Online Dose Verification Research Articles

Hybrid-Supervised Deep Learning for Proton-Acoustic Reconstruction for 3D In Vivo Proton Dose Verification

Proton-acoustic (PA) image has shown great potential to provide real-time 3D dose verification of proton therapy. However, the PA image quality suffers from severe limited view artifacts, which significantly impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for PA reconstruction to address the limited-view issues. Our method consists of two stages. In the first stage, a transformer-based network was proposed to reconstruct initial pressure maps from protoacoustic signals. The network was first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the second stage, the PA image was further enhanced by a 3D U-net. The final PA images were converted to dose maps using conversion coefficients derived from CT images. Data from 126 prostate cancer patients treated by proton therapy were collected under an IRB protocol and were split into 86 and 40 patients for model training and testing, respectively. Data of each patient contains the planning CT scan, the corresponding clinical treatment plan, and the dose map calculated by commercial software. The radiofrequency signals were generated by performing proton acoustic simulation based on CT images and the ground truth pressure map derived from the treatment plan. An ultrasound detector matrix with 64 × 64 size and 500kHz central frequency was simulated under the perineum to acquire the signals in the prostate area. In the testing results, the method's accuracy was evaluated using Root-mean-squared-error (RMSE) and structural-similarity-index-measure (SSIM) between the reconstructed and ground truth pressure map and dose distribution. Testing results showed that the reconstructed pressure map achieved an average RMSE/SSIM of 0.0292/0.96, demonstrating excellent 3D information with details. Dose maps derived from the pressure map achieved an average RMSE/SSIM of 0.018/0.99 with a gamma index of 94.7% and 95.7% for 1%/3 mm and 1%/5 mm criteria compared to the ground truth dose maps. The reconstruction time was 6s, which can be further reduced using GPU. Our study achieves start-of-the-art performance in the challenging task of direct reconstruction from limited-view radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool for in-vivo 3D proton dose verification. Such high-precision 3D online dose verification can substantially reduce the range uncertainties of proton therapy to significantly improve its precision and outcomes.

Single-Pulse X-ray Acoustic Computed Tomographic Imaging for Precision Radiation Therapy

High-precision radiation therapy is crucial for cancer treatment. Currently, the delivered dose can only be verified via simulations with phantoms, and an in-tumor, online dose verification is still unavailable. An innovative detection method called x-ray-induced acoustic computed tomography (XACT) has recently shown the potential for imaging the delivered radiation dose within the tumor. Prior XACT imaging systems have required tens to hundreds of signal averages to achieve high-quality dose images within the patient, which reduces its real-time capability. Here, we demonstrate that XACT dose images can be reproduced from a single x-ray pulse (4 µs) with sub-mGy sensitivity from a clinical linear accelerator. By immersing an acoustic transducer in a homogeneous medium, it is possible to detect pressure waves generated by the pulsed radiation from a clinical linear accelerator. After rotating the collimator, signals of different angles are obtained to perform a tomographic reconstruction of the dose field. Using 2-stage amplification with further bandpass filtering increases the signal-to-noise ratio (SNR). Acoustic peak SNR and voltage values were recorded for singular and dual-amplifying stages. The SNR for single-pulse mode was able to satisfy the Rose criterion, and the collected signals were able to reconstruct 2-dimensional images from the 2 homogeneous media. By overcoming the low SNR and requirement of signal averaging, single-pulse XACT imaging holds great potential for personalized dose monitoring from each individual pulse during radiation therapy.

Ultra-high dose rate dosimetry for pre-clinical experiments with mm-small proton fields

PurposeTo characterize an experimental setup for ultra-high dose rate (UHDR) proton irradiations, and to address the challenges of dosimetry in millimetre-small pencil proton beams. MethodsAt the PSI Gantry 1, high-energy transmission pencil beams can be delivered to biological samples and detectors up to a maximum local dose rate of ∼9000 Gy/s. In the presented setup, a Faraday cup is used to measure the delivered number of protons up to ultra-high dose rates. The response of transmission ion-chambers, as well as of different field detectors, was characterized over a wide range of dose rates using the Faraday cup as reference. ResultsThe reproducibility of the delivered proton charge was better than 1 % in the proposed experimental setup. EBT3 films, Al2O3:C optically stimulated luminescence detectors and a PTW microDiamond were used to validate the predicted dose. Transmission ionization chambers showed significant volume ion-recombination (>30 % in the tested conditions) which can be parametrized as a function of the maximum proton current density. Over the considered range, EBT3 films, inorganic scintillator-based screens and the PTW microDiamond were demonstrated to be dose rate independent within ±3 %, ±1.8 % and ±1 %, respectively. ConclusionsFaraday cups are versatile dosimetry instruments that can be used for dose estimation, field detector characterization and on-line dose verification for pre-clinical experiments in UHDR proton pencil beams. Among the tested detectors, the commercial PTW microDiamond was found to be a suitable option to measure real time the dosimetric properties of narrow pencil proton beams for dose rates up to 2.2 kGy/s.

Technical note: Characterization of novel iterative reconstructed cone beam CT images for dose tracking and adaptive radiotherapy on L-shape linacs.

Cone-beam computed tomography (CBCT) allows for patient setup and positioning, and potentially dose verification or adaptive replanning prior to each treatment delivery. Poor CBCT image quality due to scatter artifacts and patient motion has been a major limiting factor. A new image reconstruction algorithm was recently clinically implemented for improving image quality through iterative reconstruction (iCBCT). This study aims to characterize iCBCT image quality, establish image value (HU)-to-relative electron density (RED) calibration curves for dose calculation, and assess the dosimetric accuracy for different anatomical sites. Both conventional CBCT and iCBCT scans were acquired from a Varian TrueBeam On-Board Imager system. A Catphan 604 phantom was scanned to compare image quality between the traditional Feldkamp-Davis-Kress (FDK) and novel iterative reconstruction techniques. Computerized Imaging Reference Systems (CIRS) electron density phantom was used to construct site-specific HU-RED curves corresponding to various scan settings. The CIRS Dynamic Thorax phantom, Rando pelvis phantom, and BrainLab head phantom were used for assessing dosimetric accuracy calculated on iCBCT images, compared to that on traditional FDK-based CBCT images. All phantoms were scanned on a computed tomography (CT) to obtain baseline HU values for comparison. Test results obtained from Catphan showed statistically significant improvement with iCBCT, compared to FDK CBCT. Average HU differences from the baseline CT values were improved to within ±30HU for iCBCT, compared to FDK CBCT for phantom studies. Dose calculated on iCBCT for both phantoms and patient cases directly using baseline HU-RED calibration from CT showed 0.5%-2.0% accuracy from the baseline dose calculated on CT, which is comparable to doses calculated using site-specific HU-RED calibration curves. iCBCT provides improved image quality, improved HU accuracy compared to CT baseline, and has potential to provide online dose verification as part of the adaptive radiotherapy workflow directly using the baseline HU-RED calibration curve from CT.

Machine learning based oxygen and carbon concentration derivation using dual-energy CT for PET-based dose verification in proton therapy.

Online dose verification based on proton-induced positron emitters requires high accuracy in the assignment of elemental composition (e.g., C and O). We developed a machine learning framework for deriving oxygen and carbon concentration based on dual-energy CT (DECT). Digital phantoms at the head site were constructed based on single-energy CT (SECT) and stoichiometric calibration. DECT images (80 and 140kVp) were synthesized using two methods: (1) theoretical CT numbers with Gaussian noise (method 1) and (2) forward/backward image reconstruction with poly-energetic energy spectrum and Poisson noise modeled (method 2). Two architectures of convolutional neural networks, UNet and ResNet, were investigated to map from DECT images to C/O weights. Four cases (UNet-1: Method 1+UNet, ResNet-1: Method 1+ResNet, UNet-2: Method 2+UNet, and ResNet-2: Method 2 +ResNet) were tested for different tissue types and different noise levels. Monte-Carlo simulation was employed to identify the impact of fluctuation in oxygen and carbon concentration on activity/dose distribution. When no noise is present, all four cases are able to obtain <2% mean absolute errors and <4% root mean square error (RMSE). For the worst image quality (e.g., lowest image SNR), the RMSE for O among all tissue types is 3.02% (UNet-1), 4.46% (ResNet-1), 4.38% (UNet-2), and 6.31% (ResNet-2), respectively. For UNet-1 and ResNet-1, the model performed slightly better in terms of RMSE for skeletal tissue than soft tissues. Such a trend is not observed for UNet-2 and ResNet-2. With regard to the comparison between UNet and ResNet, different accuracy and noise immunity are observed. The activity profiles exhibit 3%-5% difference in terms of mean relative error between the ground truth and machine learning outcome. We explored the feasibility of a machine learning framework to derive elemental concentration of oxygen and carbon based on DECT images. Two machine learning models, UNet and ResNet, are able to utilize spatial correlation and obtain accurate carbon and oxygen concentration. This study lays a foundation for us to apply the proposed approach to clinical DECT images.

Further investigation of 3D dose verification in proton therapy utilizing acoustic signal, wavelet decomposition and machine learning

Online dose verification in proton therapy is a critical task for quality assurance. We further studied the feasibility of using a wavelet-based machine learning framework to accomplishing that goal in three dimensions, built upon our previous work in 1D. The wavelet decomposition was utilized to extract features of acoustic signals and a bidirectional long-short-term memory (Bi-LSTM) recurrent neural network (RNN) was used. The 3D dose distributions of mono-energetic proton beams (multiple beam energies) inside a 3D CT phantom, were generated using Monte-Carlo simulation. The 3D propagation of acoustic signal was modeled using the k-Wave toolbox. Three different beamlets (i.e. acoustic pathways) were tested, one with its own model. The performance was quantitatively evaluated in terms of mean relative error (MRE) of dose distribution and positioning error of Bragg peak (), for two signal-to-noise ratios (SNRs). Due to the lack of experimental data for the time being, two SNR conditions were modeled (SNR = 1 and 5). The model is found to yield good accuracy and noise immunity for all three beamlets. The results exhibit an MRE below 0.6% (without noise) and 1.2% (SNR = 5), and below 1.2 mm (without noise) and 1.3 mm (SNR = 5). For the worst-case scenario (SNR = 1), the MRE and are below 2.3% and 1.9 mm, respectively. It is encouraging to find out that our model is able to identify the correlation between acoustic waveforms and dose distributions in 3D heterogeneous tissues, as in the 1D case. The work lays a good foundation for us to advance the study and fully validate the feasibility with experimental results.

Extension and validation of a GPU-Monte Carlo dose engine gDPM for 1.5 T MR-LINAC online independent dose verification.

To extend and validate the accuracy and efficiency of a graphics processing unit (GPU)-Monte Carlo dose engine for Elekta Unity 1.5 T Magnetic Resonance-Linear Accelerator (MR-LINAC) online independent dose verification. Electron/positron propagation physics in a uniform magnetic field was implemented in a previously developed GPU-Monte Carlo dose engine-gDPM. The dose calculation accuracy in the magnetic field was first evaluated in heterogeneous phantom with EGSnrc. The dose engine was then commissioned to a Unity machine with a virtual two photon-source model and compared with the Monaco treatment planning system. Fifteen patient plans from five tumor sites were included for the quantification of online dose verification accuracy and efficiency. The extended gDPM accurately calculated the dose in a 1.5 T external magnetic field and was well matched with EGSnrc. The relative dose difference along central beam axis was less than 0.5% for the homogeneous region in water-lung phantom. The maximum difference was found at the build-up regions and heterogeneous interfaces, reaching 1.9% and 2.4% for 2 and 6MeV mono-energy photon beams, respectively. The root mean square errors for depth-dose fall-off region were less than 0.2% for all field sizes and presented a good match between gDPM and Monaco GPUMCD. For in-field profiles, the dose differences were within 1% for cross-plane and in-plane directions for all calculated depths except dmax. For penumbra regions, the distance-to-agreements between two dose profiles were less than 0.1cm. For patient plan verification, the maximum relative average dose difference was 1.3%. The gamma passing rates with criteria 3% (2mm) for dose regions above 20% were between 93% and 98%. gDPM can complete the dose calculation for less than 40 s with 5 × 108 photons on a single NVIDIA GTX-1080Ti GPU and achieve a statistical uncertainty of 0.5%-1.1% for all evaluated cases. A GPU-Monte Carlo package-gDPM was extended and validated for Elekta Unity online plan verification. Its calculation accuracy and efficiency make it suitable for online independent dose verification for MR-LINAC.

Portal dosimetry in radiotherapy repeatability evaluation.

The accuracy of radiotherapy is the subject of continuous discussion, and dosimetry methods, particularly in dynamic techniques, are being developed. At the same time, many oncology centers develop quality procedures, including pretreatment and online dose verification and proper patient tracking methods. This work aims to present the possibility of using portal dosimetry in the assessment of radiotherapy repeatability. The analysis was conducted on 74 cases treated with dynamic techniques. Transit dosimetry was made for each collision‐free radiation beam. It allowed the comparison of summary fluence maps, obtained for fractions with the corresponding summary maps from all other treatment fractions. For evaluation of the compatibility in the fluence map pairs (6798), the gamma coefficient was calculated. The results were considered in four groups, depending on the used radiotherapy technique: stereotactic fractionated radiotherapy, breath‐hold, free‐breathing, and conventionally fractionated other cases. The chi2 or Fisher's exact test was made depending on the size of the analyzed set and also Mann–Whitney U‐test was used to compare treatment repeatability of different techniques. The aim was to test whether the null hypothesis of error‐free therapy was met. The patient is treated repeatedly if the P‐value in all the fluence maps sets is higher than the level of 0.01. The best compatibility between treatment fractions was obtained for the stereotactic technique. The technique with breath‐holding gave the lowest percentage of compliance of the analyzed fluence pairs. The results indicate that the repeatability of the treatment is associated with the radiotherapy technique. Treated volume location is also an essential factor found in the evaluation of treatment accuracy. The EPID device is a useful tool in assessing the repeatability of radiotherapy. The proposed method of fluence maps comparison also allows us to assess in which therapeutic session the patient was treated differently from the other fractions.

A machine learning framework with anatomical prior for online dose verification using positron emitters and PET in proton therapy

We developed a machine learning framework in order to establish the correlation between dose and activity distributions in proton therapy. A recurrent neural network was used to predict dose distribution in three dimensions based on the information of proton-induced positron emitters. Hounsfield Unit (HU) information from CT images and analytically derived stopping power (SP) information were incorporated as auxiliary inputs. Four different scenarios were investigated: Activity only, Activity + HU, Activity + SP and Activity + HU + SP. The performance was quantitatively studied in terms of mean absolute error (MAE) and mean relative error (MRE), under different signal-to-noise ratios (SNRs). In addition to the first dataset of mono-energetic beams, three additional datasets were validated to help evaluate the generalization capability of our proposed model: a dataset of a lower SNR, five reconstructed PET images, and a dataset of spread-out Bragg peaks. Good verification accuracy of dose verification in three dimensions is demonstrated. The inclusion of anatomical information improves both accuracy and generalization. For an activity profile with an SNR of 4 (the mono-energetic case), the framework is able to obtain an MRE of ∼ 0.99% over the whole range and a range uncertainty of ∼ 0.27 mm. The machine learning-based framework may emerge as a useful tool to allow for online dose verification and quality assurance in proton therapy.

Feasibility study of patient-specific dose verification in proton therapy utilizing positron emission tomography (PET) and generative adversarial network (GAN).

Online dose verification based on proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between dose and the activity distributions, a machine learning-based approach was developed to establish their relationship. Simulations were carried out using a pencil beam scanning system and a computed tomography (CT) image-based phantom. A DiscoGAN model was developed to perform dose verification for both central and off-center lines. Besides the activity as input, HU information from CT images and stopping power (SP) prior were incorporated as auxiliary features for the model. The performance was quantitatively studied in terms of mean absolute error (MAE) and mean relative error (MRE), under different signal-to-noise ratios (SNRs). In addition to a dataset comprising monoenergetic beams, two additional datasets were generated to evaluate the model's generalization capability: five reconstructed PET images based on an in-beam PET system and a dataset comprising spread-out Bragg peaks (SOBPs). The feasibility of dose verification was successfully demonstrated for all three datasets. For the monoenergetic case (i.e., raw activity of positron emitters), the MRE is found to be <1% for the central lines and 5% for the off-center lines, respectively. The range uncertainty is found to be less than 1mm. The prediction based on five PET images, which take into account the detection of 511-keV photons and image reconstruction, yields slightly inferior performance. For the SOBP case, the MRE of the center lines is found to be <3% and the range uncertainty is <1mm. The inclusion of anatomical information (HU and SP) improves both accuracy and generalization of the DiscoGAN model. The combination of proton-induced positron emitters, in-beam PET, and machine learning may become a useful tool allowing for patient-specific online dose verification in proton therapy.

Anovel approach to SBRT patient quality assurance using EPID-based real-time transit dosimetry : Astep to QA with in vivo EPID dosimetry.

Intra- and inter-fraction organ motion is amajor concern in stereotactic body radiation therapy (SBRT). It may cause substantial differences between the planned and delivered dose distribution. Such delivery errors may lead to medical harm and reduce life expectancy for patients. The project presented here investigates and improves arapid method to detect such errors by performing online dose verification through the analysis of electronic portal imaging device (EPID) images. To validate the method, arespiratory phantom with inhomogeneous insert was examined under various scenarios: no-error and error-simulated measurements. Simulation of respiratory motions was practiced for target ranges up to 2 cm. Three types of treatment planning technique - 3DCRT (three-dimensional conformal radiation therapy), IMRT (intensity modulated radiation therapy), and VMAT (volumetricmodulated arc therapy - were generated for lung SBRT. Atotal of 54plans were generated to assess the influence of techniques on the performance of portal dose images. Subsequently, EPID images of 52SBRT patients were verified. Both for phantom and patient cases, dose distributions were compared using the gamma index method according to analysis protocols in the target volume. The comparison of error-introduced EPID-measured images to reference images showed no significant differences with 3%/3 mm gamma evaluation, though target coverage was strongly underestimated. Gamma tolerance of 2%/2 mm reported noticeable detection in EPID sensitivity for simulated errors in 3DCRT and IMRT techniques. The passing rates for 3DCRT, IMRT, and VMAT with 1%/1 mm in open field were 84.86%, 92.91%, and 98.75%, and by considering MLC-CIAO + 1 cm (threshold 5%), were 68.25%, 83.19%, and 95.29%, respectively. This study demonstrates the feasibility of EPID for detecting the interplay effects. We recommend using thin computed tomography slices and adding sufficient tumor margin in order to limit the dosimetric organ motion in hypofractionated irradiation with preserved plan quality. In the presence of respiratory and gastrointestinal motion, tighter criteria and consequently using local gamma evaluation should be considered, especially for VMAT. This methodology offers asubstantial step forward in in vivo dosimetry and the potential to distinguish errors depending on the gamma tolerances. Thus, the approach/prototype provides afast and easy quality assurance procedure for treatment delivery verification.

Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton-induced positron emitters.

Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distribution of positron emitters in addition to the presence of noise, machine learning approaches were proposed to establish their relationship. Simulations were carried out with a spot-scanning proton system using GATE-8.0 and Geant4-10.3 toolkit with a computed tomography (CT)-based patient phantom. The one-dimensional (1D) distributions of positron emitters and radiation dose were obtained. A feedforward neural network classification model comprising two hidden layers, was developed to estimate whether the range is within a preset threshold. A recurrent neural network (RNN) regression model comprising three layers and ten neurons in each hidden layer was developed to estimate dose distribution. The performance was quantitatively studied in terms of mean squared error (MSE) and mean absolute error (MAE) under different signal-to-noise ratio (SNR) values. The feasibility of proton range and dose verification using the proposed neural network framework was demonstrated. The feedforward NN model achieves high classification accuracy close to 100% for individual classes without bias. The RNN model is able to accurately predict the 1D dose distribution for different energies and irradiation positions. When the SNR of the input activity profiles is above 4, the framework is able to predict with an MAE of ~0.60mm and an MSE of ~0.066. Moreover, the model demonstrates a good capability of generalization. The RNN model is found to be effective in identifying the relationship between the distributions of dose and positron emitters. The machine learning-based framework and RNN models may be a useful tool to allow for accurate online range and dose verification based on proton-induced positron emitters.

SU‐D‐BRC‐03: Development and Validation of an Online 2D Dose Verification System for Daily Patient Plan Delivery Accuracy Check

Purpose:All plan verification systems for particle therapy are designed to do plan verification before treatment. However, the actual dose distributions during patient treatment are not known. This study develops an online 2D dose verification tool to check the daily dose delivery accuracy.Methods:A Siemens particle treatment system with a modulated scanning spot beam is used in our center. In order to do online dose verification, we made a program to reconstruct the delivered 2D dose distributions based on the daily treatment log files and depth dose distributions. In the log files we can get the focus size, position and particle number for each spot. A gamma analysis is used to compare the reconstructed dose distributions with the dose distributions from the TPS to assess the daily dose delivery accuracy. To verify the dose reconstruction algorithm, we compared the reconstructed dose distributions to dose distributions measured using PTW 729XDR ion chamber matrix for 13 real patient plans. Then we analyzed 100 treatment beams (58 carbon and 42 proton) for prostate, lung, ACC, NPC and chordoma patients.Results:For algorithm verification, the gamma passing rate was 97.95% for the 3%/3mm and 92.36% for the 2%/2mm criteria. For patient treatment analysis,the results were 97.7%±1.1% and 91.7%±2.5% for carbon and 89.9%±4.8% and 79.7%±7.7% for proton using 3%/3mm and 2%/2mm criteria, respectively. The reason for the lower passing rate for the proton beam is that the focus size deviations were larger than for the carbon beam. The average focus size deviations were −14.27% and −6.73% for proton and −5.26% and −0.93% for carbon in the x and y direction respectively.Conclusion:The verification software meets our requirements to check for daily dose delivery discrepancies. Such tools can enhance the current treatment plan and delivery verification processes and improve safety of clinical treatments.

Patient-specific online dose verification based on transmission detector measurements

Background and purposeSince IMRT-techniques lead to an increasingly complicated environment, a patient specific IMRT-plan verification is recommended. Furthermore, verifications during patient irradiation and 3D dose reconstruction have the potential to improve treatment delivery, accuracy and safety. This study provides a detailed investigation of the new transmission detector (DTD) Dolphin (IBA Dosimetry, Germany) for online dosimetry. Materials and methodsThe clinical performance of the DTD was tested by dosimetric plan verification in 2D and 3D for 18 IMRT-sequences. In 2D, DTD measurements were compared to a pre-treatment verification method and a treatment planning system by gamma index and dose difference evaluations. In 3D, dose–volume-histogram (DVH) indices and gamma analysis were evaluated. Furthermore, the error detection ability was tested with leaf position uncertainties and deviations in the linear accelerator (LINAC) output. ResultsThe DTD measurements were in excellent agreement to reference measurements in both 2D (γ3%,3mm=(99.7±0.6)% <1, ΔD±5%=(99.5±0.5)%) and 3D. Only a small dose underestimation (<2%) within the target volume was observed when analyzing DVH-indices. Positional errors of the leaf banks larger than 1mm and errors in LINAC output larger than 2% were identified with the DTD. ConclusionsThe DTD measures the delivered dose with sufficient accuracy and is therefore suitable for clinical routine.

SU‐E‐T‐602: Patient‐Specific Online Dose Verification Based On Transmission Detector Measurements

Purpose:Intensity modulated radiotherapy requires a comprehensive quality assurance program in general and ideally independent verification of dose delivery. Since conventional 2D detector arrays allow only pre‐treatment verification, there is a debate concerning the need of online dose verification. This study presents the clinical performance, including dosimetric plan verification in 2D as well as in 3D and the error detection abilities of a new transmission detector (TD) for online dose verification of 6MV photon beam.Methods:To validate the dosimetric performance of the new device, dose reconstruction based on TD measurements were compared to a conventional pre‐treatment verification method (reference) and treatment planning system (TPS) for 18 IMRT and VMAT treatment plans. Furthermore, dose reconstruction inside the patient based on TD read‐out was evaluated by comparing various dose volume indices and 3D gamma evaluations against independent dose computation and TPS. To investigate the sensitivity of the new device, different types of systematic and random errors for leaf positions and linac output were introduced in IMRT treatment sequences.Results:The 2D gamma index evaluation of transmission detector based dose reconstruction showed an excellent agreement for all IMRT and VMAT plans compared to reference measurements (99.3±1.2)% and TPS (99.1±0.7)%. Good agreement was also obtained for 3D dose reconstruction based on TD read‐out compared to dose computation (mean gamma value of PTV = 0.27±0.04). Only a minimal dose underestimation within the target volume was observed when analyzing DVH indices (&lt;1%). Positional errors in leaf banks larger than 1mm and errors in linac output larger than 2% could clearly identified with the TD.Conclusion:Since 2D and 3D evaluations for all IMRT and VMAT treatment plans were in excellent agreement with reference measurements and dose computation, the new TD is suitable to qualify for routine treatment plan verification.Funding Support, Disclosures, and Conflict of Interest: COIs: Frank Lohr: Elekta: research grant, travel grants, teaching honoraria IBA: research grant, travel grants, teaching honoraria, advisory board C‐Rad: board honoraria, travel grants Frederik Wenz: Elekta: research grant, teaching honoraria, consultant, advisory board Zeiss: research grant, teaching honoraria, patent Hansjoerg Wertz: Elekta: research grant, teaching honoraria IBA: research grant

SU‐E‐J‐146: A Research of PET‐CT SUV Range for the Online Dose Verification in Carbon Ion Radiation Therapy

Purpose:Positron‐emitting isotope distributions can be used for the image fusion of the carbon ion planning CT and online target verification PETCT, after radiation in the same decay period,the relationship between the same target volume and the SUV value of different every single fraction dose can be found,then the range of SUV for the radiation target could be decided.So this online range also can provide reference for the correlation and consistency in planning target dose verification and evaluation for the clinical trial.Methods:The Rando head phantom can be used as real body,the 10cc cube volume target contouring is done,beam ISO Center depth is 7.6cm and the 90 degree fixed carbon ion beams should be delivered in single fraction effective dose of 2.5GyE,5GyE and 8GyE.After irradiation,390 seconds later the 30 minutes PET‐CT scanning is performed,parameters are set to 50Kg virtual weight,0.05mCi activity.MIM Maestro is used for the image processing and fusion,five 16mm diameter SUV spheres have been chosen in the different direction in the target.The average SUV in target for different fraction dose can be found by software.Results:For 10cc volume target,390 seconds decay period,the Single fraction effective dose equal to 2.5Gy,Ethe SUV mean value is 3.42,the relative range is 1.72 to 6.83;Equal to 5GyE,SUV mean value is 9.946,the relative range is 7.016 to 12.54;Equal or above to 8GyE,SUV mean value is 20.496,the relative range is 11.16 to 34.73.Conclusion:Making an evaluation for accuracy of the dose distribution using the SUV range which is from the planning CT with after treatment online PET‐CT fusion for the normal single fraction carbon ion treatment is available.Even to the plan which single fraction dose is above 2GyE,in the condition of other parameters all the same,the SUV range is linearly dependent with single fraction dose,so this method also can be used in the hyper‐fraction treatment plan.

A systematic review of antiproton radiotherapy

Antiprotons have been proposed as possible particles for radiotherapy; over the past years, the renewed interest in the potential biomedical relevance led to an increased research activity. It is the aim of this review to deliver a comprehensive overview regarding the evidence accumulated so far, analysing the background and depicting the current status of antiprotons in radiotherapy. A literature search has been conducted, including major scientific and commercial databases. All articles and a number of relevant conference abstracts published in the respective field have been included in this systematic review. The physical basis of antiproton radiotherapy is complex; however, the characterisation of the energy deposition profile supports its potential use in radiotherapy. Also the dosimetry improved considerably over the past few years. Regarding the biological properties, data on the effects on cells are presented; however, definite conclusions regarding the relative biological effectiveness cannot be made at the moment and radiobiological evidence of enhanced effectiveness remains scarce. In addition, there is new evidence supporting the potential imaging properties, for example for online dose verification. Clinical settings which might profit from the use of antiprotons have been further tracked. Judging from the evidence available so far, clinical constellations requiring optimal sparing in the entrance region of the beam and re-irradiations might profit most from antiproton radiotherapy. While several open questions remain to be answered, first steps towards a thorough characterisation of this interesting modality have been made.

SU-E-T-154: Online Dose Verification with Gafchromic Film for Fixed-Gantry and Rotational Intensity Modulated Radiation Therapy: A Phantom Study.

The patient specific quality assurance (QA) measurements for fixed-gantry and rotational intensity modulated radiation therapy (IMRT and VMAT/RapidArc) are usually performed on a homogeneous phantom prior to the treatment. The purpose of this study is to develop an online method to verify the delivered dose to the patient on the treatment day. An anthropomorphic (Rando) head phantom was immobilized in treatment position with a thermoplastic mask to simulate a real patient. A sheet of gafchromic film (EBT2) was sandwiched between a 1-cm-thick solid water slab, which was fixed to the Type-S extension board, and the patient's head hold (a pillow used here). The CT images of the Rando phantom were acquired and exported to the treatment planning systems. One step-and-shot fixed-gantry IMRT plan and one RapidArc plan were generated and the dose distributions on the film plane were calculated. The two plans were delivered to the patient (Rando phantom in this study) in the treatment position on a Varian Trilogy linear accelerator with two new films. The films were scanned, and the measurements were compared with the planned doses. The composite dose distributions measured on the film plane were the actual delivered dose for the treatment. The comparison between the measurement and planned dose profiles shows an agreement within 3% because of the good reproducibility of phantom positioning. Gamma pass rates (using 3mm and 3% criteria) for the IMRT and RapidArc plan were found to be 95% and 94%, respectively. The phantom study has demonstrated the feasibility of using gafchromic film for online dose verification. This simple method takes into account the patient heterogeneity and the treatment associated uncertainties such as setup error, intrafraction motions and machine related variations. It can be implemented as an online physics and/or clinical QA tool without taking additional machine time.

Investigation of the spatial resolution of an online dose verification device

The aim of this work is to characterize a new online dose verification device, COMPASS transmission detector array (IBA Dosimetry, Schwarzenbruck, Germany). The array is composed of 1600 cylindrical ionization chambers of 3.8 mm diameter, separated by 6.5 mm center-to-center spacing, in a 40 × 40 arrangement. The line spread function (LSF) of a single ion chamber in the detector was measured with a narrow slit collimator for a 6 MV photon beam. The 0.25 × 10 mm(2) slit was formed by two machined lead blocks. The LSF was obtained by laterally translating the detector in 0.25 mm steps underneath the slit over a range of 24 mm and taking a measurement at each step. This measurement was validated with Monte Carlo simulation using BEAMnrc and DOSXYZnrc. The presampling modulation transfer function (MTF), the Fourier transform of the line spread function, was determined and compared to calculated (Monte Carlo and analytical) MTFs. Two head-and-neck intensity modulated radiation therapy (IMRT) fields were measured using the device and were used to validate the LSF measurement. These fields were simulated with the BEAMnrc Monte Carlo model, and the Monte Carlo generated incident fluence was convolved with the 2D detector response function (derived from the measured LSF) to obtain calculated dose. The measured and calculated dose distributions were then quantitatively compared using χ-comparison criteria of 3% dose difference and 3 mm distance-to-agreement for in-field points (defined as those above the 10% maximum dose threshold). The full width at half-maximum (FWHM) of the measured detector response for a single chamber is 4.3 mm, which is comparable to the chamber diameter of 3.8 mm. The pre-sampling MTF was calculated, and the resolution of one chamber was estimated as 0.25 lp∕mm from the first zero crossing. For both examined IMRT fields, the χ-comparison between measured and calculated data show good agreement with 95.1% and 96.3% of in-field points below χ of 1.0 for fields 1 and 2, respectively (with an average χ of 0.29 for IMRT field 1 and 0.24 for IMRT field 2). The LSF for a new novel online detector has been measured at 6 MV using a narrow slit technique, and this measurement has been validated by Monte Carlo simulation. The detector response function derived from line spread function has been applied to recover measured IMRT fields. The results have shown that the device measures IMRT fields accurately within acceptable tolerance.

SU-E-T-216: Investigation of the System Performance of An Online Dose Verification Device

Purpose: To measure the detector response function of an online dose verification device and to validate our measurement by applying the detector response to Monte Carlo (MC) derived incident fluence. Methods: A transmission detector array (IBA Dosimetry, Schwarzenbruck, Germany) was recently developed as an IMRT quality assurance tool. To understand the detector system performance for verification of IMRT fluence, the detector response function at 6MV of a single ion chamber in the detector was measured with a narrow slit collimator. The narrow slit was formed by two 24 × 72 × 152 mm3 lead blocks, which provided a slit of 0.25 × 10 mm2. The LSF of the detector was obtained by laterally translating it in 0.25 mm steps underneath the slit. Two head-and-neck IMRT fields were also measured using the device. The same fields were simulated in our BEAMnrc MC model, and the MC incident fluence was convolved with the 2D detector response to obtain calculated dose. The measured and calculated dose distributions were then quantitatively compared using chi comparison (3%/3mm) for in-field points (defined as those above 10% maximum dose). Results: The FWHM of the measured detector response for a single chamber is 4.3 mm which is comparable to the chamber diameter of 3.8 mm. For both examined IMRT fields, the Chi comparison between measured and calculated data show good agreement with 100% of in-field points below a chi of 1.0 (chi <0.6 for IMRT field 1 and <0.12 for IMRT field 2). Conclusions: The detector response function for a new novel online detector has been measured at 6 MV using a narrow slit technique. The good comparison shown between measured and calculated dose of the two IMRT fields is a validation of our response function measurement.