Assessing the quality of proton PBS treatment delivery using machine log files: comprehensive analysis of clinical treatments delivered at PSI Gantry 2

Monte Carlo simulation-based patient-specific QA using machine log files for line-scanning proton radiation therapy

BACKGROUND

Quality assurance (QA) is a prerequisite for safe and accurate pencil-beam proton therapy. Conventional measurement-based patient-specific QA (pQA) can only verify limited aspects of patient treatment and is labor-intensive. Thus, a better method is needed to ensure the integrity of the treatment plan.

PURPOSE

Line scanning, which involves continuous and rapid delivery of pencil beams, is a state-of-the-art proton therapy technique. Machine performance in delivering scanning protons is dependent on the complexity of the beam modulations. Moreover, it contributes to patient treatment accuracy. A Monte Carlo (MC) simulation-based QA method that reflects the uncertainty related to the machine during scanning beam delivery was developed and verified for clinical applications to pQA.

METHODS

Herein, a tool for particle simulation (TOPAS) for nozzle modeling was used, and the code was commissioned against the measurements. To acquire the beam delivery uncertainty for each plan, patient plans were delivered. Furthermore, log files recorded every 60 μs by the monitors downstream of the nozzle were exported from the treatment control system. The spot positions and monitor unit (MU) counts in the log files were converted to dipole magnet strengths and number of particles, respectively, and entered into the TOPAS. For the 68 clinical cases, MC simulations were performed in a solid water phantom, and two-dimensional (2D) absolute dose distributions at 20-mm depth were measured using an ionization chamber array (Octavius 1500, PTW, Freiburg, Germany). Consequently, the MC-simulated 2D dose distributions were compared with the measured data, and the dose distributions in the pre-treatment QA plan created with RayStation (RaySearch Laboratories, Stockholm, Sweden). Absolute dose comparisons were made using gamma analysis with 3%/3 mm and 2%/2 mm criteria for 47 clinical cases without considering daily machine output variation in the MC simulation and 21 cases with daily output variation, respectively. All cases were analyzed with 90% or 95% of passing rate thresholds.

RESULTS

For 47 clinical cases not considering daily output variations, the absolute gamma passing rates compared with the pre-treatment QA plan were 99.71% and 96.97%, and the standard deviations (SD) were 0.70% and 3.78% with the 3%/3 mm or 2%/2 mm criteria, respectively. Compared with the measurements, the passing rate of 2%/2 mm gamma criterion was 96.76% with 3.99% of SD. For the 21 clinical cases compared with pre-treatment QA plan data and measurements considering daily output variations, the 2%/2 mm absolute gamma analysis result was 98.52% with 1.43% of SD and 97.67% with 2.72% of SD, respectively. With a 95% passing rate threshold of 2%/2 mm criterion, the false-positive and false-negative were 21.8% and 8.3% for without and with considering output variation, respectively. With a 90% threshold, the false-positive and false-negative reduced to 11.4% and 0% for without and with considering output variation, respectively.

CONCLUSIONS

A log-file-based MC simulation method for patient QA of line-scanning proton therapy was successfully developed. The proposed method exhibited clinically acceptable accuracy, thereby exhibiting a potential to replace the measurement-based dosimetry QA method with a 90% gamma passing rate threshold when applying the 2%/2 mm criterion.

Dosimetric comparison of autocontouring techniques for online adaptive proton therapy

Objective.Anatomical and daily set-up uncertainties impede high precision delivery of proton therapy. With online adaptation, the daily plan is reoptimized on an image taken shortly before the treatment, reducing these uncertainties and, hence, allowing a more accurate delivery. This reoptimization requires target and organs-at-risk (OAR) contours on the daily image, which need to be delineated automatically since manual contouring is too slow. Whereas multiple methods for autocontouring exist, none of them are fully accurate, which affects the daily dose. This work aims to quantify the magnitude of this dosimetric effect for four contouring techniques.Approach.Plans reoptimized on automatic contours are compared with plans reoptimized on manual contours. The methods include rigid and deformable registration (DIR), deep-learning based segmentation and patient-specific segmentation.Main results.It was found that independently of the contouring method, the dosimetric influence of usingautomaticOARcontoursis small (<5% prescribed dose in most cases), with DIR yielding the best results. Contrarily, the dosimetric effect of using theautomatic target contourwas larger (>5% prescribed dose in most cases), indicating that manual verification of that contour remains necessary. However, when compared to non-adaptive therapy, the dose differences caused by automatically contouring the target were small and target coverage was improved, especially for DIR.Significance.The results show that manual adjustment of OARs is rarely necessary and that several autocontouring techniques are directly usable. Contrarily, manual adjustment of the target is important. This allows prioritizing tasks during time-critical online adaptive proton therapy and therefore supports its further clinical implementation.

A bi-directional beam-line energy ramping for efficient patient treatment with scanned proton therapy

Objective.The treatment of mobile tumours using Pencil Beam Scanning (PBS) has become more prevalent in the last decade. However, to achieve the same beam delivery quality as for static tumours, treatments have to be combined with motion mitigation techniques, not limited but including, breath hold, gating and re-scanning, which typically prolong treatment time. In this article we present a novel method of bi-directional energy modulation and demonstrate our initial experience in improvement of treatment efficiency. Approach.At Paul Scherrer Institute Gantry 2 mobile tumours are treated by combining PBS with gating and volumetric re-scanning (VR), where the target volume is irradiated multiple times. Initial implementation of VR used only descending beam energies, creating a substantial dead time due to the beam-line initialization (ramping) before each re-scan. In 2019 we commissioned an energy meandering strategy that allows us to avoid beam line ramping in-between energy series while maintaining beam delivery quality.Main results.The measured beam parameters difference for both energy sequence are in the order of the typical daily variations: 0.2 mm in beam position and 0.2 mm in range. Using machine log files, we performed point-to-point dose difference calculations between original and new applications where we observed dose differences of less than 2%. After three years of operation employing bi-directional energy modulation, we have analysed the individual beam delivery time for 181 patients and have compared this to simulations of the timing behaviour assuming uni-directional energy sequence application. Depending on treatment complexity, we obtained plan delivery time reductions of up to 55%, with a median time gain of 17% for all types of treatments.Significance. Bi-directional energy modulation can help improving patient treatment efficiency by reducing delivery times especially for complex and specialised irradiations. It could be implemented in many existing facilities without significant additional hardware upgrades.

Technical note: A method to evaluate the effect of scanning beam delivery error on 3D dose and its utilization on carbon ion radiotherapy for prostate cancer

PURPOSE

To establish a method for evaluating the effect of scanning ion beam delivery error on three-dimensional (3D) dose reconstructed on patients' CT based on log file.

MATERIALS AND METHODS

This study used the MATLAB program to reconstruct the 3D dose on the patient's CT based on the log file (Dose_log ) for treatment delivery accuracy check. In addition, differences between the parameters in the log file and the treatment plan, such as the spot position, spot size, and particle number, were analyzed, as well as their effects on the dose distribution. The accuracy of the dose reconstruction algorithm was verified by comparing dose from TPS (Dose_TPS ) and the dose recalculated based on the treatment plan (Dose_rec ). Twenty treatment plans of ten prostate cancer patients received carbon ion therapy, and their corresponding 160 log files were selected for analysis and treatment delivery accuracy check. The regions with dose higher than 10% of the maximum dose were selected and 2 mm/2% criteria were used for global gamma analysis. Multiple linear regression was used to evaluate the relation between dose deviation and delivery errors.

RESULTS

For the algorithm accuracy verification, the mean relative dose difference is 1.02% ± 0.12%. For prostate cancer patients treated in our facility using carbon ion radiotherapy, the average passing rate of the gamma analysis between the Dose_log and the Dose_TPS was 95.3%. The dose deviation caused by the difference in the spot position and the number of particles was smaller than that caused by the spot size deviation.

CONCLUSION

This study established a 3D dose verification method based on log files to evaluate the accuracy of daily delivered treatment doses. In our facility, the daily delivered dose accuracy of carbon ion therapy for prostate cancer was mainly affected by the spot size deviation in terms of the machine delivery part.

SWFT-Net: a deep learning framework for efficient fine-tuning spot weights towards adaptive proton therapy

Objective. One critical task for adaptive proton therapy is how to perform spot weight re-tuning and reoptimize plan, both of which are time-consuming and labor intensive. We proposed a deep learning framework (SWFT-Net) to speed up such a task, a starting point for us to move towards online adaptive proton therapy.Approach. For a H&N patient case, a reference intensity modulated proton therapy plan was generated. For data augmentation, spot weights were modified to generate three datasets (DS10, DS30, DS50), corresponding to different levels of weight adjustment. For each dataset, the samples were split into the training and testing groups at a ratio of 8:2 (6400 for training, 1706 for testing). To ease the difficulty of machine learning, the residuals of dose maps and spot weights (i.e. difference relative to a reference) were used as inputs and outputs, respectively. Quantitative analyses were performed in terms of normalized root mean square error (NRMSE) of spot weights, Gamma passing rate and dose difference within the PTV.Main results. The SWFT-Net is able to generate an adapted plan in less than a second with a NVIDIA GeForce RTX 3090 GPU. For the 1706 samples in the testing dataset, the NRMSE is 0.41% (DS10), 1.05% (DS30) and 2.04% (DS50), respectively. Cold/hot spots in the dose maps after adaptation are observed. The mean relative dose difference is 0.64% (DS10), 0.92% (DS30) and 0.88% (DS50), respectively. For all three datasets, the mean Gamma passing rate is consistently over 95% for both 1 mm/1% and 3 mm/3% settings.Significance. The proposed SWFT-Net is a promising tool to help realize adaptive proton therapy. It can be used as an alternative tool to other spot fine-tuning optimization algorithms, likely demonstrating superior performance in terms of speed, accuracy, robustness and minimum human interaction. This study lays down a foundation for us to move further incorporating other factors such as daily anatomical changes and propagated PTVs, and develop a truly online adaptive workflow in proton therapy.

Validation of dose distribution for liver tumors treated with real-time-image gated spot-scanning proton therapy by log data based dose reconstruction

In spot scanning proton therapy (SSPT), the spot position relative to the target may fluctuate through tumor motion even when gating the radiation by utilizing a fiducial marker. We have established a procedure that evaluates the delivered dose distribution by utilizing log data on tumor motion and spot information. The purpose of this study is to show the reliability of the dose distributions for liver tumors treated with real-time-image gated SSPT (RGPT). In the evaluation procedure, the delivered spot information and the marker position are synchronized on the basis of log data on the timing of the spot irradiation and fluoroscopic X-ray irradiation. Then a treatment planning system reconstructs the delivered dose distribution. Dose distributions accumulated for all fractions were reconstructed for eight liver cases. The log data were acquired in all 168 fractions for all eight cases. The evaluation was performed for the values of maximum dose, minimum dose, D99, and D5-D95 for the clinical target volumes (CTVs) and mean liver dose (MLD) scaled by the prescribed dose. These dosimetric parameters were statistically compared between the planned dose distribution and the reconstructed dose distribution. The mean difference of the maximum dose was 1.3% (95% confidence interval [CI]: 0.6%-2.1%). Regarding the minimum dose, the mean difference was 0.1% (95% CI: -0.5%-0.7%). The mean differences of D99, D5-D95 and MLD were below 1%. The reliability of dose distributions for liver tumors treated with RGPT-SSPT was shown by the evaluation of the accumulated dose distributions.

A machine learning-based framework for delivery error prediction in proton pencil beam scanning using irradiation log-files

PURPOSE

This study aims to investigate the use of machine learning models for delivery error prediction in proton pencil beam scanning (PBS) delivery.

METHODS

A dataset of planned and delivered PBS spot parameters was generated from a set of 20 prostate patient treatments. Planned spot parameters (spot position, MU and energy) were extracted from the treatment planning system (TPS) for each beam. Delivered spot parameters were extracted from irradiation log-files for each beam delivery following treatment. The dataset was used as a training dataset for three machine learning models which were trained to predict delivered spot parameters based on planned parameters. K-fold cross validation was employed for hyper-parameter tuning and model selection where the mean absolute error (MAE) was used as the model evaluation metric. The model with lowest MAE was then selected to generate a predicted dose distribution for a test prostate patient within a commercial TPS.

RESULTS

Analysis of the spot position delivery error between planned and delivered values resulted in standard deviations of 0.39 mm and 0.44 mm for x and y spot positions respectively. Prediction error standard deviation values of spot positions using the selected model were 0.22 mm and 0.11 mm for x and y spot positions respectively. Finally, a three-way comparison of dose distributions and DVH values for select OARs indicates that the random-forest-predicted dose distribution within the test prostate patient was in closer agreement to the delivered dose distribution than the planned distribution.

CONCLUSIONS

PBS delivery error can be accurately predicted using machine learning techniques.

Update on yesterday's dose-Use of delivery log-files for daily adaptive proton therapy (DAPT)

In daily adaptive proton therapy (DAPT), the treatment plan is re-optimized on a daily basis. It is a straightforward idea to incorporate information from the previous deliveries during the optimization to refine this daily proton delivery. A feedback signal was used to correct for delivery errors and errors from an inaccurate dose calculation used for plan optimization. This feedback signal consisted of a dose distribution calculated with a Monte Carlo algorithm and was based on the spot delivery information from the previous deliveries in the form of log-files. We therefore called the method Update On Yesterday's Dose (UYD). The UYD method was first tested with a simulated DAPT treatment and second with dose measurements using an anthropomorphic phantom. For both, the simulations and the measurements, a better agreement between the delivered and the intended dose distribution could be observed using UYD. Gamma pass rates (1%/1 mm) increased from around 75% to above 90%, when applying the closed-loop correction for the simulations, as well as the measurements. For a DAPT treatment, positioning errors or anatomical changes are incorporated during the optimization and therefore are less dominant in the overall dose uncertainty. Hence, the relevance of algorithm or delivery machine errors even increases compared to standard therapy. The closed-loop process described here is a method to correct for these errors, and potentially further improve DAPT.

Fraction-variant beam orientation optimization for intensity-modulated proton therapy

PURPOSE

To achieve a superior balance between dosimetry and the delivery efficiency of intensity-modulated proton therapy (IMPT) using as few beams as possible in a single fraction, we optimally vary beams in different fractions.

METHODS

In the optimization, 400~800 feasible noncoplanar beams were included in the candidate pool. For each beam, the doses of all scanning spots covering the target volume and a margin were calculated. The fraction-variant beam orientation optimization (FVBOO) problem was formulated to include three terms: two quadratic dose fidelity terms to penalize the deviation of planning target volume fractional dose and organs at risk (OAR) cumulative doses from prescription, respectively; an L2,1/2-norm group sparsity term to control the number of active beams per fraction to between 1 and 4. The Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) was applied to solve this problem. FVBOO was tested on a patient with base-of-skull (BOS) tumor of 5 fractions (5f) and 30 fractions (30f) with an average number of active beams per fraction varying between 4 and 1. In addition, one bilateral head-and-neck (H&N) patient, and one esophageal cancer (ESG) patient of 30f were tested with about three active beams per fraction. The results were compared with IMPT plans that use fixed beams in each fraction. The fixed beams were selected using the group sparsity term with a fraction-invariant BOO (FIBOO) constraint.

RESULTS

Varying beams were chosen in either the 5f or 30f FVBOO plans. While similar number of beams per fraction was selected as the FIBOO plan, the FVBOO plans were able to spare the OARs better, with an average reduction of [Dmean, Dmax] from the FIBOO plans by [0.85, 2.08] Relative Biological Effective Gy (GyRBE) in the 5f plan and [1.87, 4.06] GyRBE in the 30f plans. While reducing the number of beams per fraction in the BOS patient, a three-beam/fraction 5f FVBOO plan performs comparably as the four-beam FIBOO plan and a two-beam/fraction 30f FVBOO plan still provides superior dosimetry.

CONCLUSION

Fraction-variant beam orientation optimization allows the utilization of a larger beam solution space for superior dose distribution in IMPT while maintaining a practical number of beams in each fraction.

Online daily adaptive proton therapy

It is recognized that the use of a single plan calculated on an image acquired some time before the treatment is generally insufficient to accurately represent the daily dose to the target and to the organs at risk. This is particularly true for protons, due to the physical finite range. Although this characteristic enables the generation of steep dose gradients, which is essential for highly conformal radiotherapy, it also tightens the dependency of the delivered dose to the range accuracy. In particular, the use of an outdated patient anatomy is one of the most significant sources of range inaccuracy, thus affecting the quality of the planned dose distribution. A plan should be ideally adapted as soon as anatomical variations occur, ideally online. In this review, we describe in detail the different steps of the adaptive workflow and discuss the challenges and corresponding state-of-the art developments in particular for an online adaptive strategy.

Accuracy assessment of the CNAO dose delivery system in the initial period of clinical activity and impact of later improvements on delivered dose distributions

PURPOSE

A retrospective analysis of the dose delivery system (DDS) performances of the initial clinical operation at CNAO (Centro Nazionale di Adroterapia Oncologica) is reported, and compared with the dose delivery accuracy following the implementation of a position feedback control.

METHODS

Log files and raw data of the DDS were analyzed for every field of patients treated with protons and carbon ions between January 2012 and April 2013 (~3800 fields). To investigate the DDS accuracy, the spot positions and the number of particles per spot measured by the DDS and prescribed by the treatment planning system were compared for each field. The impact of deviations on dose distributions was studied by comparing, through the gamma-index method, 2 three-dimensional (3D) physical dose maps (one for prescribed, one for measured data), generated by a validated dose computation software. The maximum gamma and the percentage of points with gamma ≤ 1 (passing volume) were studied as a function of the treatment day, and correlated with the deviations from the prescription in the measured number of particles and spot positions. Finally, delivered dose distributions of same treatment plans were compared before and after the implementation of a feedback algorithm for the correction of small position deviations, to study the effect on the delivery quality. A double comparison of prescribed and measured 3D maps, before and after feedback implementation, is reported and studied for a representative treatment delivered in 2012, redelivered on a polymethyl methacrylate (PMMA) block in 2018.

RESULTS

Systematic deviations of spot positions, mainly due to beam lateral offsets, were always found within 1.5 mm, with the exception of the initial clinical period. The number of particles was very stable, as possible deviations are exclusively related to the quantization error in the conversion from monitor counts to particles. For the chosen representative patient treatment, the gamma-index evaluation of prescribed and measured dose maps, before and after feedback implementation, showed a higher variability of maximum gamma for the 2012 irradiation, with respect to the reirradiation of 2018. However, the 2012 passing volume is >99.8% for the sum of all fields, which is comparable to the value of 2018, with the exception of one day with 98.2% passing volume, probably related to an instability of the accelerating system.

CONCLUSIONS

A detailed retrospective analysis of the DDS performances in the initial period of CNAO clinical activity is reported. The spot position deviations are referable to beam lateral offset fluctuations, while almost no deviation was found in the number of particles. The impact of deviations on dose distributions showed that the position feedback implementation and the increased beam control capability acquired after the first years of clinical experience led to an evident improvement in the DDS stability, evaluated in terms of gamma-index as a measure of the impact on dose distributions. However, the clinical effect of the maximum gamma variability found in the 2012 representative irradiation is mitigated by averaging along the number of fractions, and the high percentage of passing volumes confirmed the accuracy of the delivery even before the feedback implementation.

Feasibility study of a plastic scintillating plate-based treatment beam fluence monitoring system for use in pencil beam scanning proton therapy

PURPOSE

The purpose of this study was to describe a plastic scintillating plate-based gantry-attachable dosimetry system for pencil beam scanning proton therapy to monitor entrance proton beam fluence, and to evaluate the dosimetric characteristics of this system and its feasibility for clinical use.

METHODS

The dosimetry system, consisting of a plastic scintillating plate and a CMOS camera, was attached to a dedicated scanning nozzle and scintillation during proton beam irradiation was recorded. Dose distribution was calculated from the accumulated recorded frames. The dosimetric characteristics (energy dependency, dose linearity, dose rate dependency, and reproducibility) of the gantry-attachable dosimetry system for use with therapeutic proton beams were measured, and the feasibility of this system during clinical use was evaluated by determining selected quality assurance items at our institution.

RESULTS

The scintillating plate shortened the range of the proton beam by the water-equivalent thickness of the plate and broadened the spatial profile of the single proton spot by 11% at 70 MeV. The developed system functioned independently of the beam energy (<1.3%) and showed dose linearity, and also functioned independently of the dose rate. The feasibility of the system for clinical use was evaluated by comparing the measured quality assurance dose distribution to that of the treatment planning system. The gamma passing rate with a criterion of 3%/3 mm was 97.58%.

CONCLUSIONS

This study evaluated the dosimetric characteristics of a plastic scintillating plate-based dosimetry system for use with scanning proton beams. The ability to account for the interference of the dosimetry system on the therapeutic beam enabled offline monitoring of the entrance beam fluence of the pencil beam scanning proton therapy independent of the treatment system with high resolution and in a cost-effective manner.

Dynamic beam current control for improved dose accuracy in PBS proton therapy

The step-and-shoot method of pencil beam scanning delivers the dose on a 3D grid in the target volume, with one dimension defined by the proton energy. While the dose per pencil beam may vary substantially within an iso-energy layer, the beam current typically remains constant. In this static operation mode, the inherent latency of the beam switch-off mechanism results in a lower limit for the deliverable spot dose, which may prevent the application of some of the low-weighted spots prescribed by the treatment planning system. To overcome this limitation, we introduced dynamic beam current control at the PSI Gantry 2, an innovative new approach successfully commissioned and in clinical operation since fall 2017. The control system was enhanced with a direct link to the vertical deflector located at the centre of the cyclotron. This connection allows much faster beam current changes (~0.1 ms) and hence opens up the possibility of dynamically reducing the current for individual low-dose spots. We demonstrate that with this new dynamic operation mode, all spots are delivered as planned without compromising treatment time. We show by two independent and complementary methods that the delivered dose distribution is improved.

Impact of machine log-files uncertainties on the quality assurance of proton pencil beam scanning treatment delivery

Irradiation log-files store useful information about the plan delivery, and together with independent Monte Carlo dose engine calculations can be used to reduce the time needed for patient-specific quality assurance (PSQA). Nonetheless, machine log-files carry an uncertainty associated to the measurement of the spot position and intensity that can influence the correct evaluation of the quality of the treatment delivery. This work addresses the problem of the inclusion of these uncertainties for the final verification of the treatment delivery. Dedicated measurements performed in an IBA Proteus Plus gantry with a pencil beam scanning (PBS) dedicated nozzle have been carried out to build a 'room-dependent' model of the spot position uncertainties. The model has been obtained through interpolation of the look-up tables describing the systematic and random uncertainties, and it has been tested for a clinical case of a brain cancer patient irradiated in a dry-run. The delivered dose has been compared with the planned dose with the inclusion of the errors obtained applying the model. Our results suggest that the accuracy of the treatment delivery is higher than the spot position uncertainties obtained from the log-file records. The comparison in terms of DVHs shows that the log-reconstructed dose is compatible with the planned dose within the 95% confidence interval obtained applying our model. The initial mean dose difference between the calculated dose to the patient based on the plan and recorded data is around 1%. The difference is essentially due to the log-file uncertainties and it can be removed with a correct treatment of these errors. In conclusion our new PSQA protocol allows for a fast verification of the dose delivered after every treatment fraction through the use of machine log-files and an independent Monte Carlo dose engine. Moreover, the inclusion of log-file uncertainties in the dose calculation allows for a correct evaluation of the quality of the treatment plan delivery.

Highly efficient and sensitive patient-specific quality assurance for spot-scanned proton therapy

The purpose of this work was to develop an end-to-end patient-specific quality assurance (QA) technique for spot-scanned proton therapy that is more sensitive and efficient than traditional approaches. The patient-specific methodology relies on independently verifying the accuracy of the delivered proton fluence and the dose calculation in the heterogeneous patient volume. A Monte Carlo dose calculation engine, which was developed in-house, recalculates a planned dose distribution on the patient CT data set to verify the dose distribution represented by the treatment planning system. The plan is then delivered in a pre-treatment setting and logs of spot position and dose monitors, which are integrated into the treatment nozzle, are recorded. A computational routine compares the delivery log to the DICOM spot map used by the Monte Carlo calculation to ensure that the delivered parameters at the machine match the calculated plan. Measurements of dose planes using independent detector arrays, which historically are the standard approach to patient-specific QA, are not performed for every patient. The nozzle-integrated detectors are rigorously validated using independent detectors in regular QA intervals. The measured data are compared to the expected delivery patterns. The dose monitor reading deviations are reported in a histogram, while the spot position discrepancies are plotted vs. spot number to facilitate independent analysis of both random and systematic deviations. Action thresholds are linked to accuracy of the commissioned delivery system. Even when plan delivery is acceptable, the Monte Carlo second check system has identified dose calculation issues which would not have been illuminated using traditional, phantom-based measurement techniques. The efficiency and sensitivity of our patient-specific QA program has been improved by implementing a procedure which independently verifies patient dose calculation accuracy and plan delivery fidelity. Such an approach to QA requires holistic integration and maintenance of patient-specific and patient-independent QA.

Log file based Monte Carlo calculations for proton pencil beam scanning therapy

Patient specific quality assurance is crucial to guarantee safety in proton pencil beam scanning. In current clinical practice, this requires extensive, time consuming measurements. Additionally, these measurements do not consider the influence of density heterogeneities in the patient and are insensitive to delivery errors. In this work, we investigate the use of log file based Monte Carlo calculations for dose reconstructions in the patient CT, which takes the combined influence of calculational and delivery errors into account. For one example field, 87%/90% of the voxels agree within  ±3% when taking either calculational or delivery uncertainties into account (analytical versus Monte Carlo calculation/Monte Carlo from planned versus Monte Carlo from log file). 78% agree when considering both uncertainties simultaneously (nominal field versus Monte Carlo from log files). We then show the application of the log file based Monte Carlo calculations as a patient specific quality assurance tool for a set of five patients (16 fields) treated for different indications. For all fields, absolute dose scaling factors based on the log file Monte Carlo agree within  ±3% to the measurement based absolute dose scaling. Relative comparison shows that more than 90% of the voxels agree within  ±  5% between the analytical calculated plan and the Monte Carlo based on log files. The log file based Monte Carlo approach is an end-to-end test incorporating all requirements of patient specific quality assurance. It has the potential to reduce the workload and therefore to increase the patient throughput, while simultaneously enabling more accurate dose verification directly in the patient geometry.

Automation of routine elements for spot-scanning proton patient-specific quality assurance

PURPOSE

At our institution, all proton patient plans undergo patient-specific quality assurance (PSQA) prior to treatment delivery. For intensity-modulated proton beam therapy, quality assurance is complex and time consuming, and it may involve multiple measurements per field. We reviewed our PSQA workflow and identified the steps that could be automated and developed solutions to improve efficiency.

METHODS

We used the treatment planning system's (TPS) capability to support C# scripts to develop an Eclipse scripting application programming interface (ESAPI) script and automate the preparation of the verification phantom plan for measurements. A local area network (LAN) connection between our measurement equipment and shared database was established to facilitate equipment control, measurement data transfer, and storage. To improve the analysis of the measurement data, a Python script was developed to automatically perform a 2D-3D γ-index analysis comparing measurements in the plane of a two-dimensional detector array with TPS predictions in a water phantom for each acquired measurement.

RESULTS

Device connection via LAN granted immediate access to the plan and measurement information for downstream analysis using an online software suite. Automated scripts applied to verification plans reduced time from preparation steps by at least 50%; time reduction from automating γ-index analysis was even more pronounced, dropping by a factor of 10. On average, we observed an overall time savings of 55% in completion of the PSQA per patient plan.

CONCLUSIONS

The automation of the routine tasks in the PSQA workflow significantly reduced the time required per patient, reduced user fatigue, and frees up system users from routine and repetitive workflow steps allowing increased focus on evaluating key quality metrics.

Fast robust dose calculation on GPU for high-precision 1H, 4He, 12C and 16O ion therapy: the FRoG platform

Radiotherapy with protons and heavier ions landmarks a novel era in the field of high-precision cancer therapy. To identify patients most benefiting from this technologically demanding therapy, fast assessment of comparative treatment plans utilizing different ion species is urgently needed. Moreover, to overcome uncertainties of actual in-vivo physical dose distribution and biological effects elicited by different radiation qualities, development of a reliable high-throughput algorithm is required. To this end, we engineered a unique graphics processing unit (GPU) based software architecture allowing rapid and robust dose calculation. FRoG, Fast Recalculation on GPU, currently operates with four particle beams available at Heidelberg Ion Beam Therapy center, i.e., raster-scanning proton (¹H), helium (⁴He), carbon (¹²C) and oxygen ions (¹⁶O). FRoG enables comparative analysis of different models for estimation of physical and biological effective dose in 3D within minutes and in excellent agreement with the gold standard Monte Carlo (MC) simulation. This is a crucial step towards development of next-generation patient specific radiotherapy.

A comprehensive and efficient daily quality assurance for PBS proton therapy

There are several general recommendations for quality assurance (QA) measures, which have to be performed at proton therapy centres. However, almost each centre uses a different therapy system. In particular, there is no standard procedure for centres employing pencil beam scanning and each centre applies a specific QA program. Gantry 2 is an operating therapy system which was developed at PSI and relies on the most advanced technological innovations. We developed a comprehensive daily QA program in order to verify the main beam characteristics to assure the functionality of the therapy delivery system and the patient safety system. The daily QA program entails new hardware and software solutions for a highly efficient clinical operation. In this paper, we describe a dosimetric phantom used for verifying the most critical beam parameters and the software architecture developed for a fully automated QA procedure. The connection between our QA software and the database allows us to store the data collected on a daily basis and use it for trend analysis over longer periods of time. All the data presented here have been collected during a time span of over two years, since the beginning of the Gantry 2 clinical operation in 2013. Our procedure operates in a stable way and delivers the expected beam quality. The daily QA program takes only 20 min. At the same time, the comprehensive approach allows us to avoid most of the weekly and monthly QA checks and increases the clinical beam availability.