is indeed one component of geometrical uncertainties that includes delineation and target definition uncertainties, microscopic disease and setup errors. At present, minimising motion seems to be the easiest to implement in clinical practice. If combined with adaptive approaches to correct for gradual anatomical variations, it may be a practical strategy. Other approaches such as repainting and tracking could increase the accuracy of proton therapy delivery, but advanced 4D solutions are needed. Moreover, there is a need to perform clinical studies to investigate which approach is the best in a given clinical situation. The good news is that existing and emerging technology and treatment planning systems as will without doubt lead in the forthcoming future to practical solutions to tackle intra-fraction motion in proton therapy. These developments may also improve motion management in photon therapy as well

[en] New hybrid radiotherapy treatment systems combining an MRI scanner with a source of ionizing radiation are being introduced in the clinic. The strong magnetic fields of MRI considerably affect radiation dose distributions, especially at tissue-air interfaces due to the electron return effect (ERE). Experimental investigation of the ERE within a sub-millimeter thick surface layer is still highly challenging.

In the present work, we examine and quantify the magnetic field induced perturbations of dose distributions within a 0.5 mm layer surrounding millimeter-size air cavities by applying electron paramagnetic resonance imaging (EPRI).

Air-filled fused quartz tubes (inner diameter 3 or 4 mm) mimic small air cavities and serve as model systems. The tubes were irradiated inside a PMMA phantom by a 6 MV photon beam. The irradiations were performed in the presence or absence of a transverse, magnetic field providing a magnetic field strength of 1.0 Tesla. The spatial distributions of radiation induced paramagnetic defects in the quartz tubes were subsequently determined by applying field-swept echo-detected EPRI and were then converted to relative dose distributions.

The transverse magnetic field leads to considerable local dose enhancements and reductions (up to 35%) with respect to the mean dose within the quartz tubes. The experimentally determined dose distributions are in good quantitative agreement with Monte Carlo radiation transport simulations.

The results of this work demonstrate the feasibility of field-swept echo-detected EPRI to measure magnetic field induced perturbations of dose distributions within a sub-millimeter thick surface layer at the dosimeter-air interface. (paper)