We present commissioning and validation of Fred, a graphical processing unit (GPU)–accelerated Monte Carlo code, for two proton beam therapy facilities of different beam line design: CCB (Krakow, IBA) and EMORY (Atlanta, Varian). We followed clinical acceptance tests required to approve the certified treatment planning system for clinical use. We implemented an automated and efficient procedure to build a parameter library characterizing the clinical proton pencil beam. Beam energy, energy spread, lateral propagation model, and a dosimetric calibration factor were parametrized based on measurements performed during the facility start-up. The Fred beam model was validated against commissioning and supplementary measurements performed with and without range shifter. We obtained 1) submillimeter agreement of Bragg peak shapes in water and lateral beam profiles in air and slab phantoms, 2) <2% dose agreement for spread out Bragg peaks of different ranges, 3) average gamma index (2%/2 mm) passing rate of >95% for >1000 patient verification measurements using a two-dimensional array of ionization chambers, and 4) gamma index passing rate of >99% for three-dimensional dose distributions computed with Fred and measured with an array of ionization chambers behind an anthropomorphic phantom. The results of example treatment planning study on >100 patients demonstrated that Fred simulations in computed tomography enable an accurate prediction of dose distribution in patient and application of Fred as second patient quality assurance tool. Computation of a patient treatment in a CT using 10^4 protons per pencil beam took on average 2′30 min with a tracking rate of 2.9×10^5 p+/s. Fred was successfully commissioned and validated against the clinical beam model, showing that it could potentially be used in clinical routine. Thanks to high computational performance due to GPU acceleration and an automated beam model implementation method, the application of Fred is now possible for research or quality assurance purposes in most of the proton facilities.
Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calculation. The aim of this study is to develop a physics-informed deep learning (PIDL) framework to derive accurate mass density and relative stopping power maps from dual-energy computed tomography (DECT) images. The PIDL framework allows deep learning (DL) models to be trained with a physics loss function, which includes a physics model to constrain DL models. Five DL models were implemented including a fully connected neural network (FCNN), dual-FCNN (DFCNN), and three variants of residual networks (ResNet): ResNet-v1 (RN-v1), ResNet-v2 (RN-v2), and dual-ResNet-v2 (DRN-v2). An artificial neural network (ANN) and the five DL models trained with and without physics loss were explored to evaluate the PIDL framework. Two empirical DECT models were implemented to compare with the PIDL method. DL training data were from CIRS electron density phantom 062M (Computerized Imaging Reference Systems, Inc., Norfolk, VA). The performance of DL models was tested by CIRS adult male, adult female, and 5-year-old child anthropomorphic phantoms. For density map inference, the physics-informed RN-v2 was 3.3%, 2.9% and 1.9% more accurate than ANN for the adult male, adult female, and child phantoms. The physics-informed DRN-v2 was 0.7%, 0.6%, and 0.8% more accurate than DRN-v2 without physics training for the three phantoms, respectfully. The results indicated that physics-informed training could reduce uncertainty when ANN/DL models without physics training were insufficient to capture data structures or derived significant errors. DL models could also achieve better image noise control compared to the empirical DECT parametric mapping methods. The proposed PIDL framework can potentially improve proton range uncertainty by offering accurate material properties conversion from DECT.
Magnetic resonance imaging (MRI) has been widely used in combination with computed tomography (CT) radiation therapy because MRI improves the accuracy and reliability of target delineation due to its superior soft tissue contrast over CT. The MRI-only treatment process is currently an active field of research since it could eliminate systematic MR-CT co-registration errors, reduce medical cost, avoid diagnostic radiation exposure, and simplify clinical workflow. The purpose of this work is to validate the application of a deep learning-based method for abdominal synthetic CT (sCT) generation by image evaluation and dosimetric assessment in a commercial proton pencil beam treatment planning system (TPS). This study proposes to integrate dense block into a 3D cycle-consistent generative adversarial networks (cycle GAN) framework in an effort to effectively learn the nonlinear mapping between MRI and CT pairs. A cohort of 21 patients with co-registered CT and MR pairs were used to test the deep learning-based sCT image quality by leave-one-out cross validation. The CT image quality, dosimetric accuracy and the distal range fidelity were rigorously checked, using side-by-side comparison against the corresponding original CT images. The average mean absolute error (MAE) was 72.87±18.16 HU. The relative differences of the statistics of the PTV dose volume histogram (DVH) metrics between sCT and CT were generally less than 1%. Mean 3D gamma analysis passing rate of 1mm/1%, 2mm/2%, 3mm/3% criteria with 10% dose threshold were 90.76±5.94%, 96.98±2.93% and 99.37±0.99%, respectively. The median, mean and standard deviation of absolute maximum range differences were 0.170 cm, 0.186 cm and 0.155 cm. The image similarity, dosimetric and distal range agreement between sCT and original CT suggests the feasibility of further development of an MRI-only workflow for liver proton radiotherapy.
Monte Carlo (MC)-based dose calculations are generally superior to analytical dose calculations (ADC) in modeling the dose distribution for proton pencil beam scanning (PBS) treatments. The purpose of this paper is to present a methodology for commissioning and validating an accurate MC code for PBS utilizing a parameterized source model, including an implementation of a range shifter, that can independently check the ADC in commercial treatment planning system (TPS) and fast Monte Carlo dose calculation in opensource platform (MCsquare). The source model parameters (including beam size, angular divergence and energy spread) and protons per MU were extracted and tuned at the nozzle exit by comparing Tool for Particle Simulation (TOPAS) simulations with a series of commissioning measurements using scintillation screen/CCD camera detector and ionization chambers. The range shifter was simulated as an independent object with geometric and material information. The MC calculation platform was validated through comprehensive measurements of single spots, field size factors (FSF) and three-dimensional dose distributions of spread-out Bragg peaks (SOBPs), both without and with the range shifter. Differences in field size factors and absolute output at various depths of SOBPs between measurement and simulation were within 2.2%, with and without a range shifter, indicating an accurate source model. TOPAS was also validated against anthropomorphic lung phantom measurements. Comparison of dose distributions and DVHs for representative liver and lung cases between independent MC and analytical dose calculations from a commercial TPS further highlights the limitations of the ADC in situations of highly heterogeneous geometries. The fast MC platform has been implemented within our clinical practice to provide additional independent dose validation/QA of the commercial ADC for patient plans. Using the independent MC, we can more efficiently commission ADC by reducing the amount of measured data required for low dose “halo” modeling, especially when a range shifter is employed.
Purpose:
There has been a recent epidemic of human papillomavirus (HPV)–positive oropharyngeal cancer, accounting for 70% to 80% of diagnosed cases. These patients have an overall favorable prognosis and are typically treated with a combination of surgery, chemotherapy, and radiation. Because these patients live longer, they are at risk of secondary malignant neoplasms (SMNs) associated with radiation therapy. Therefore, we assessed the predicted risk of SMNs after adjuvant radiation therapy with intensity-modulated proton therapy (IMPT) compared with intensity modulated photon radiation therapy (IMRT) in patients with HPV- positive oropharyngeal cancers after complete resection.
Materials and Methods:
Thirteen consecutive patients with HPV-positive oropharyngeal cancers treated with postoperative radiation alone were selected. All patients were treated with pencil beam scanning IMPT to a total dose of 60 Gy in 2 Gy fractions. The IMRT plans were generated for clinical backup and were used for comparative purposes. The SMN risk was calculated based on an organ equivalent dose model for the linear-exponential dose-response curve.
Results:
Median age of the patient cohort was 63 years (range, 47-73 years). There was no difference in target coverage between IMPT and IMRT plans. We noted significant reductions in mean mandible, contralateral parotid, lung and skin organ equivalent doses with IMPT compared with IMRT plans (P < .001). Additionally, a significant decrease in the risk of SMNs with IMPT was observed for all the evaluated organs. Per our analysis, for patients with oropharyngeal cancers diagnosed at a national median age of 54 years with an average life expectancy of 27 years (per national Social Security data), 4 excess SMNs per 100 patients could be avoided by treating them with IMPT versus IMRT.
Conclusions:
Treatment with IMPT can achieve comparable target dose coverage while significantly reducing the dose to healthy organs, which can lead to fewer predicted SMNs compared with IMRT.
This work of fiction is part of a case study series developed by the Medical Physics Leadership Academy (MPLA). It is intended to facilitate the discussion of how students and advisors can better communicate expectations and navigate difficult conversations. In this case, a fourth-year Ph.D. student Emma learns that her advisor Dr. So is leaving the institution and has not arranged to bring any students with him. As Emma and Dr. So meet to discuss Emma's next steps, the conversation reveals misunderstandings and miscommunications of expectations, including a specific publication requirement for graduation from Dr. So. Having just learned of Dr. So's publication requirement, Emma realizes that graduating before the lab shuts down is not feasible. The intended use of this case, through group discussion or self-study, is to encourage readers to discuss the situation at hand and inspire professionalism and leadership thinking. This case study falls under the scope of and is supported by the MPLA, a committee in the American Association of Physicists in Medicine (AAPM).
Shoot-through proton FLASH radiation therapy has been proposed where the highest energy is extracted from a cyclotron to maximize the dose rate (DR). Although our proton pencil beam scanning system can deliver 250 MeV (the highest energy), this energy is not used clinically, and as such, 250 MeV has yet to be characterized during clinical commissioning. We aim to characterize the 250-MeV proton beam from the Varian ProBeam system for FLASH and assess the usability of the clinical monitoring ionization chamber (MIC) for FLASH use. We measured the following data for beam commissioning: integral depth dose curve, spot sigma, and absolute dose. To evaluate the MIC, we measured output as a function of beam current. To characterize a 250 MeV FLASH beam, we measured (1) the central axis DR as a function of current and spot spacing and arrangement, (2) for a fixed spot spacing, the maximum field size that achieves FLASH DR (ie, > 40 Gy/s), and (3) DR reproducibility. All FLASH DR measurements were performed using an ion chamber for the absolute dose, and irradiation times were obtained from log files. We verified dose measurements using EBT-XD films and irradiation times using a fast, pixelated spectral detector. R90 and R80 from integral depth dose were 37.58 and 37.69 cm, and spot sigma at the isocenter were σx = 3.336 and σy = 3.332 mm, respectively. The absolute dose output was measured as 0.343 Gy*mm2/MU for the commissioning conditions. Output was stable for beam currents up to 15 nA and gradually increased to 12-fold for 115 nA. Dose and DR depended on beam current, spot spacing, and arrangement and could be reproduced with 6.4% and 4.2% variations, respectively. Although FLASH was achieved and the largest field size that delivers FLASH DR was determined as 35 × 35 mm2, the current MIC has DR dependence, and users should measure dose and DR independently each time for their FLASH applications.
The environmental conditions that could lead to an increased risk for the development of an infection during prolonged space flight include: microgravity, stress, radiation, disturbance of circadian rhythms, and altered nutritional intake. A large body of literature exists on the impairment of the immune system by space flight. With the advent of missions outside the Earth's magnetic field, the increased risk of adverse effects due to exposure to radiation from a solar particle event (SPE) needs to be considered. Using models of reduced gravity and SPE radiation, we identify that either 2 Gy of radiation or hindlimb suspension alone leads to activation of the innate immune system and the two together are synergistic. The mechanism for the transient systemic immune activation is a reduced ability of the GI tract to contain bacterial products. The identification of mechanisms responsible for immune dysfunction during extended space missions will allow the development of specific countermeasures.
by
Krista C. J. Wink;
Erik Roelofs;
Timothy Solberg;
Liyong Lin;
Charles B. Simone;
Annika Jakobi;
Christian Richter;
Philippe Lambin;
Esther G. C. Troost
This review article provides a systematic overview of the currently available evidence on the clinical effectiveness of particle therapy for the treatment of NSCLC and summarizes findings of in silico comparative planning studies. Furthermore, technical issues and dosimetric uncertainties with respect to thoracic pa rticle therapy are discussed.
The purpose of this study is to determine whether organ sparing and target coverage can be simultaneously maintained for pencil beam scanning (PBS) proton therapy treatment of thoracic tumors in the presence of motion, stopping power uncertain-ties, and patient setup variations. Ten consecutive patients that were previously treated with proton therapy to 66.6/1.8 Gy (RBE) using double scattering (DS) were replanned with PBS. Minimum and maximum intensity images from 4D CT were used to introduce flexible smearing in the determination of the beam specific PTV (BSPTV). Datasets from eight 4D CT phases, using ± 3% uncertainty in stop-ping power and ± 3 mm uncertainty in patient setup in each direction, were used to create 8 × 12 × 10 = 960 PBS plans for the evaluation of 10 patients. Plans were normalized to provide identical coverage between DS and PBS. The average lung V20, V5, and mean doses were reduced from 29.0%, 35.0%, and 16.4 Gy with DS to 24.6%, 30.6%, and 14.1 Gy with PBS, respectively. The average heart V30 and V45 were reduced from 10.4% and 7.5% in DS to 8.1% and 5.4% for PBS, respectively. Furthermore, the maximum spinal cord, esophagus, and heart doses were decreased from 37.1 Gy, 71.7 Gy, and 69.2 Gy with DS to 31.3 Gy, 67.9 Gy, and 64.6 Gy with PBS. The conformity index (CI), homogeneity index (HI), and global maximal dose were improved from 3.2, 0.08, 77.4 Gy with DS to 2.8, 0.04, and 72.1 Gy with PBS. All differences are statistically significant, with p-values < 0.05, with the exception of the heart V45 (p = 0.146). PBS with BSPTV achieves better organ sparing and improves target coverage using a repainting method for the treatment of thoracic tumors. Incorporating motion-related uncertainties is essential.