A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model

Evaluation of clinical parallel workflow in online adaptive MR-guided Radiotherapy: A detailed assessment of treatment session times

Introduction

Advancements in MRI-guided radiotherapy (MRgRT) enable clinical parallel workflows (CPW) for online adaptive planning (oART), allowing medical physicists (MPs), physicians (MDs), and radiation therapists (RTTs) to perform their tasks simultaneously. This study evaluates the impact of this upgrade on the total treatment time by analyzing each step of the current 0.35T-MRgRT workflow.

Methods

The time process of the workflow steps for 254 treatment fractions in 0.35 MRgRT was examined. Patients have been grouped based on disease site, breathing modality (BM) (BHI or FB), and fractionation (stereotactic body RT [SBRT] or standard fractionated long course [LC]). The time spent for the following workflow steps in Adaptive Treatment (ADP) was analyzed: Patient Setup Time (PSt), MRI Acquisition and Matching (MRt), MR Re-contouring Time (RCt), Re-Planning Time (RPt), Treatment Delivery Time (TDt). Also analyzed was the timing of treatments that followed a Simple workflow (SMP), without the online re-planning (PSt + MRt + TDt.).

Results

The time analysis revealed that the ADP workflow (median: 34 min) is significantly (p < 0.05) longer than the SMP workflow (19 min). The time required for ADP treatments is significantly influenced by TDt, constituting 40 % of the total time. The oART steps (RCt + RPt) took 11 min (median), representing 27 % of the entire procedure. Overall, 79.2 % of oART fractions were completed in less than 45 min, and 30.6 % were completed in less than 30 min.

Conclusion

This preliminary analysis, along with the comparative assessment against existing literature, underscores the potential of CPW to diminish the overall treatment duration in MRgRT-oART. Additionally, it suggests the potential for CPW to promote a more integrated multidisciplinary approach in the execution of oART.

Dose Calculation Accuracy of Beam Models in RadCalc for a 1.5 T MR-Linac

The purpose of this study is to evaluate RadCalc, an independent dose verification software, for patient-specific quality assurance (PSQA) in online adaptive planning with a magnetic resonance linear accelerator (MR-linac) of a 1.5 T. Version 7.1.4 of RadCalc to introduce the capability to establish a beam model that incorporates MR field characteristics. A total of six models were established, with one using manufacturer-provided data and the others differing in percentage depth dose (PDD) data sources. Overall, two models utilized PDD data from the treatment planning system (TPS), and three used commissioned PDD data from gantry angles of 0° and 270°. Simple tests on a virtual water phantom assessed dose-calculation accuracy, revealing percentage differences ranging from -0.5% to -20.6%. Excluding models with significant differences, clinical tests on 575 adaptive plans (prostate, liver, and breast) showed percentage differences of -0.51%, 1.12%, and 4.10%, respectively. The doses calculated using RadCalc demonstrated similar trends to those of the PSQA-based measurements. The newly released version of RadCalc enables beam modeling that considers the characteristics of the 1.5 T magnetic field. The accuracy of the software in calculating doses at 1.5 T magnetic fields has been verified, thereby making it a reliable and effective tool for PSQA in adaptive plans.

Why we should care about gas pockets in online adaptive MRgRT: a dosimetric evaluation

Introduction

Contouring of gas pockets is a time consuming step in the workflow of adaptive radiotherapy. We would like to better understand which gas pockets electronic densitiy should be used and the dosimetric impact on adaptive MRgRT treatment.

Materials and methods

21 CT scans of patients undergoing SBRT were retrospectively evaluated. Anatomical structures were contoured: Gross Tumour Volume (GTV), stomach (ST), small bowel (SB), large bowel (LB), gas pockets (GAS) and gas in each organ respectively STG, SBG, LBG. Average HU in GAS was converted in RED, the obtained value has been named as Gastrointestinal Gas RED (GIGED). Differences of average HU in GAS, STG, SBG and LBG were computed. Three treatment plans were calculated editing the GAS volume RED that was overwritten with: air RED (0.0012), water RED (1.000), GIGED, generating respectively APLAN, WPLAN and the GPLAN. 2-D dose distributions were analyzed by gamma analysis. Parameter called active gas volume (AGV) was calculated as the intersection of GAS with the isodose of 5% of prescription dose.

Results

Average HU value contained in GAS results to be equal to -620. No significative difference was noted between the average HU of gas in different organ at risk. Value of Gamma Passing Rate (GPR) anticorrelates with the AGV for each plan comparison and the threshold value for GPR to fall below 90% is 41, 60 and 139 cc for WPLANvsAPLAN, GPLANvsAPLAN and WPLANvsGPLAN respectively.

Discussions

GIGED is the right RED for Gastrointestinal Gas. Novel AGV is a useful parameter to evaluate the effect of gas pocket on dose distribution.

Development and clinical application of a GPU-based Monte Carlo dose verification module and software for 1.5 T MR-LINAC

BACKGROUND

Adaptive radiotherapy (ART) has made significant advances owing to magnetic resonance linear accelerator (MR-LINAC), which provides superior soft-tissue contrast, fast speed and rich functional magnetic resonance imaging (MRI) to guide radiotherapy. Independent dose verification plays a critical role in discovering errors, while several challenges remain in MR-LINAC.

PURPOSE

A Monte Carlo-based GPU-accelerated dose verification module for Unity is proposed and integrated into the commercial software ArcherQA to achieve fast and accurate quality assurance (QA) for online ART.

METHODS

Electron or positron motion in a magnetic field was implemented, and a material-dependent step-length limit method was used to trade off speed and accuracy. Transport was verified by dose comparison with EGSnrc in three A-B-A phantoms. Then, an accurate Monte Carlo-based Unity machine model was built in ArcherQA, including an MR-LINAC head, cryostat, coils, and treatment couch. In particular, a mixed model combining measured attenuation and homogeneous geometry was adopted for the cryostat. Several parameters in the LINAC model were tuned to commission it in the water tank. An alternating open-closed MLC plan on solid water measured with EBT-XD film was used to verify the LINAC model. Finally, the ArcherQA dose was compared with ArcCHECK measurements and GPUMCD in 30 clinical cases through the gamma test.

RESULTS

ArcherQA and EGSnrc were well matched in three A-B-A phantom tests, and the relative dose difference (RDD) was less than 1.6% in the homogenous region. A Unity model was commissioned in the water tank, and the RDD in the homogenous region was less than 2%. In the alternating open-closed MLC plan, the gamma result (3%/3 mm) between ArcherQA and Film was 96.55%, better than the gamma result between GPUMCD and Film (92.13%). In 30 clinical cases, the mean three-dimensional (3D) gamma result (3%/2 mm) was 99.36% ± 1.28% between ArcherQA and ArcCHECK for the QA plans and 99.27% ± 1.04% between ArcherQA and GPUMCD for the clinical patient plans. The average dose calculation time was 106 s in all clinical patient plans.

CONCLUSIONS

A GPU-accelerated Monte Carlo-based dose verification module was developed and built for the Unity MR-LINAC. The fast speed and high accuracy were proven by comparison with EGSnrc, commission data, the ArcCHECK measurement dose, and the GPUMCD dose. This module can achieve fast and accurate independent dose verification for Unity.

GPU-accelerated Monte Carlo simulation of electron and photon interactions for radiotherapy applications

Objective. The Monte Carlo simulation software is a valuable tool in radiation therapy, in particular to achieve the needed accuracy in the dose evaluation for the treatment plans optimisation. The current challenge in this field is the time reduction to open the way to many clinical applications for which the computational time is an issue. In this manuscript we present an innovative GPU-accelerated Monte Carlo software for dose valuation in electron and photon based radiotherapy, developed as an update of the FRED (Fast paRticle thErapy Dose evaluator) software.Approach. The code transports particles through a 3D voxel grid, while scoring their energy deposition along their trajectory. The models of electromagnetic interactions in the energy region between 1 MeV-1 GeV available in literature have been implemented to efficiently run on GPUs, allowing to combine a fast tracking while keeping high accuracy in dose assessment. The FRED software has been bench-marked against state-of-art full MC (FLUKA, GEANT4) in the realm of two different radiotherapy applications: Intra-Operative Radio Therapy and Very High Electron Energy radiotherapy applications.Results. The single pencil beam dose-depth profiles in water as well as the dose map computed on non-homogeneous phantom agree with full-MCs at 2% level, observing a gain in processing time from 200 to 5000.Significance. Such performance allows for computing a plan with electron beams in few minutes with an accuracy of ∼%, demonstrating the FRED potential to be adopted for fast plan re-calculation in photon or electron radiotherapy applications.

An independent Monte Carlo-based IMRT QA tool for a 0.35 T MRI-guided linear accelerator

PURPOSE

To develop an independent log file-based intensity-modulated radiation therapy (IMRT) quality assurance (QA) tool for the 0.35 T magnetic resonance-linac (MR-linac) and investigate the ability of various IMRT plan complexity metrics to predict the QA results. Complexity metrics related to tissue heterogeneity were also introduced.

METHODS

The tool for particle simulation (TOPAS) Monte Carlo code was utilized with a previously validated linac head model. A cohort of 29 treatment plans was selected for IMRT QA using the developed QA tool and the vendor-supplied adaptive QA (AQA) tool. For 27 independent patient cases, various IMRT plan complexity metrics were calculated to assess the deliverability of these plans. A correlation between the gamma pass rates (GPRs) from the AQA results and calculated IMRT complexity metrics was determined using the Pearson correlation coefficients. Tissue heterogeneity complexity metrics were calculated based on the gradient of the Hounsfield units.

RESULTS

The median and interquartile range for the TOPAS GPRs (3%/3 mm criteria) were 97.24% and 3.75%, respectively, and were 99.54% and 0.36% for the AQA tool, respectively. The computational time for TOPAS ranged from 4 to 8 h to achieve a statistical uncertainty of <1.5%, whereas the AQA tool had an average calculation time of a few minutes. Of the 23 calculated IMRT plan complexity metrics, the AQA GPRs had correlations with 7 out of 23 of the calculated metrics. Strong correlations (|r| > 0.7) were found between the GPRs and the heterogeneity complexity metrics introduced in this work.

CONCLUSIONS

An independent MC and log file-based IMRT QA tool was successfully developed and can be clinically deployed for offline QA. The complexity metrics will supplement QA reports and provide information regarding plan complexity.

A deep learning approach to generate synthetic CT in low field MR-guided radiotherapy for lung cases

INTRODUCTION

This study aims to apply a conditional Generative Adversarial Network (cGAN) to generate synthetic Computed Tomography (sCT) from 0.35 Tesla Magnetic Resonance (MR) images of the thorax.

METHODS

Sixty patients treated for lung lesions were enrolled and divided into training (32), validation (8), internal (10,T_A) and external (10,T_B) test set. Image accuracy of generated sCT was evaluated computing the mean absolute (MAE) and mean error (ME) with respect the original CT. Three treatment plans were calculated for each patient considering MRI as reference image: original CT, sCT (pure sCT) and sCT with GTV density override (hybrid sCT) were used as Electron Density (ED) map. Dose accuracy was evaluated comparing treatment plans in terms of gamma analysis and Dose Volume Histogram (DVH) parameters.

RESULTS

No significant difference was observed between the test sets for image and dose accuracy parameters. Considering the whole test cohort, a MAE of 54.9 ± 10.5 HU and a ME of 4.4 ± 7.4 HU was obtained. Mean gamma passing rates for 2%/2mm, and 3%/3mm tolerance criteria were 95.5 ± 5.9% and 98.2 ± 4.1% for pure sCT, 96.1 ± 5.1% and 98.5 ± 3.9% for hybrid sCT: the difference between the two approaches was significant (p = 0.01). As regards DVH analysis, differences in target parameters estimation were found to be within 5% using hybrid approach and 20% using pure sCT.

CONCLUSION

The DL algorithm here presented can generate sCT images in the thorax with good image and dose accuracy, especially when the hybrid approach is used. The algorithm does not suffer from inter-scanner variability, making feasible the implementation of MR-only workflows for palliative treatments.

A phase space model of a Versa HD linear accelerator for application to Monte Carlo dose calculation in a real-time adaptive workflow

PURPOSE

This study aims to develop and validate a simple geometric model of the accelerator head, from which a particle phase space can be calculated for application to fast Monte Carlo dose calculation in real-time adaptive photon radiotherapy. With this objective in view, the study investigates whether the phase space model can facilitate dose calculations which are compatible with those of a commercial treatment planning system, for convenient interoperability.

MATERIALS AND METHODS

A dual-source model of the head of a Versa HD accelerator (Elekta AB, Stockholm, Sweden) was created. The model used parameters chosen to be compatible with those of 6-MV flattened and 6-MV flattening filter-free photon beams in the RayStation treatment planning system (RaySearch Laboratories, Stockholm, Sweden). The phase space model was used to calculate a photon phase space for several treatment plans, and the resulting phase space was applied to the Dose Planning Method (DPM) Monte Carlo dose calculation algorithm. Simple fields and intensity-modulated radiation therapy (IMRT) treatment plans for prostate and lung were calculated for benchmarking purposes and compared with the convolution-superposition dose calculation within RayStation.

RESULTS

For simple square fields in a water phantom, the calculated dose distribution agrees to within ±2% with that from the commercial treatment planning system, except in the buildup region, where the DPM code does not model the electron contamination. For IMRT plans of prostate and lung, agreements of ±2% and ±6%, respectively, are found, with slightly larger differences in the high dose gradients.

CONCLUSIONS

The phase space model presented allows convenient calculation of a phase space for application to Monte Carlo dose calculation, with straightforward translation of beam parameters from the RayStation beam model. This provides a basis on which to develop dose calculation in a real-time adaptive setting.

Towards efficient Monte Carlo N-Particle simulation of a positron emission tomography (PET) via source volume definition

Monte Carlo N-Particle (MCNP) simulation has been extensively proven in nuclear medicine imaging systems, most notably in designing and optimizing new medical imaging tools. It enables more complicated geometries and the simulation of particles passing through and interacting with materials. However, a relatively long simulation time is a drawback of Monte Carlo simulation, mainly when complex geometry exists. The current study presents an alternative variance reduction technique for a modeled positron emission tomography (PET) camera by reducing the height of the source volume definition while maintaining the geometry of the simulated model. The National Electrical Manufacturers Association (NEMA) of the International Electrotechnical Commission (IEC) PET's phantom was used with a 1 cm diameter and 7 cm height of line source placed in the middle. The first geometry was fully filled the line source with 0.50 mCi radioactivity. In contrast, the second geometry decreased the source definition to 2.4 cm in height, covering 1 cm above and below the sub-block detector level. The source volume definition approach led to a 71% reduction in the total photons to be simulated. Results showed that the proposed variance reduction strategy could produce spatial resolution as precise as fully filled geometry and sped up the simulation time by approximately 65%. Hence, this strategy can be utilized for further PET optimizing simulation studies.

Development of a GPU-superposition Monte Carlo code for fast dose calculation in magnetic fields

Objective. To develop and validate a graphics processing unit (GPU) based superposition Monte Carlo (SMC) code for efficient and accurate dose calculation in magnetic fields. Approach. A series of mono-energy photons ranging from 25 keV to 7.7 MeV were simulated with EGSnrc in a water phantom to generate particle tracks database. SMC physics was extended with charged particle transport in magnetic fields and subsequently programmed on GPU as gSMC. Optimized simulation scheme was designed by combining variance reduction techniques to relieve the thread divergence issue in general GPU-MC codes and improve the calculation efficiency. The gSMC code’s dose calculation accuracy and efficiency were assessed through both phantoms and patient cases. Main results. gSMC accurately calculated the dose in various phantoms for both B = 0 T and B = 1.5 T, and it matched EGSnrc well with a root mean square error of less than 1.0% for the entire depth dose region. Patient cases validation also showed a high dose agreement with EGSnrc with 3D gamma passing rate (2%/2 mm) large than 97% for all tested tumor sites. Combined with photon splitting and particle track repeating techniques, gSMC resolved the thread divergence issue and showed an efficiency gain of 186–304 relative to EGSnrc with 10 CPU threads. Significance. A GPU-superposition Monte Carlo code called gSMC was developed and validated for dose calculation in magnetic fields. The developed code’s high calculation accuracy and efficiency make it suitable for dose calculation tasks in online adaptive radiotherapy with MR-LINAC.

Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy

Objective.Next generation online and real-time adaptive radiotherapy workflows require precise particle transport simulations in sub-second times, which is unfeasible with current analytical pencil beam algorithms (PBA) or Monte Carlo (MC) methods. We present a deep learning based millisecond speed dose calculation algorithm (DoTA) accurately predicting the dose deposited by mono-energetic proton pencil beams for arbitrary energies and patient geometries.Approach.Given the forward-scattering nature of protons, we frame 3D particle transport as modeling a sequence of 2D geometries in the beam's eye view. DoTA combines convolutional neural networks extracting spatial features (e.g. tissue and density contrasts) with a transformer self-attention backbone that routes information between the sequence of geometry slices and a vector representing the beam's energy, and is trained to predict low noise MC simulations of proton beamlets using 80 000 different head and neck, lung, and prostate geometries.Main results.Predicting beamlet doses in 5 ± 4.9 ms with a very high gamma pass rate of 99.37 ± 1.17% (1%, 3 mm) compared to the ground truth MC calculations, DoTA significantly improves upon analytical pencil beam algorithms both in precision and speed. Offering MC accuracy 100 times faster than PBAs for pencil beams, our model calculates full treatment plan doses in 10-15 s depending on the number of beamlets (800-2200 in our plans), achieving a 99.70 ± 0.14% (2%, 2 mm) gamma pass rate across 9 test patients.Significance.Outperforming all previous analytical pencil beam and deep learning based approaches, DoTA represents a new state of the art in data-driven dose calculation and can directly compete with the speed of even commercial GPU MC approaches. Providing the sub-second speed required for adaptive treatments, straightforward implementations could offer similar benefits to other steps of the radiotherapy workflow or other modalities such as helium or carbon treatments.

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

PURPOSE

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.

METHODS

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.

RESULTS

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 6 MeV 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 0.1 cm. For patient plan verification, the maximum relative average dose difference was 1.3%. The gamma passing rates with criteria 3% (2 mm) 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.

CONCLUSIONS

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.

Automatic 3D Monte-Carlo-based secondary dose calculation for online verification of 1.5 T magnetic resonance imaging guided radiotherapy

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First implementation of an independent 3D-secondary dose calculation (3D-SDC).

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Validation of the 3D-SDC solution using patient plans and experimental plan QA.

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Online SDC of central targets is feasible with a median calculation time of 1:23 min.

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Peripheral targets with small beam numbers need alternative validation strategies.

Background and purpose

Hybrid magnetic resonance linear accelerator (MR-Linac) systems represent a novel technology for online adaptive radiotherapy. 3D secondary dose calculation (SDC) of online adapted plans is required to assure patient safety. Currently, no 3D-SDC solution is available for 1.5T MR-Linac systems. Therefore, the aim of this project was to develop and validate a method for online automatic 3D-SDC for adaptive MR-Linac treatments.

Materials and methods

An accelerator head model was designed for an 1.5T MR-Linac system, neglecting the magnetic field. The use of this model for online 3D-SDC of MR-Linac plans was validated in a three-step process: (1) comparison to measured beam data, (2) investigation of performance and limitations in a planning phantom and (3) clinical validation using n = 100 patient plans from different tumor entities, comparing the developed 3D-SDC with experimental plan QA.

Results

The developed model showed median gamma passing rates compared to MR-Linac base data of 84.7%, 100% and 99.1% for crossplane, inplane and depth-dose-profiles, respectively. Comparison of 3D-SDC and full dose calculation in a planning phantom revealed that with ⩾5 beams gamma passing rates >95% can be achieved for central target locations. With a median calculation time of 1:23 min, 3D-SDC of online adapted clinical MR-Linac plans demonstrated a median gamma passing rate of 98.9% compared to full dose calculation, whereas experimental plan QA reached 99.5%.

Conclusion

Here, we describe the first technical 3D-SDC solution for online adaptive MR-guided radiotherapy. For clinical situations with peripheral targets and a small number of beams additional verification appears necessary. Further improvement may include 3D-SDC with consideration of the magnetic field.

Delivery of online adaptive magnetic resonance guided radiotherapy based on isodose boundaries

Magnetic Resonance-guided Radiotherapy (MRgRT) allows direct monitoring of treated volumes. The aim of this study was to investigate the feasibility of a new gating strategy consisting in using an isodose as boundary. Forty-four patients treated for thoracic and abdominal lesions using MRgRT were enrolled. The accuracy of the new strategy was compared to the conventional one in terms of area improvement available for gating without compromising target coverage. A mean increase of 24% for lung, 15% for liver and 11% for pancreas was observed, demonstrating how the new method can be useful in challenging situations with low dose conformality.

Dosimetric accuracy of dual isocenter irradiation in low magnetic field resonance guided radiotherapy system for extended abdominal tumours

PURPOSE

Due to limited field size of Magnetic Resonance Linear Accelerators (MR-Linac), some treatments could require a dual-isocenter planning approach to achieve a complete target coverage and thus exploit the benefits of the online adaptation. This study evaluates the dosimetric accuracy of the dual-isocenter intensity modulated radiation therapy (IMRT) delivery technique for MR-Linac.

MATERIAL AND METHODS

Dual-isocenter multi leaf collimator (MLC) and couch accuracy tests have been performed to evaluate the delivery accuracy of the system. A mono-isocenter plan delivered in clinical practice has then been retrospectively re-planned with dual-isocenter technique. The dual-isocenter plan has been re-calculated and delivered on a 3-dimensional (3D) ArcCHECK phantom and 2-dimensional (2D) films to assess its dosimetric accuracy in terms of gamma analysis. Clinical and planning target volume (CTV and PTV respectively) coverage robustness was then investigated after the introduction of ± 2 mm and ± 5 mm positioning errors by shifting the couch.

RESULTS

MLC and couch accuracy tests confirmed the system accuracy in delivering a dual-isocenter irradiation. 2D/3D gamma analysis results occurred always to be above 95% if considered a gamma criteria 1%/2 mm and 1%/1 mm respectively for the 2D and 3D analysis. The mean variations for CTV D98% and PTV V95% were 0.2% and 1.1% respectively when positioning error was introduced separately in each direction, while the maximum observed variations were 0.9% (CTV) and 3.7% (PTV).

CONCLUSION

The dosimetric accuracy of dual-isocenter irradiation has been verified for MR-Linac, achieving accurate and robust treatment strategy and improving dose conformality also in presence of targets whose extension exceeds the nominal maximum field size.

Machine QA for the Elekta Unity system: A Report from the Elekta MR-linac consortium

Over the last few years, magnetic resonance image‐guided radiotherapy systems have been introduced into the clinic, allowing for daily online plan adaption. While quality assurance (QA) is similar to conventional radiotherapy systems, there is a need to introduce or modify measurement techniques. As yet, there is no consensus guidance on the QA equipment and test requirements for such systems. Therefore, this report provides an overview of QA equipment and techniques for mechanical, dosimetric, and imaging performance of such systems and recommendation of the QA procedures, particularly for a 1.5T MR‐linac device. An overview of the system design and considerations for QA measurements, particularly the effect of the machine geometry and magnetic field on the radiation beam measurements is given. The effect of the magnetic field on measurement equipment and methods is reviewed to provide a foundation for interpreting measurement results and devising appropriate methods. And lastly, a consensus overview of recommended QA, appropriate methods, and tolerances is provided based on conventional QA protocols. The aim of this consensus work was to provide a foundation for QA protocols, comparative studies of system performance, and for future development of QA protocols and measurement methods.

Development and evaluation of a GEANT4-based Monte Carlo Model of a 0.35 T MR-guided radiation therapy (MRgRT) linear accelerator

PURPOSE

The aim of this work was to develop and benchmark a magnetic resonance (MR)-guided linear accelerator head model using the GEANT4 Monte Carlo (MC) code. The validated model was compared to the treatment planning system (TPS) and was also used to quantify the electron return effect (ERE) at a lung-water interface.

METHODS

The average energy, including the spread in the energy distribution, and the radial intensity distribution of the incident electron beam were iteratively optimized in order to match the simulated beam profiles and percent depth dose (PDD) data to measured data. The GEANT4 MC model was then compared to the TPS model using several photon beam tests including oblique beams, an off-axis aperture, and heterogeneous phantoms. The benchmarked MC model was utilized to compute output factors (OFs) with the 0.35 T magnetic field turned on and off. The ERE was quantified at a lung-water interface by simulating PDD curves with and without the magnetic field for 6.6 × 6.6  cm 2 and 2.5 × 2.5  cm 2 field sizes. A 2%/2 mm gamma criterion was used to compare the MC model with the TPS data throughout this study.

RESULTS

The final incident electron beam parameters were 6.0 MeV average energy with a 1.5 MeV full width at half maximum (FWHM) Gaussian energy spread and a 1.0 mm FWHM Gaussian radial intensity distribution. The MC-simulated OFs were found to be in agreement with the TPS-calculated and measured OFs, and no statistical difference was observed between the 0.35 T and 0.0 T OFs. Good agreement was observed between the TPS-calculated and MC-simulated data for the photon beam tests with gamma pass rates ranging from 96% to 100%. An increase of 4.3% in the ERE was observed for the 6.6 × 6.6  cm 2 field size relative to the 2.5 × 2.5  cm 2 field size. The ratio of the 0.35 T PDD to the 0.0 T PDD was found to be up to 1.098 near lung-water interfaces for the 6.6 × 6.6  cm 2 field size using the MC model.

CONCLUSIONS

A vendor-independent Monte Carlo model has been developed and benchmarked for a 0.35 T/6 MV MR-linac. Good agreement was obtained between the GEANT4 and TPS models except near heterogeneity interfaces.

Technical Note: Cumulative dose modeling for organ motion management in MRI-guided radiation therapy

PURPOSE

To develop a method for continuous online dose accumulation during irradiation in MRI-guided radiation therapy (MRgRT) and to demonstrate its application in evaluating the impact of internal organ motion on cumulative dose.

METHODS

An intensity-modulated radiation therapy (IMRT) treatment plan is partitioned into its unique apertures. Dose for each planned aperture is calculated using Monte Carlo dose simulation on each phase of a four-dimensional computed tomography (4D-CT) dataset. Deformable image registration is then performed both (a) between each frame of a cine-MRI acquisition obtained during treatment and a reference frame, and (b) between each volume of the 4D-CT phases and a reference phase. These registrations are used to associate each cine image with a 4D-CT phase. Additionally, for each 4D-CT phase, the deformation vector field (DVF) is used to warp the pre-calculated dose volumes per aperture onto the reference CT dataset. To estimate the dose volume delivered during each frame of the cine-MRI acquisition, we retrieve the pre-calculated warped dose volume for the delivered aperture on the associated 4D-CT phase and adjust it by a rigid translation to account for baseline drift and instances where motion on the cine image exceeds the amplitude observed between 4D-CT phases.

RESULTS

The proposed dose accumulation method is retrospectively applied to a liver cancer case previously treated on an MRgRT platform. Cumulative dose estimated for free-breathing and respiration-gated delivery is compared against dose calculated on static anatomy. In this sample case, the target minimum dose and D 98 varied by as much as 5% and 7%, respectively.

CONCLUSION

We demonstrate a technique suitable for continuous online dose accumulation during MRgRT. In contrast to other approaches, dose is pre-calculated per aperture and phase and then retrieved based on a mapping scheme between cine MRI and 4D-CT datasets, aiming at reducing the computational burden for potential real-time applications.

Case Report: First in Human Online Adaptive MR Guided SBRT of Peritoneal Carcinomatosis Nodules: A New Therapeutic Approach for the Oligo-Metastatic Patient

Peritoneal carcinosis (PC) is characterized by poor prognosis. PC is currently treated as a locoregional disease and the possibility to perform very precise treatments such as stereotactic body radiation therapy (SBRT) has opened up new therapeutic perspectives. More recently, the introduction of Magnetic Resonance-guided Radiation Therapy (MRgRT) allowed online adaptation (OA) of treatment plan to optimize daily dose distribution based on patient’s anatomy. The aim of this study is the evaluation of the effectiveness of SBRT OA workflow in an oligometastatic patient affected by PC. We report the clinical case of a patient affected by PC originating from colon cancer, previously treated with chemotherapy and surgery, addressed to OA SBRT treatment on a single chemoresistant PC nodule, delivered with a 0.35 T MR Linac. Treatment was delivered using gating approach in deep inspiration breath hold condition in order to reduce intrafraction variability. Prescription dose was 35 Gy in 5 fractions. The PTV V95% of the original plan was 96.6%, while the predicted values for the following fractions were 11.9, 56.4, 0, 0, and 61%. Similarly, the small bowel V19.5 Gy of the original plan was 4.63 cc, while the predicted values for the following fractions were 3.7, 8.6, 10.7, 1.96, 3.7 cc. Thanks to the OA approach, the re-optimized PTV V95% coverage improved to 96.1, 89.0, 85.5, 94.5, and 94%; while the small bowel V19.5 Gy to 3.36; 3.28; 1.84; 2.62; 2.6 cc respectively. After the end of RT, the patient was addressed to follow-up, and the re-evaluation ¹⁸F-FDG PET-CT was performed after 10 months from irradiation showed complete response. No acute or late toxicities were recorded. MRgRT with OA approach in PC patients is technically and clinically feasible with clean toxicity result. Online adaptive SBRT for oligometastases opens up new therapeutic scenarios in the management of this category of patients.

Feasibility of using a commercial collapsed cone dose engine for 1.5T MR-LINAC online independent dose verification

PURPOSE

To validate the feasibility and accuracy of commonly used collapsed cone (CC) dose engine for Elekta Unity 1.5T MR-LINAC online independent dose verification.

MATERIALS AND METHODS

The Unity beam model was built and commissioned in RayStation treatment planning system with CC dose engine. Four AAPM TG-119 test plans were created and measured with ArcCHECK phantom for comparison, another thirty patient plans from six tumor sites were also included. The dosimetric criteria for various ROIs and 3D gamma passing rates were quantitatively evaluated, and the effects of magnetic field and dose deposition type on the dose difference between two systems were further analyzed.

RESULTS

ArcCHECK based measurement showed a clear magnetic field induced profile shift between CC with both measurement and GPUMCD. For clinical plans, gamma passing rates with criteria (3%, 3 mm) between GPUMCD and CC large than 90% can be achieved for most tumor sites except esophagus and lung cases, the mean dose difference of 3% can be satisfied for most ROIs from all tumor sites. The magnetic field caused a large dose impact on low density areas, the average gamma passing rates were improved from 85.54% to 96.43% and 87.40% to 99.54% for esophagus and lung cases when the magnetic field effect was excluded.

CONCLUSIONS

It is feasible to use CC dose engine as a secondary dose calculation tool for Elekta Unity system for most tumor sites, while the accuracy is limited and should be used carefully for low density areas, such as esophagus and lung cases.

Using prediction models to evaluate magnetic resonance image guided radiation therapy plans

Comprehensive analysis of daily, online adaptive plan quality and safety in magnetic resonance imaging (MRI) guided radiation therapy is critical to its widespread use. Artificial neural network models developed with offline plans created after simulation were used to analyze and compare online plans that were adapted and reoptimized in real time prior to treatment. Roughly one third of ⁶⁰Co adapted plans were of inferior quality relative to fully optimized, offline plans, but MRI-linac adapted plans were essentially equivalent to offline plans. The models also enabled clear justification that MRI-linac plans are superior to ⁶⁰Co in an overwhelming majority of cases.

Time Analysis of Online Adaptive Magnetic Resonance-Guided Radiation Therapy Workflow According to Anatomical Sites

PURPOSE

To document time analysis of detailed workflow steps for the online adaptive magnetic resonance-guided radiation therapy treatments (MRgRT) with the ViewRay MRIdian system and to identify the barriers to and solutions for shorter treatment times.

METHODS AND MATERIALS

A total of 154 patients were treated with the ViewRay MRIdian system between September 2018 and October 2019. The time process of MRgRT workflow steps of 962 fractions for 166 treatment sites was analyzed in terms of patient and online adaptive treatment (ART) characteristics.

RESULTS

Overall, 774 of 962 fractions were treated with online ART, and 83.2% of adaptive fractions were completed in less than 60 minutes. Sixty-three percent, 50.3%, and 4.2% of fractions were completed in less than 50 minutes, 45 minutes, and 30 minutes, respectively. Eight-point-three percent and 3% of fractions were completed in more than 70 minutes and 80 minutes, respectively. The median time (t_med) for ART workflow steps were as follows: (1) setup t_med: 5.0 minutes, (2) low-resolution scanning t_med: 1 minute, (3) high-resolution scanning t_med: 3 minutes, (4) online contouring t_med: 9 minutes, (5) reoptimization with online quality assurance t_med: 5 minutes, (6) real targeting t_med: 3 minutes, (7) beam delivery with gating t_med: 17 minutes, and (8) net total treatment time t_med: 45 minutes. The shortest and longest t_mean rates of net total treatment time were 41.59 minutes and 64.43 minutes for upper-lung-lobe-located thoracic tumors and ultracentrally located thoracic tumors, respectively.

CONCLUSIONS

To our knowledge, this is the first broad treatment-time analysis for online ART in the literature. Although treatment times are long due to human- and technology-related limitations, benefits offered by MRgRT might be clinically important. In the future, implementation of artificial intelligence segmentation, an increase in dose rate, and faster multileaf collimator and gantry speeds may lead to achieving shorter MRgRT treatments.

Development and evaluation of machine learning models for voxel dose predictions in online adaptive magnetic resonance guided radiation therapy

PURPOSE

Daily online adaptive plan quality in magnetic resonance imaging guided radiation therapy (MRgRT) is difficult to assess in relation to the fully optimized, high quality plans traditionally established offline. Machine learning prediction models developed in this work are capable of predicting 3D dose distributions, enabling the evaluation of online adaptive plan quality to better inform adaptive decision-making in MRgRT.

METHODS

Artificial neural networks predicted 3D dose distributions from input variables related to patient anatomy, geometry, and target/organ-at-risk relationships in over 300 treatment plans from 53 patients receiving adaptive, linac-based MRgRT for abdominal cancers. The models do not include any beam related variables such as beam angles or fluence and were optimized to balance errors related to raw dose and specific plan quality metrics used to guide daily online adaptive decisions.

RESULTS

Averaged over all plans, the dose prediction error and the absolute error were 0.1 ± 3.4 Gy (0.1 ± 6.2%) and 3.5 ± 2.4 Gy (6.4 ± 4.3%) respectively. Plan metric prediction errors were -0.1 ± 1.5%, -0.5 ± 2.1%, -0.9 ± 2.2 Gy, and 0.1 ± 2.7 Gy for V95, V100, D95, and Dmean respectively. Plan metric prediction absolute errors were 1.1 ± 1.1%, 1.5 ± 1.5%, 1.9 ± 1.4 Gy, and 2.2 ± 1.6 Gy. Approximately 10% (25) of the plans studied were clearly identified by the prediction models as inferior quality plans needing further optimization and refinement.

CONCLUSION

Machine learning prediction models for treatment plan 3D dose distributions in online adaptive MRgRT were developed and tested. Clinical integration of the models requires minimal effort, producing 3D dose predictions for a new patient's plan using only target and OAR structures as inputs. These models can enable improved workflows for MRgRT through more informed plan optimization and plan quality assessment in real time.

Medical physics challenges in clinical MR-guided radiotherapy

The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.

Dose uncertainty and resolution of polymer gel dosimetry using an MRI guided radiation therapy system's onboard 0.35 T scanner

Magnetic Resonance Imaging (MRI) scanners are widely used for 3D gel dosimeters readout. However, limited access to MRI scanners is a challenge in MRI-based gel dosimetry. Recent clinical implementation of MRI-guided radiation therapy machines provides potential opportunities for onboard gel dosimetry using its MRI subsystem. The objective of this study was to investigate the feasibility of gel dosimetry using ViewRay's onboard 0.35 T MRI scanner. A BANG® polymer gel dosimeter was irradiated by three beams of 3 × 3 cm2 field size. The T2 relaxation rate (R2) of the irradiated gel was measured using a Philips 1.5 T Ingenia MRI and a ViewRay 0.35 T onboard MRI and spin-echo pulse sequences. The number of signal averages (NSA) was set to 16 for the ViewRay acquisitions and one for the Philips 1.5 T MRI to achieve similar signal-to-noise ratios. The in-plane spatial resolution was 1.5 × 1.5 mm2 and the slice thickness was 5 mm. The relative dose uncertainty was obtained using R2 versus dose curves to compare the performance of dosimetry using the two different MRIs and field strengths. The dose uncertainty decreased from 12% at 2 Gy to 3.5% at 7.5 Gy at 1.5 T. The dose uncertainty decreased from 13% at 2 Gy to 4% at 7.5 Gy with NSA = 16 and 3 × 3 mm2 pixel size, and from 10.5% at 2 Gy to 3.2% at 7.5 Gy with NSA = 16 and denoised R2 maps (1.5 × 1.5 mm2 pixel size) at 0.35 T. The mean of dose resolution was 0.4 Gy at 1.5 T while the mean of dose resolution was 0.8 Gy and 0.64 Gy at 0.35 T by downsampling and denoising the R2 map, respectively. Therefore, comparable dose uncertainty was achievable using the ViewRay's onboard 0.35 T and Philips 1.5 T MRI scanners. 3D gel dosimetry using onboard low-field MRI scanner provides ViewRay users a 3D high resolution dosimetry option besides film and ionization chamber.

MOSFET dosimeter characterization in MR-guided radiation therapy (MRgRT) Linac

PURPOSE

With the increasing use of MR-guided radiation therapy (MRgRT), it becomes important to understand and explore accuracy of medical dosimeters in the presence of magnetic field. The purpose of this work is to characterize metal-oxide-semiconductor field-effect transistors (MOSFETs) in MRgRT systems at 0.345 T magnetic field strength.

METHODS

A MOSFET dosimetry system, developed by Best Medical Canada for in-vivo patient dosimetry, was used to study various commissioning tests performed on a MRgRT system, MRIdian^® Linac. We characterized the MOSFET dosimeter with different cable lengths by determining its calibration factor, monitor unit linearity, angular dependence, field size dependence, percentage depth dose (PDD) variation, output factor change, and intensity modulated radiation therapy quality assurance (IMRT QA) verification for several plans. MOSFET results were analyzed and compared with commissioning data and Monte Carlo calculations.

RESULTS

MOSFET measurements were not found to be affected by the presence of 0.345 T magnetic field. Calibration factors were similar for different cable length dosimeters either placed at the parallel or perpendicular direction to the magnetic field, with variations of less than 2%. The detector showed good linearity (R²  = 0.999) for 100-600 MUs range. Output factor measurements were consistent with ionization chamber data within 2.2%. MOSFET PDD measurements were found to be within 1% for 1-15 cm depth range in comparison to ionization chamber. MOSFET normalized angular response matched thermoluminescent detector (TLD) response within 5.5%. The IMRT QA verification data for the MRgRT linac showed that the percentage difference between ionization chamber and MOSFET was 0.91%, 2.05%, and 2.63%, respectively for liver, spine, and mediastinum.

CONCLUSION

MOSFET dosimeters are not affected by the 0.345 T magnetic field in MRgRT system. They showed physics parameters and performance comparable to TLD and ionization chamber; thus, they constitute an alternative to TLD for real-time in-vivo dosimetry in MRgRT procedures.

On the accuracy of bulk synthetic CT for MR-guided online adaptive radiotherapy

PURPOSE

MR-guided radiotherapy (MRgRT) relies on the daily assignment of a relative electron density (RED) map to allow the fraction specific dose calculation. One approach to assign the RED map consists of segmenting the daily magnetic resonance image into five different density levels and assigning a RED bulk value to each level to generate a synthetic CT (sCT). The aim of this study is to evaluate the dose calculation accuracy of this approach for applications in MRgRT.

METHODS

A planning CT (pCT) was acquired for 26 patients with abdominal and pelvic lesions and segmented in five levels similar to an online approach: air, lung, fat, soft tissue and bone. For each patient, the median RED value was calculated for fat, soft tissue and bone. Two sCTs were generated assigning different bulk values to the segmented levels on pCT: The sCT_ICRU uses the RED values recommended by ICRU46, and the sCT_tailor uses the median patient-specific RED values. The same treatment plan was calculated on two the sCTs and the pCT. The dose calculation accuracy was investigated in terms of gamma analysis and dose volume histogram parameters.

RESULTS

Good agreement was found between dose calculated on sCTs and pCT (gamma passing rate 1%/1 mm equal to 91.2% ± 6.9% for sCT_ICRU and 93.7% ± 5.3% b or sCT_tailor). The mean difference in estimating V95 (PTV) was equal to 0.2% using sCT_tailor and 1.2% using sCT_ICRU, respect to pCT values CONCLUSIONS: The bulk sCT guarantees a high level of dose calculation accuracy also in presence of magnetic field, making this approach suitable to MRgRT. This accuracy can be improved by using patient-specific RED values.

Evaluation of a simplified optimizer for MR-guided adaptive RT in case of pancreatic cancer

PURPOSE

Magnetic resonance-guided adaptive radiotherapy (MRgART) is considered a promising resource for pancreatic cancer, as it allows to online modify the dose distribution according to daily anatomy. This study aims to compare the dosimetric performance of a simplified optimizer implemented on a MR-Linac treatment planning system (TPS) with those obtained using an advanced optimizer implemented on a conventional Linac.

METHODS

Twenty patients affected by locally advanced pancreatic cancer (LAPC) were considered. Gross tumor volume (GTV) and surrounding organ at risks (OARs) were contoured on the average 4DCT scan. Planning target volume was generated from GTV by adding an isotropic 3 mm margin and excluding overlap areas with OARs. Treatment plans were generated by using the simple optimizer for the MR-Linac in intensity-modulated radiation therapy (IMRT) and the advanced optimizer for conventional Linac in IMRT and volumetric modulated arc therapy (VMAT) technique. Prescription dose was 40 Gy in five fractions. The dosimetric comparison was performed on target coverage, dosimetric indicators, and low dose diffusion.

RESULTS

The simplified optimizer of MR-Linac generated clinically acceptable plans in 80% and optimal plans in 55% of cases. The number of clinically acceptable plans obtained using the advanced optimizer of the conventional Linac with IMRT was the same of MR-Linac, but the percentage of optimal plans was higher (65%). Using the VMAT technique, it is possible to obtain clinically acceptable plan in 95% and optimal plans in 90% of cases. The advanced optimizer combined with VMAT technique ensures higher target dose homogeneity and minor diffusion of low doses, but its actual optimization time is not suitable for MRgART.

CONCLUSION

Simplified optimization solutions implemented in the MR-Linac TPS allows to elaborate in most of cases treatment plans dosimetrically comparable with those obtained by using an advanced optimizer. A superior treatment plan quality is possible using the VMAT technique that could represent a breakthrough for the MRgART if the modern advancements will lead to shorter optimization times.

Quantitative predictions in small-animal X-ray fluorescence tomography

X-ray fluorescence (XRF) tomography from nanoparticles (NPs) shows promise for high-spatial-resolution molecular imaging in small-animals. Quantitative reconstruction algorithms aim to reconstruct the true distribution of NPs inside the small-animal, but so far there has been no feasible way to predict signal levels or evaluate the accuracy of reconstructions in realistic scenarios. Here we present a GPU-based computational model for small-animal XRF tomography. The unique combination of a highly accelerated Monte Carlo tool combined with an accurate small-animal phantom allows unprecedented realistic full-body simulations. We use this model to simulate our experimental system to evaluate the quantitative performance and accuracy of our reconstruction algorithms on large-scale organs as well as mm-sized tumors. Furthermore, we predict the detection limits for sub-mm tumors at realistic NP concentrations. The computational model will be a valuable tool for optimizing next-generation experimental arrangements and reconstruction algorithms.

[Constraint priority list-based multi-objective optimization for intensity-modulated radiation therapy]

In intensity-modulated radiation therapy (IMRT), it is time-consuming to repeatedly adjust the objectives manually to obtain the best tradeoff between the prescribed dose of the planning target volume and sparing the organs-at-risk. Here we propose a new method to realize automatic multi-objective IMRT optimization, which quantifies the clinical preferences into the constraint priority list and adjusts the dose constraints based on the list to obtain the optimal solutions under the dose constraints. This method contains automatic adjustment mechanism of the dose constraint and automatic voxel weighting factor-based FMO model. Every time the dose constraint is adjusted, the voxel weighting factor-based FMO model is launched to find a global optimal solution that satisfied the current constraints. We tested the feasibility and effectiveness of this method in 6 cases of cervical cancer with IMRT by comparing the original plan and the automatic optimization plan generated by this method. The results showed that with the same PTV coverage and uniformity, the automatic optimization plan had a better a dose sparing of the organs-at-risk and a better plan quality than the original plan, and resulted in obvious reductions of the average V45 of the rectum from (41.99∓13.31)% to (32.55∓22.27)% and of the bladder from (44.37∓4.08)% to (28.99∓15.25)%.

Relative dosimetry with an MR-linac: Response of ion chambers, diamond, and diode detectors for off-axis, depth dose, and output factor measurements

PURPOSE

The purpose of this study was to acquire beam data for an MR-linac, with and without a 1.5 T magnetic field, by using a variety of commercially available detectors to assess their relative response in the magnetic field. The impact of the magnetic field on the measured dose distribution was also assessed.

METHODS

An MR-safe 3D scanning water phantom was used to measure output factors, depth dose curves, and off-axis profiles for various depths and for field sizes between 2 × 2 cm² and 22 × 22 cm² for an Elekta MR-linac beam with the orthogonal 1.5 T magnetic field on or off. An on-board MV portal imaging system was used to ensure that the reproducibility of the detector position, both with and without the magnetic field, was within 0.1 mm. The detectors used included ionization chambers with large, medium, and small sensitive volumes; a diamond detector; a shielded diode; and an unshielded diode.

RESULTS

The offset of the effective point of measurement of the ionization chambers was found to be reduced by at least half for each chamber in the direction parallel with the beam. A lateral shift of similar magnitude was also introduced to the chambers' effective point of measurement toward the average direction of the Lorentz force. A similar lateral shift (but in the opposite direction) was also observed for the diamond and diode detectors. The measured lateral shift in the dose distribution was independent of depth and field size for each detector for fields between 2 × 2 cm² and 10 × 10 cm² . The shielded diode significantly misrepresented the dose distribution in the lateral direction perpendicular to the magnetic field, making it seem more symmetric. The percentage depth dose was generally found to be lower with the magnetic field than without, but this difference was reduced as field size increased. The depth of maximum dose showed little dependence on field size in the presence of the magnetic field, with values from 1.2 cm to 1.3 cm between the 2 × 2 cm² and 22 × 22 cm² fields. Output factors measured in the magnetic field at the center of the beam profile produced a larger spread of values between detectors for fields smaller than 10 × 10 cm² (with a spread of 2% at 3 × 3 cm² ). The spread of values was more consistent when the output factors were measured at the point of peak intensity of the lateral dose distribution instead (except for the shielded diode which differed by up to 2% depending on field size).

CONCLUSIONS

The magnetic field of the MR-linac alters the effective point of measurement of ionization chambers, shifting it both downstream and laterally. Shielded diodes produce incorrect and misleading dose profiles. The output factor measured at the point of peak intensity in the lateral dose distribution is more robust than the conventional output factor (measured at central axis). Diodes are not recommended for output factor measurements in the magnetic field.