Evaluation of proton and photon dose distributions recalculated on 2D and 3D Unet-generated pseudoCTs from T1-weighted MR head scans.

Introduction: The recent developments of magnetic resonance (MR) based adaptive strategies for photon and, potentially for proton therapy, require a fast and reliable conversion of MR images to X-ray computed tomography (CT) values. CT values are needed for photon and proton dose calculation. The improvement of conversion results employing a 3D deep learning approach is evaluated. Material and methods: A database of 89 T1-weighted MR head scans with about 100 slices each, including rigidly registered CTs, was created. Twenty-eight validation patients were randomly sampled, and four patients were selected for application. The remaining patients were used to train a 2D and a 3D U-shaped convolutional neural network (Unet). A stack size of 32 slices was used for 3D training. For all application cases, volumetric modulated arc therapy photon and single-field uniform dose pencil-beam scanning proton plans at four different gantry angles were optimized for a generic target on the CT and recalculated on 2D and 3D Unet-based pseudoCTs. Mean (absolute) error (MAE/ME) and a gradient sharpness estimate were used to quantify the image quality. Three-dimensional gamma and dose difference analyses were performed for photon (gamma criteria: 1%, 1 mm) and proton dose distributions (gamma criteria: 2%, 2 mm). Range (80% fall off) differences for beam's eye view profiles were evaluated for protons. Results: Training 36 h for 1000 epochs in 3D (6 h for 200 epochs in 2D) yielded a maximum MAE of 147 HU (135 HU) for the application patients. Except for one patient gamma pass rates for photon and proton dose distributions were above 96% for both Unets. Slice discontinuities were reduced for 3D training at the cost of sharpness. Conclusions: Image analysis revealed a slight advantage of 2D Unets compared to 3D Unets. Similar dose calculation performance was reached for the 2D and 3D network.

ScatterNet: A convolutional neural network for cone-beam CT intensity correction.

PURPOSE: To demonstrate a proof-of-concept for fast cone-beam CT (CBCT) intensity correction in projection space by the use of deep learning. METHODS: The CBCT scans and corresponding projections were acquired from 30 prostate cancer patients. Reference shading correction was performed using a validated method (CBCT cor ), which estimates scatter and other low-frequency deviations in the measured CBCT projections on the basis of a prior CT image obtained from warping the planning CT to the CBCT. A convolutional neural network (ScatterNet) was designed, consisting of an attenuation conversion stage followed by a shading correction stage using a UNet-like architecture. The combined network was trained in 2D, utilizing pairs of measured and corrected projections of the reference method, in order to perform shading correction in projection space before reconstruction. The number of patients used for training, testing, and evaluation was 15, 7, and 8, respectively. The reconstructed CBCT ScatterNet was compared to CBCT cor in terms of mean and absolute errors (ME and MAE) for the eight evaluation patients (not included in the network training). Volumetric modulated arc photon therapy (VMAT) and intensity-modulated proton therapy (IMPT) plans were generated on CBCT cor . Dose was recalculated on CBCT ScatterNet to evaluate its dosimetric accuracy. Single-field uniform dose proton plans were utilized for proton range comparison of CBCT ScatterNet and CBCT cor . RESULTS: The CBCT ScatterNet showed no cupping artifacts and a considerably smaller MAE and ME with respect to CBCT cor than the uncorrected CBCT (on average 144 Hounsfield units (HU) vs 46 HU for MAE and 138 HU vs -3 HU for ME). The pass-rates using a 2% dose-difference criterion at 50% dose cut-off, were close to 100% for the VMAT plans of all patients when comparing CBCT ScatterNet to CBCT cor . For IMPT plans pass-rates were clearly lower, ranging from 15% to 81%. Proton range differences of up to 5 mm occurred. CONCLUSIONS: Using a deep convolutional neural network for CBCT intensity correction was shown to be feasible in the pelvic region for the first time. Dose calculation accuracy on CBCT ScatterNet was high for VMAT, but unsatisfactory for IMPT. With respect to the reference technique (CBCT cor ), the neural network enabled a considerable increase in speed for intensity correction and might eventually allow for on-the-fly shading correction during CBCT acquisition.

Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction.

PURPOSE: Photon counting detectors (PCDs) are being introduced in advanced x-ray computed tomography (CT) scanners. From a single PCD-CT acquisition, multiple images can be reconstructed, each based on only a part of the original x-ray spectrum. In this study, we investigated whether PCD-CT can be used to estimate stopping power ratios (SPRs) for proton therapy treatment planning, both by comparing to other SPR methods proposed for single energy CT (SECT) and dual energy CT (DECT) as well as to experimental measurements. METHODS: A previously developed DECT-based SPR estimation method was adapted to PCD-CT data, by adjusting the estimation equations to allow for more energy spectra. The method was calibrated directly on noisy data to increase the robustness toward image noise. The new PCD SPR estimation method was tested in theoretical calculations as well as in an experimental setup, using both four and two energy bin PCD-CT images, and through comparison to two other SPR methods proposed for SECT and DECT. These two methods were also evaluated on PCD-CT images, full spectrum (one-bin) or two-bin images, respectively. In a theoretical framework, we evaluated the effect of patient-specific tissue variations (density and elemental composition) and image noise on the SPR accuracy; the latter effect was assessed by applying three different noise levels (low, medium, and high noise). SPR estimates derived using real PCD-CT images were compared to experimentally measured SPRs in nine organic tissue samples, including fat, muscle, and bone tissues. RESULTS: For the theoretical calculations, the root-mean-square error (RMSE) of the SPR estimation was 0.1% for the new PCD method using both two and four energy bins, compared to 0.2% and 0.7% for the DECT- and SECT-based method, respectively. The PCD method was found to be very robust toward CT image noise, with a RMSE of 2.7% when high noise was added to the CT numbers. Introducing tissue variations, the RMSE only increased to 0.5%; even when adding high image noise to the changed tissues, the RMSE stayed within 3.1%. In the experimental measurements, the RMSE over the nine tissue samples was 0.8% when using two energy bins, and 1.0% for the four-bin images. CONCLUSIONS: In all tested cases, the new PCD method produced similar or better results than the SECT- and DECT-based methods, showing an overall improvement of the SPR accuracy. This study thus demonstrated that PCD-CT scans will be a qualified candidate for SPR estimations.

Fast 4D cone-beam CT from 60 s acquisitions.

BACKGROUND & PURPOSE: Four dimensional Cone beam CT (CBCT) has many potential benefits for radiotherapy but suffers from poor image quality, long acquisition times, and/or long reconstruction times. In this work we present a fast iterative reconstruction algorithm for 4D reconstruction of fast acquisition cone beam CT, as well as a new method for temporal regularization and compare to state of the art methods for 4D CBCT. MATERIALS & METHODS: Regularization parameters for the iterative algorithms were found automatically via computer optimization on 60 s acquisitions using the XCAT phantom. Nineteen lung cancer patients were scanned with 60 s arcs using the onboard image on a Varian trilogy linear accelerator. Images were reconstructed using an accelerated ordered subset algorithm. A frequency based temporal regularization algorithm was developed and compared to the McKinnon-Bates algorithm, 4D total variation and prior images compressed sensing (PICCS). RESULTS: All reconstructions were completed in 60 s or less. The proposed method provided a structural similarity of 0.915, compared with 0.786 for the classic McKinnon-bates method. For the patient study, it provided fewer image artefacts than PICCS, and better spatial resolution than 4D TV. CONCLUSION: Four dimensional iterative CBCT reconstruction was done in less than 60 s, demonstrating the clinical feasibility. The frequency based method outperformed 4D total variation and PICCS on the simulated data, and for patients allowed for tumor location based on 60 s acquisitions, even for slowly breathing patients. It should thus be suitable for routine clinical use.

Inter-centre variability of CT-based stopping-power prediction in particle therapy: Survey-based evaluation.

BACKGROUND AND PURPOSE: Stopping-power ratios (SPRs) are used in particle therapy to calculate particle range in patients. The heuristic CT-to-SPR conversion (Hounsfield Look-Up-Table, HLUT), needed for treatment planning, depends on CT-scan and reconstruction parameters as well as the specific HLUT definition. To assess inter-centre differences in these parameters, we performed a survey-based qualitative evaluation, as a first step towards better standardisation of CT-based SPR derivation. MATERIALS AND METHODS: A questionnaire was sent to twelve particle therapy centres (ten from Europe and two from USA). It asked for details on CT scanners, image acquisition and reconstruction, definition of the HLUT, body-region specific HLUT selection, investigations of beam-hardening and experimental validations of the HLUT. Technological improvements were rated regarding their potential to improve SPR accuracy. RESULTS: Scan parameters and HLUT definition varied widely. Either the stoichiometric method (eight centres) or a tissue-substitute-only HLUT definition (three centres) was used. One centre combined both methods. The number of HLUT line segments varied widely between two and eleven. Nine centres had investigated influence of beam-hardening, often including patient-size dependence. Ten centres had validated their HLUT experimentally, with very different validation schemes. Most centres deemed dual-energy CT promising for improving SPR accuracy. CONCLUSIONS: Large inter-centre variability was found in implementation of CT scans, image reconstruction and especially in specification of the CT-to-SPR conversion. A future standardisation would reduce time-intensive institution-specific efforts and variations in treatment quality. Due to the interdependency of multiple parameters, no conclusion can be drawn on the derived SPR accuracy and its inter-centre variability.

Validation of proton stopping power ratio estimation based on dual energy CT using fresh tissue samples.

Dual energy CT (DECT) has been shown, in theoretical and phantom studies, to improve the stopping power ratio (SPR) determination used for proton treatment planning compared to the use of single energy CT (SECT). However, it has not been shown that this also extends to organic tissues. The purpose of this study was therefore to investigate the accuracy of SPR estimation for fresh pork and beef tissue samples used as surrogates of human tissues. The reference SPRs for fourteen tissue samples, which included fat, muscle and femur bone, were measured using proton pencil beams. The tissue samples were subsequently CT scanned using four different scanners with different dual energy acquisition modes, giving in total six DECT-based SPR estimations for each sample. The SPR was estimated using a proprietary algorithm (syngo.via DE Rho/Z Maps, Siemens Healthcare, Forchheim, Germany) for extracting the electron density and the effective atomic number. SECT images were also acquired and SECT-based SPR estimations were performed using a clinical Hounsfield look-up table. The mean and standard deviation of the SPR over large volume-of-interests were calculated. For the six different DECT acquisition methods, the root-mean-square errors (RMSEs) for the SPR estimates over all tissue samples were between 0.9% and 1.5%. For the SECT-based SPR estimation the RMSE was 2.8%. For one DECT acquisition method, a positive bias was seen in the SPR estimates, having a mean error of 1.3%. The largest errors were found in the very dense cortical bone from a beef femur. This study confirms the advantages of DECT-based SPR estimation although good results were also obtained using SECT for most tissues.

Pre-treatment patient-specific stopping power by combining list-mode proton radiography and x-ray CT.

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.

ISMRM Raw data format: A proposed standard for MRI raw datasets.

PURPOSE: This work proposes the ISMRM Raw Data format as a common MR raw data format, which promotes algorithm and data sharing. METHODS: A file format consisting of a flexible header and tagged frames of k-space data was designed. Application Programming Interfaces were implemented in C/C++, MATLAB, and Python. Converters for Bruker, General Electric, Philips, and Siemens proprietary file formats were implemented in C++. Raw data were collected using magnetic resonance imaging scanners from four vendors, converted to ISMRM Raw Data format, and reconstructed using software implemented in three programming languages (C++, MATLAB, Python). RESULTS: Images were obtained by reconstructing the raw data from all vendors. The source code, raw data, and images comprising this work are shared online, serving as an example of an image reconstruction project following a paradigm of reproducible research. CONCLUSION: The proposed raw data format solves a practical problem for the magnetic resonance imaging community. It may serve as a foundation for reproducible research and collaborations. The ISMRM Raw Data format is a completely open and community-driven format, and the scientific community is invited (including commercial vendors) to participate either as users or developers. Magn Reson Med 77:411-421, 2017. © 2016 Wiley Periodicals, Inc.

A robust empirical parametrization of proton stopping power using dual energy CT.

PURPOSE: In this study the authors present a new method for estimation of proton stopping power ratios (SPRs) using dual energy CT (DECT), which is robust toward CT noise. The authors propose a parametrization for SPR based directly on the CT numbers in a DECT image set, whereby the intermediate steps of estimating the relative electron density, ρe, and mean excitation energy, I, are avoided. METHODS: The SPR parametrization proposed in this study is a purely empirical fit based on the theoretical SPR values for a list of 34 reference human tissues. To investigate the SPR estimation made with this new method the authors performed a calibration and an evaluation with the method. The authors initially calculated CT numbers using CT energy spectrum characterization parameters obtained from calibration based on a Gammex 467 electron density calibration phantom. These CT numbers were fitted to the theoretical SPR for the reference human tissues using the new SPR parametrization presented in this study. The method was evaluated based on theoretical CT numbers for the reference human tissues. The root-mean-square error (RMSE) of the SPR and the proton range error from the continuous slowing down approximation were calculated for the reference human tissues. To test the stability of the parametrization the authors varied the density and elemental composition of the reference human tissues and calculated their new SPR estimates. Further, clinically realistic noise values were added to the theoretical CT numbers to investigate how CT noise affected the estimated water equivalent range through 10 cm of the reference human tissues. All results for the new SPR parametrization were compared to the results obtained using two previously published DECT methods for SPR estimation. Comparisons were also made to a single energy CT (SECT) SPR estimation method, the stoichiometric method, which is commonly used in clinical practise for proton therapy treatment planning. RESULTS: The RMSE for the SPR of the 34 reference human tissues using the new SPR parametrization was 0.12%, compared to 0.19% and 0.28% for the two previously published DECT methods. The SPR parametrization was more stable toward variations of the calcium content in the reference human tissues, but less stable toward density variations and changes to the hydrogen content than the two other DECT methods. When adding noise to the theoretical CT numbers the SPR parametrization gave the lowest water equivalent range errors of all four tested SPR estimation methods (maximum error reduced to 0.4 mm). In all cases tested, the new SPR parametrization outperformed the SECT stoichiometric method. CONCLUSIONS: The new SPR parametrization gave lower RMSEs than the two other published DECT methods, and was in particular more robust against added noise. The method has potential for reducing range uncertainty margins in treatment planning of proton therapy.

Fast reconstruction of low dose proton CT by sinogram interpolation.

Proton computed tomography (CT) has been demonstrated as a promising image modality in particle therapy planning. It can reduce errors in particle range calculations and consequently improve dose calculations. Obtaining a high imaging resolution has traditionally required computationally expensive iterative reconstruction techniques to account for the multiple scattering of the protons. Recently, techniques for direct reconstruction have been developed, but these require a higher imaging dose than the iterative methods. No previous work has compared the image quality of the direct and the iterative methods. In this article, we extend the methodology for direct reconstruction to be applicable for low imaging doses and compare the obtained results with three state-of-the-art iterative algorithms. We find that the direct method yields comparable resolution and image quality to the iterative methods, even at 1 mSv dose levels, while yielding a twentyfold speedup in reconstruction time over previously published iterative algorithms.

A simulation study on proton computed tomography (CT) stopping power accuracy using dual energy CT scans as benchmark.

BACKGROUND: Accurate stopping power estimation is crucial for treatment planning in proton therapy, and the uncertainties in stopping power are currently the largest contributor to the employed dose margins. Dual energy x-ray computed tomography (CT) (clinically available) and proton CT (in development) have both been proposed as methods for obtaining patient stopping power maps. The purpose of this work was to assess the accuracy of proton CT using dual energy CT scans of phantoms to establish reference accuracy levels. MATERIAL AND METHODS: A CT calibration phantom and an abdomen cross section phantom containing inserts were scanned with dual energy and single energy CT with a state-of-the-art dual energy CT scanner. Proton CT scans were simulated using Monte Carlo methods. The simulations followed the setup used in current prototype proton CT scanners and included realistic modeling of detectors and the corresponding noise characteristics. Stopping power maps were calculated for all three scans, and compared with the ground truth stopping power from the phantoms. RESULTS: Proton CT gave slightly better stopping power estimates than the dual energy CT method, with root mean square errors of 0.2% and 0.5% (for each phantom) compared to 0.5% and 0.9%. Single energy CT root mean square errors were 2.7% and 1.6%. Maximal errors for proton, dual energy and single energy CT were 0.51%, 1.7% and 7.4%, respectively. CONCLUSION: Better stopping power estimates could significantly reduce the range errors in proton therapy, but requires a large improvement in current methods which may be achievable with proton CT.

The image quality of ion computed tomography at clinical imaging dose levels.

PURPOSE: Accurately predicting the range of radiotherapy ions in vivo is important for the precise delivery of dose in particle therapy. Range uncertainty is currently the single largest contribution to the dose margins used in planning and leads to a higher dose to normal tissue. The use of ion CT has been proposed as a method to improve the range uncertainty and thereby reduce dose to normal tissue of the patient. A wide variety of ions have been proposed and studied for this purpose, but no studies evaluate the image quality obtained with different ions in a consistent manner. However, imaging doses ion CT is a concern which may limit the obtainable image quality. In addition, the imaging doses reported have not been directly comparable with x-ray CT doses due to the different biological impacts of ion radiation. The purpose of this work is to develop a robust methodology for comparing the image quality of ion CT with respect to particle therapy, taking into account different reconstruction methods and ion species. METHODS: A comparison of different ions and energies was made. Ion CT projections were simulated for five different scenarios: Protons at 230 and 330 MeV, helium ions at 230 MeV/u, and carbon ions at 430 MeV/u. Maps of the water equivalent stopping power were reconstructed using a weighted least squares method. The dose was evaluated via a quality factor weighted CT dose index called the CT dose equivalent index (CTDEI). Spatial resolution was measured by the modulation transfer function. This was done by a noise-robust fit to the edge spread function. Second, the image quality as a function of the number of scanning angles was evaluated for protons at 230 MeV. In the resolution study, the CTDEI was fixed to 10 mSv, similar to a typical x-ray CT scan. Finally, scans at a range of CTDEI's were done, to evaluate dose influence on reconstruction error. RESULTS: All ions yielded accurate stopping power estimates, none of which were statistically different from the ground truth image. Resolution (as defined by the modulation transfer function = 10% point) was the best for the helium ions (18.21 line pairs/cm) and worst for the lower energy protons (9.37 line pairs/cm). The weighted quality factor for the different ions ranged from 1.23 for helium to 2.35 for carbon ions. For the angle study, a sharp increase in absolute error was observed below 45 distinct angles, giving the impression of a threshold, rather than smooth, limit to the number of angles. CONCLUSIONS: The method presented for comparing various ion CT modalities is feasible for practical use. While all studied ions would improve upon x-ray CT for particle range estimation, helium appears to give the best results and deserves further study for imaging.

Improved proton computed tomography by dual modality image reconstruction.

PURPOSE: Proton computed tomography (CT) is a promising image modality for improving the stopping power estimates and dose calculations for particle therapy. However, the finite range of about 33 cm of water of most commercial proton therapy systems limits the sites that can be scanned from a full 360° rotation. In this paper the authors propose a method to overcome the problem using a dual modality reconstruction (DMR) combining the proton data with a cone-beam x-ray prior. METHODS: A Catphan 600 phantom was scanned using a cone beam x-ray CT scanner. A digital replica of the phantom was created in the Monte Carlo code Geant4 and a 360° proton CT scan was simulated, storing the entrance and exit position and momentum vector of every proton. Proton CT images were reconstructed using a varying number of angles from the scan. The proton CT images were reconstructed using a constrained nonlinear conjugate gradient algorithm, minimizing total variation and the x-ray CT prior while remaining consistent with the proton projection data. The proton histories were reconstructed along curved cubic-spline paths. RESULTS: The spatial resolution of the cone beam CT prior was retained for the fully sampled case and the 90° interval case, with the MTF = 0.5 (modulation transfer function) ranging from 5.22 to 5.65 linepairs/cm. In the 45° interval case, the MTF = 0.5 dropped to 3.91 linepairs/cm For the fully sampled DMR, the maximal root mean square (RMS) error was 0.006 in units of relative stopping power. For the limited angle cases the maximal RMS error was 0.18, an almost five-fold improvement over the cone beam CT estimate. CONCLUSIONS: Dual modality reconstruction yields the high spatial resolution of cone beam x-ray CT while maintaining the improved stopping power estimation of proton CT. In the case of limited angles, the use of prior image proton CT greatly improves the resolution and stopping power estimate, but does not fully achieve the quality of a 360° proton CT scan.

Voxelwise comparison of perfusion parameters estimated using dynamic contrast enhanced (DCE) computed tomography and DCE-magnetic resonance imaging in locally advanced cervical cancer.

PURPOSE: Dynamic contrast enhanced (DCE) imaging has gained interest as an imaging modality for assessment of tumor characteristics and response to cancer treatment. However, for DCE-magnetic resonance imaging (MRI) tissue contrast enhancement may vary depending on imaging sequence and temporal resolution. The aim of this study is to compare DCE-MRI to DCE-computed tomography (DCE-CT) as the gold standard. MATERIAL AND METHODS: Thirteen patients with advanced cervical cancer were scanned once prior to chemo-radiation and during chemo-radiation with DCE-CT and -MRI in immediate succession. A total of 22 paired DCE-CT and -MRI scans were acquired for comparison. Kinetic modeling using the extended Tofts model was applied to both image series. Furthermore the similarity of the spatial distribution was evaluated using a Γ analysis. The correlation between the two imaging techniques was evaluated using Pearson's correlation and the parameter means were compared using a Student's t-test (p < 0.05). RESULTS: A significant positive correlation between DCE-CT and -MRI was found for all kinetic parameters. The results showing the best correlation with the DCE-CT-derived parameters were obtained using a population-based input function for MRI. The median Pearson's correlations were: volume transfer constant K(trans) (r = 0.9), flux rate constant kep (r = 0.77), extracellular volume fraction ve (r = 0.58) and blood plasma volume fraction vp (r = 0.83). All quantitative parameters were found to be significantly different as estimated by DCE-CT and -MRI. The Γ analysis in normalized maps revealed that 45% of the voxels failed to find a voxel with the corresponding value allowing for an uncertainty of 3 mm in position and 3% in value (Γ3,3). By reducing the criteria, the Γ-failure rates were: Γ3,5 (37% failure), Γ3,10 (26% failure) and at Γ3,15 (19% failure). CONCLUSION: Good to excellent correlations but significant bias was found between DCE-CT and -MRI. Both the Pearson's correlation and the Γ analysis proved that the spatial information was similar when analyzing the two sets of DCE data using the extended Tofts model. Improvement of input function sampling is needed to improve kinetic quantification using DCE-MRI.

The impact of modeling nuclear fragmentation on delivered dose and radiobiology in ion therapy.

The importance of nuclear interactions for ion therapy arises from the influence of the particle spectrum on, first, radiobiology and therefore also on treatment planning, second, the accuracy of measuring dose and, third, the delivered dose distribution. This study tries to determine the qualitative as well as the quantitative influence of the modeling of inelastic nuclear interactions on ion therapy. Thereby, three key disciplines are investigated, namely dose delivery, dose assessment and radiobiology. In order to perform a quantitative analysis, a relative comparison between six different descriptions of nuclear interactions is carried out for carbon ions. The particle transport is simulated with the Monte Carlo code SHIELD-HIT10A while dose planning and radiobiology are covered by the analytic treatment planning program for particles TRiP, which determines the relative biological effectiveness (RBE) with the local effect model. The obtained results show that the physical dose distribution can in principle be significantly influenced by the modeling of fragmentation (about 10% for a 20% change in all inelastic nuclear cross sections for a target volume ranging from 15 to 25 cm). While the impact of nuclear fragmentation on stopping power ratios can be neglected, the fluence correction factor may be influenced by the applied nuclear models. In contrast to the results for the physical dose, the variation of the RBE is only small (about 1% for a 20% change in all inelastic nuclear cross sections) suggesting a relatively weak dependence of radiobiology on the detailed composition of the particle energy spectrum of the mixed radiation field. Also, no significant change (about 0.2 mm) of the lateral penumbra of the RBE-weighted dose is observed.

Optimizing SHIELD-HIT for carbon ion treatment.

The SHIELD-HIT Monte Carlo transport code has been widely used in particle therapy, but has previously shown some discrepancies, when compared with experimental data. In this work, the inelastic nuclear cross sections of SHIELD-HIT are calibrated to experimental data for carbon ions. In addition, the models for nuclear fragmentation were adjusted to experiments, for the partial charge-changing cross section of carbon ions in water. Comparison with fragmentation yield experiments for carbon and neon primaries were made for validation. For carbon primaries, excellent agreement between simulation and experiment was observed, with only minor discrepancies. For neon primaries, the agreement was also good, but larger discrepancies were observed, which require further investigation. In conclusion, the current version SHIELD-HIT10A is well suited for simulating problems arising in particle therapy for clinical ion beams.

Stopping power for particle therapy: the generic library libdEdx and clinically relevant stopping-power ratios for light ions.

PURPOSE: Stopping-power data enter at a number of different places in particle therapy and their uncertainties have a direct impact on the accuracy of the therapy, e.g., in treatment planning. Furthermore, for clinical quality assurance, the particle beam stopping-power ratios (STPR) have to be known accurately for dosimetry. METHODS: An open-source computer library called libdEdx (library for energy loss per unit path length, dE/dx, calculations) is developed, providing stopping-power data from data tables and computer programs as well as a stopping-power formula comprising a large list of target materials. Calculations of STPR in the case of spread-out Bragg-peaks (SOBP) are performed with the Monte Carlo transportation code SHIELD-HIT (SHIELD-Heavy Ion Transport) using different ions relevant for particle therapy. RESULTS: For SOBP the water-to-air STPR depends on the residual range and is qualitatively very similar for different ions; however, small quantitative differences exist between the considered ion species. CONCLUSIONS: libdEdx allows for a convenient and efficient treatment of stopping powers in numerical applications. It can be applied to estimate the dependence on the accuracy of the stopping power and to provide data for an extended number of target materials. The STPR for SOBP for different ions are found to be qualitatively the same which may allow for an analytical description valid for all ions.

Recent improvements in the SHIELD-HIT code.

PURPOSE: The SHIELD-HIT Monte Carlo particle transport code has previously been used to study a wide range of problems for heavy-ion treatment and has been benchmarked extensively against other Monte Carlo codes and experimental data. Here, an improved version of SHIELD-HIT is developed concentrating on three objectives, namely: Enhanced functionality, improved efficiency, and a modification of employed physical models. METHODOLOGICAL DEVELOPMENTS: SHIELD-HIT (currently at version '10A') is now equipped with an independent detector geometry, ripple filter implementations, and it is capable of using accelerator control files as a basis for the primaries. Furthermore, the code has been parallelized and efficiency is improved. The physical description of inelastic ion collisions has been modified. RESULTS: The simulation of an experimental depth-dose distribution including a ripple filter reproduces experimental measurements with high accuracy. CONCLUSIONS: SHIELD-HIT is now faster, more user-friendly and accurate, and has an enhanced functionality with some features being currently unique to SHIELD-HIT. The possibility of data file exchange with existing treatment planning software for heavy-ion therapy allows for benchmarking under treatment conditions as well as extending the capabilities of treatment planning software.

Fluence correction factors and stopping power ratios for clinical ion beams.

BACKGROUND: In radiation therapy, the principal dosimetric quantity of interest is the absorbed dose to water. Therefore, a dose conversion to dose to water is required for dose deposited by ion beams in other media. This is in particular necessary for dose measurements in plastic phantoms for increased positioning accuracy, graphite calorimetry being developed as a primary standard for dose to water dosimetry, but also for the comparison of dose distributions from Monte Carlo simulations with those of pencil beam algorithms. MATERIAL AND METHODS: In the conversion of absorbed dose to phantom material to absorbed dose to water the water-to-material stopping power ratios (STPR) and the fluence correction factors (FCF) for the full charged particle spectra are needed. We determined STPR as well as FCF for water to graphite, bone (compact), and PMMA as a function of water equivalent depth, z(w), with the Monte Carlo code SHIELD-HIT10A. Simulations considering all secondary ions were performed for primary protons as well as carbon, nitrogen and oxygen ions with a total range of 3 cm, 14.5 cm and 27 cm as well as for two spread-out Bragg-peaks (SOBP). STPR as a function of depth are also compared to a recently proposed analytical formula. RESULTS: The STPR are of the order of 1.022, 1.070, and 1.112 for PMMA, bone, and graphite, respectively. STPR vary only little with depth except close to the total range of the ion and they can be accurately approximated with an analytical formula. The amplitude of the FCF depends on the non-elastic nuclear interactions and it is unity if these interactions are turned off in the simulation. Fluence corrections are of the order of a percent becoming more pronounced for larger depths resulting in dose difference of the order of 5% around 25 cm. The same order of magnitude is observed for SOBP. CONCLUSIONS: We conclude that for ions with small total range (z(w-eq) ≤3 cm) dosimetry without applying FCF could in principle be performed in phantoms of materials other than water without a significant loss of accuracy. However, in clinical high-energy ion beams with penetration depths z(w-eq) ≥3 cm, where accurate positioning in water is not an issue, absorbed dose measurements should be directly performed in water or accurate values of FCF need to be established.