Dose Difference Maps Research Articles

In vivo EPID-based daily treatment error identification for volumetric-modulated arc therapy in head and neck cancers with a hierarchical convolutional neural network: a feasibility study.

We proposed a deep learning approach to classify various error types in daily VMAT treatment of head and neck cancer patients based on EPID dosimetry, which could provide additional information to support clinical decisions for adaptive planning. 146 arcs from 42 head and neck patients were analyzed. Anatomical changes and setup errors were simulated in 17,820 EPID images of 99 arcs obtained from 30 patients using in-house software for model training, validation, and testing. Subsequently, 141 clinical EPID images from 47 arcs belonging to the remaining 12 patients were utilized for clinical testing. The hierarchical convolutional neural network (HCNN) model was trained to classify error types and magnitudes using EPID dose difference maps. Gamma analysis with 3%/2mm (dose difference/distance to agreement) criteria was also performed. The F1 score, a combination of precision and recall, was utilized to evaluate the performance of the HCNN model and gamma analysis. The adaptive fractioned doses were calculated to verify the HCNN classification results. For error type identification, the overall F1 score of the HCNN model was 0.99 and 0.91 for primary type and subtype identification, respectively. For error magnitude identification, the overall F1 score in the simulation dataset was 0.96 and 0.70 for the HCNN model and gamma analysis, respectively; while the overall F1 score in the clinical dataset was 0.79 and 0.20 for the HCNN model and gamma analysis, respectively. The HCNN model-based EPID dosimetry can identify changes in patient transmission doses and distinguish the treatment error category, which could potentially provide information for head and neck cancer treatment adaption.

Commissioning and clinical implementation of an independent dose calculation system for scanned proton beams.

Experimental patient-specific QA (PSQA) is a time and resource-intensive process, with a poor sensitivity in detecting errors. Radiation therapy facilities aim to substitute it by means of independent dose calculation (IDC) in combination with a comprehensive beam delivery QA program. This paper reports on the commissioning of the IDC software tool myQAiON (IBA Dosimetry) for proton therapy and its clinical implementation at the MedAustron Ion Therapy Center. The IDC commissioning work included the validation of the beam model, the implementation and validation of clinical CT protocols, and the evaluation of patient treatment data. Dose difference maps, gamma index distributions, and pass rates (GPR) have been reviewed. The performance of the IDC tool has been assessed and clinical workflows, simulation settings, and GPR tolerances have been defined. Beam model validation showed agreement of ranges within±0.2mm, Bragg-Peak widths within±0.1mm, and spot sizes at various air gaps within±5% compared to physical measurements. Simulated dose in 2D reference fields deviated by-0.3%±0.5%, while 3D dose distributions differed by 1.8% on average to measurements. Validation of the CT calibration resulted in systematic differences of 2.0% between IDC and experimental data for tissue like samples. GPRs of 99.4±0.6% were found for head, head and neck, and pediatric CT protocols on a 2%/2mm gamma criterion. GPRs for the adult abdomen protocol were at 98.9% on average with 3%/3mm. Root causes of GPR outliers, for example, implants were identified and evaluated. IDC has been successfully commissioned and integrated into the MedAustron clinical workflow for protons in 2021. IDC has been stepwise and safely substituting experimental PSQA since February 2021. The initial reduction of proton experimental PSQA was about 25% and reached up to 90% after 1 year.

Error detection using a multi-channel hybrid network with a low-resolution detector in patient-specific quality assurance.

This study aimed to develop a hybrid multi-channel network to detect multileaf collimator (MLC) positional errors using dose difference (DD) maps and gamma maps generated from low-resolution detectors in patient-specific quality assurance (QA) for Intensity Modulated Radiation Therapy (IMRT). A total of 68 plans with 358 beams of IMRT were included in this study. The MLC leaf positions of all control points in the original IMRT plans were modified to simulate four types of errors: shift error, opening error, closing error, and random error. These modified plans were imported into the treatment planning system (TPS) to calculate the predicted dose, while the PTW seven29 phantom was utilized to obtain the measured dose distributions. Based on the measured and predicted dose, DD maps and gamma maps, both with and without errors, were generated, resulting in a dataset with 3222 samples. The network's performance was evaluated using various metrics, including accuracy, sensitivity, specificity, precision, F1-score, ROC curves, and normalized confusion matrix. Besides, other baseline methods, such as single-channel hybrid network, ResNet-18, and Swin-Transformer, were also evaluated as a comparison. The experimental results showed that the multi-channel hybrid network outperformed other methods, demonstrating higher average precision, accuracy, sensitivity, specificity, and F1-scores, with values of 0.87, 0.89, 0.85, 0.97, and 0.85, respectively. The multi-channel hybrid network also achieved higher AUC values in the random errors (0.964) and the error-free (0.946) categories. Although the average accuracy of the multi-channel hybrid network was only marginally better than that of ResNet-18 and Swin Transformer, it significantly outperformed them regarding precision in the error-free category. The proposed multi-channel hybrid network exhibits a high level of accuracy in identifying MLC errors using low-resolution detectors. The method offers an effective and reliable solution for promoting quality and safety of IMRT QA.

The effect of vaginal cylinder inhomogeneity on the HDR brachytherapy dose calculations using Monte Carlo simulations.

To analytically assess the heterogeneity effect of vaginal cylinders (VC) made of high-density plastics on dose calculations, considering the prescription point (surface or 5mm beyond the surface), and benchmark the accuracy of a commercial model-based dose calculation (MBDC) algorithm using Monte Carlo (MC) simulations. The GEANT4 MC code was used to simulate a commercial 192 Ir HDR source and VC, with diameters ranging from 20 to 35mm, inside a virtual water phantom. Standard plans were generated from a commercial treatment planning system [TPS-BrachyVision ACUROS (BV)] optimized for a treatment length of 5cm through two dose calculation approaches: (1) assuming all the environment as water (i.e., Dw,w-MC & Dw,w-TG43 ) and (2) accounting for the heterogeneity of VC applicators (i.e., Dw,w-App-MC & Dw,w-App-MBDC ). The compared isodose lines, and dose & energy difference maps were extracted for analysis. In addition, the dose difference on the peripheral surface, along the applicator and at middle of treatment length, as well as apical tip was evaluated. The Dw,w-App-MC results indicated that the VC heterogeneity can cause a dose reduction of (up to) % 6.8 on average (for all sizes) on the peripheral surface, translating to 1mm shrinkage of the isodose lines compared to Dw,w-MC . In addition, the results denoted that BV overestimates the dose on the peripheral surface and apical tip of about 3.7% and 17.9%, respectively, (i.e., Dw,w-App-MBDC vs Dw,w-App-MC ) when prescribing to the surface. However, the difference between the two were negligible at the prescription point when prescribing to 5mm beyond the surface. The VCs' heterogeneity could cause dose reduction when prescribing dose to the surface of the applicator, and hence increases the level of uncertainty. Thus, reviewing the TG43 results, in addition to ACUROS, becomes prudent, when evaluating the surface coverage at the apex.

Evaluating on-board kVCT- and MVCT-based dose calculation accuracy using a thorax phantom for helical tomotherapy treatments

Purpose. To evaluate the impact of CT number calibration and imaging parameter selection on dose calculation accuracy relative to the CT planning process in thoracic treatments for on-board helical CT imaging systems used in helical tomotherapy. Methods and Materials. Direct CT number calibrations were performed with appropriate protocols for each imaging system using an electron density phantom. Large volume and SBRT treatment plans were simulated and optimized for planning CT scans of an anthropomorphic thorax phantom and transferred to registered kVCT and MVCT scans of the phantom as appropriate. Relevant DVH metrics and dose-difference maps were used to evaluate and compare dose calculation accuracy relative to the planning CT based on a variation in imaging parameters applied for the on-board systems. Results. For helical kVCT scans of the thorax phantom, median differences in DVH parameters for the large volume treatment plan were less than ±1% with dose to the target volume either over- or underestimated depending on the imaging parameters utilized for CT number calibration and thorax phantom acquisition. For the lung SBRT plan calculated on helical kVCT scans, median dose differences were up to –2.7% with a more noticeable dependence on parameter selection. For MVCT scans, median dose differences for the large volume plan were within +2% with dose to the target overestimated regardless of the imaging protocol. Conclusion. Accurate dose calculations (median errors of <±1%) using a thorax phantom simulating realistic patient geometry and scatter conditions can be achieved with images acquired with a helical kVCT system on a helical tomotherapy unit. This accuracy is considerably improved relative to that achieved with the MV-based approach. In a clinical setting, careful consideration should be made when selecting appropriate kVCT imaging parameters for this process as dose calculation accuracy was observed to vary with both parameter selection and treatment type.

Considerations for CT Number Calibration and Imaging Parameter Selection for Adaptive Planning on a Novel On-Board Helical kVCT System

<h3>Purpose/Objective(s)</h3> To evaluate the impact of CT number calibration and imaging parameters on adaptive dose calculation accuracy relative to the CT planning process for an on-board helical kVCT imaging system used in helical tomotherapy treatments. <h3>Materials/Methods</h3> Helical kVCT calibrations were performed with thorax protocols at 120 kVp for <i>Fine</i> and <i>Normal</i> imaging modes using a standard electron density phantom. For this system, <i>Mode</i> determines the longitudinal beam width at isocenter, couch speed, and views per rotation, which impact the scatter signal, scan time, and patient dose, respectively, and offers <i>Fine</i> (50 mm beam width, long relative scan time, high relative mAs/rotation), <i>Normal</i> (100 mm, moderate scan time, moderate mAs/rotation), and <i>Coarse</i> (∼140 mm, short scan time, low mAs/rotation) mode options. An SBRT treatment plan was simulated for CT simulation scans of an anthropomorphic lung phantom and transferred to on-board helical kVCT scans with varying imaging and calibration protocols, and clinically relevant DVH metrics and dose-difference maps were used to compare the calculated dose distributions. <h3>Results</h3> Use of <i>Fine</i> mode for helical kVCT calibration resulted in smaller dose-volume deviations relative to the planning CT, with the magnitude of these deviations dependent on the mode used to acquire the scan of the lung phantom. For scans acquired under <i>Fine</i> mode, use of <i>Fine</i> mode calibration showed average differences in target volume DVH metrics of ± 1.0% (-1.7% max difference) compared to ± 2.5% (-3.6% max difference) with application of <i>Normal</i> mode calibration for the same images. Likewise, for phantom scans acquired under <i>Normal</i> mode, these differences were ± 1.2% (+ 2.1%) for <i>Fine</i> calibration compared to ± 1.4% (-2.1%) for <i>Normal</i> calibration, while for phantom scans acquired under <i>Coarse</i> mode, differences were ± 2.0% (-4.1%) and ± 3.3% (-6.2%), respectively. Dose-difference maps confirmed the greatest deviations in the calculated dose distributions occurred in the high-dose, target volume region, though some differences were also observed at the tissue-air interface of the chest wall. Visual differences relative to the planning CT were lessened with use of <i>Fine</i> mode acquisitions of both the calibration and lung phantoms. <h3>Conclusion</h3> Use of <i>Fine</i> mode for helical kVCT number calibration and image acquisition resulted in dose calculations most consistent with those calculated on diagnostic-quality CT images. If excess patient dose from additional imaging is a concern, <i>Normal</i> or <i>Coarse</i> mode image acquisitions with application of <i>Fine</i> mode calibration provided dose calculation accuracies within a reasonable tolerance as well. Also, while <i>Fine</i> mode increases scan times relative to other modes, this additional time is still significantly lessened compared to on-board CBCT or MVCT systems currently used for CT-based online adaptive planning.

Deep Neural Network With Structural Similarity Difference and Orientation-Based Loss for Position Error Classification in the Radiotherapy of Graves' Ophthalmopathy Patients.

Identifying position errors for Graves' ophthalmopathy (GO) patients using electronic portal imaging device (EPID) transmission fluence maps is helpful in monitoring treatment. However, most of the existing models only extract features from dose difference maps computed from EPID images, which do not fully characterize all information of the positional errors. In addition, the position error has a three-dimensional spatial nature, which has never been explored in previous work. To address the above problems, a deep neural network (DNN) model with structural similarity difference and orientation-based loss is proposed in this paper, which consists of a feature extraction network and a feature enhancement network. To capture more information, three types of Structural SIMilarity (SSIM) sub-index maps are computed to enhance the luminance, contrast, and structural features of EPID images, respectively. These maps and the dose difference maps are fed into different networks to extract radiomic features. To acquire spatial features of the position errors, an orientation-based loss function is proposed for optimal training. It makes the data distribution more consistent with the realistic 3D space by integrating the error deviations of the predicted values in the left-right, superior-inferior, anterior-posterior directions. Experimental results on a constructed dataset demonstrate the effectiveness of the proposed model, compared with other related models and existing state-of-the-art methods.

Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients.

PurposeTo find a suitable method for analyzing electronic portal imaging device (EPID) transmission fluence maps for the identification of position errors in the in vivo dose monitoring of patients with Graves’ ophthalmopathy (GO).MethodsPosition errors combining 0-, 2-, and 4-mm errors in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions in the delivery of 40 GO patient radiotherapy plans to a human head phantom were simulated and EPID transmission fluence maps were acquired. Dose difference (DD) and structural similarity (SSIM) maps were calculated to quantify changes in the fluence maps. Three types of machine learning (ML) models that utilize radiomics features of the DD maps (ML 1 models), features of the SSIM maps (ML 2 models), and features of both DD and SSIM maps (ML 3 models) as inputs were used to perform three types of position error classification, namely a binary classification of the isocenter error (type 1), three binary classifications of LR, SI, and AP direction errors (type 2), and an eight-element classification of the combined LR, SI, and AP direction errors (type 3). Convolutional neural network (CNN) was also used to classify position errors using the DD and SSIM maps as input.ResultsThe best-performing ML 1 model was XGBoost, which achieved accuracies of 0.889, 0.755, 0.778, 0.833, and 0.532 in the type 1, type 2-LR, type 2-AP, type 2-SI, and type 3 classification, respectively. The best ML 2 model was XGBoost, which achieved accuracies of 0.856, 0.731, 0.736, 0.949, and 0.491, respectively. The best ML 3 model was linear discriminant classifier (LDC), which achieved accuracies of 0.903, 0.792, 0.870, 0.931, and 0.671, respectively. The CNN achieved classification accuracies of 0.925, 0.833, 0.875, 0.949, and 0.689, respectively.ConclusionML models and CNN using combined DD and SSIM maps can analyze EPID transmission fluence maps to identify position errors in the treatment of GO patients. Further studies with large sample sizes are needed to improve the accuracy of CNN.

Error detection model developed using a multi‐task convolutional neural network in patient‐specific quality assurance for volumetric‐modulated arc therapy

In patient-specific quality assurance (QA) for static beam intensity-modulated radiation therapy (IMRT), machine-learning-based dose analysis methods have been developed to identify the cause of an error as an alternative to gamma analysis. Although these new methods have revealed that the cause of the error can be identified by analyzing the dose distribution obtained from the two-dimensional detector, they have not been extended to the analysis of volumetric-modulated arc therapy (VMAT) QA. In this study, we propose a deep learning approach to detect various types of errors in patient-specific VMAT QA. A total of 161 beams from 104 prostate VMAT plans were analyzed. All beams were measured using a cylindrical detector (Delta4; ScandiDos, Uppsala, Sweden), and predicted dose distributions in a cylindrical phantom were calculated using a treatment planning system (TPS). In addition to the error-free plan, we simulated 12 types of errors: two types of multileaf collimator positional errors (systematic or random leaf offset of 2mm), two types of monitor unit (MU) scaling errors (±3%), two types of gantry rotation errors (±2° in clockwise and counterclockwise direction), and six types of phantom setup errors (±1mm in lateral, longitudinal, and vertical directions). The error-introduced predicted dose distributions were created by editing the calculated dose distributions using a TPS with in-house software. Those 13 types of dose difference maps, consisting of an error-free map and 12 error maps, were created from the measured and predicted dose distributions and were used to train the convolutional neural network (CNN) model. Our model was a multi-task model that individually detected each of the 12 types of errors. Two datasets, Test sets 1 and 2, were prepared to evaluate the performance of the model. Test set 1 consisted of 13 types of dose maps used for training, whereas Test set 2 included the dose maps with 25 types of errors in addition to the error-free dose map. The dose map, which introduced 25 types of errors, was generated by combining two of the 12 types of simulated errors. For comparison with the performance of our model, gamma analysis was performed for Test sets 1 and 2 with the criteria set to 3%/2mm and 2%/1mm (dose difference/distance to agreement). For Test set 1, the overall accuracy of our CNN model, gamma analysis with the criteria set to 3%/2mm, and gamma analysis with the criteria set to 2%/1mm was 0.92, 0.19, and 0.81, respectively. Similarly, for Test set 2, the overall accuracy was 0.44, 0.42, and 0.95, respectively. Our model outperformed gamma analysis in the classification of dose maps containing a single type error, and the performance of our model was inferior in the classification of dose maps containing compound errors. A multi-task CNN model for detecting errors in patient-specific VMAT QA using a cylindrical measuring device was constructed, and its performance was evaluated. Our results demonstrate that our model was effective in identifying the error type in the dose map for VMAT QA.

Error detection using a convolutional neural network with dose difference maps in patient-specific quality assurance for volumetric modulated arc therapy

The aim of this study was to evaluate the use of dose difference maps with a convolutional neural network (CNN) to detect multi-leaf collimator (MLC) positional errors in patient-specific quality assurance for volumetric modulated radiation therapy (VMAT). A cylindrical three-dimensional detector (Delta4, ScandiDos, Uppsala, Sweden) was used to measure 161 beams from 104 clinical prostate VMAT plans. For the simulation used error-free plans plus plans with two types of MLC error were introduced: systematic error and random error. A total of 483 dose distributions in a virtual cylindrical phantom were calculated with a treatment planning system. Dose difference maps were created from two planar dose distributions from the measured and calculated dose distributions, and these were used as the input for the CNN, with 375 datasets assigned for training and 108 datasets assigned for testing. The CNN model had three convolution layers and was trained with five-fold cross-validation. The CNN model classified the error types of the plans as “error-free,” “systematic error,” or “random error,” with an overall accuracy of 0.944. The sensitivity values for the “error-free,” “systematic error,” and “random error” classifications were 0.889, 1.000, and 0.944, respectively, and the specificity values were 0.986, 0.986, and 0.944, respectively. This approach was superior to those based on gamma analysis. Using dose difference maps with a CNN model may provide an effective solution for detecting MLC errors for patient-specific VMAT quality assurance.

New capabilities of the Monte Carlo dose engine ARCHER-RT: Clinical validation of the Varian TrueBeam machine for VMAT external beam radiotherapy.

The Monte Carlo radiation transport method is considered the most accurate approach for absorbed dose calculations in external beam radiation therapy. In this study, an efficient and accurate source model of the Varian TrueBeam 6X STx Linac is developed and integrated with a fast Monte Carlo photon-electron transport absorbed dose engine, ARCHER-RT, which is capable of being executed on CPUs, NVIDIA GPUs, and AMD GPUs. This capability of fast yet accurate radiation dose calculation is essential for clinical utility of this new technology. This paper describes the software and algorithmic developments made to the ARCHER-RT absorbed dose engine. AMD's Heterogeneous-Compute Interface for Portability (HIP) was implemented in ARCHER-RT to allow for device independent execution on NVIDIA and AMD GPUs. Architecture-specific atomic-add algorithms have been identified and both more accurate single-precision and double-precision computational absorbed dose calculation methods have been added to ARCHER-RT and validated through a test case to evaluate the accuracy and performance of the algorithms. The validity of the source model and the radiation transport physics were benchmarked against Monte Carlo simulations performed with EGSnrc. Secondary dose-check physics plans, and a clinical prostate treatment plan were calculated to demonstrate the applicability of the platform for clinical use. Absorbed dose difference maps and gamma analyses were conducted to establish the accuracy and consistency between the two Monte Carlo models. Timing studies were conducted on a CPU, an NVIDIA GPU, and an AMD GPU to evaluate the computational speed of ARCHER-RT. Percent depth doses were computed for different field sizes ranging from 1.5cm2 ×1.5cm2 to 22cm2 ×40cm2 and the two codes agreed for all points outside high gradient regions within 3%. Axial profiles computed for a 10cm2 ×10cm2 field for multiple depths agreed for all points outside high gradient regions within 2%. The test case investigating the impact of native single-precision compared to double-precision showed differences in voxels as large as 71.47% and the implementation of KAS single-precision reduced the difference to less than 0.01%. The 3%/3mm gamma pass rates for an MPPG5a multileaf collimator (MLC) test case and a clinical VMAT prostate plan were 94.2% and 98.4% respectively. Timing studies demonstrated the calculation of a VMAT plan was completed in 50.3, 187.9, and 216.8s on an NVIDIA GPU, AMD GPU, and Intel CPU, respectively. ARCHER-RT is capable of patient-specific VMAT external beam photon absorbed dose calculations and its potential has been demonstrated by benchmarking against a well validated EGSnrc model of a Varian TrueBeam. Additionally, the implementation of AMD's HIP has shown the flexibility of the ARCHER-RT platform for device independent calculations. This work demonstrates the significant addition of functionality added to ARCHER-RT framework which has marked utility for both research and clinical applications and demonstrates further that Monte Carlo-based absorbed dose engines like ARCHER-RT have the potential for widespread clinical implementation.

Local Dose Effects for Late Gastrointestinal Toxicity After Hypofractionated and Conventionally Fractionated Modern Radiotherapy for Prostate Cancer in the HYPRO Trial.

Purpose: Late gastrointestinal (GI) toxicity after radiotherapy for prostate cancer may have significant impact on the cancer survivor's quality of life. To date, little is known about local dose-effects after modern radiotherapy including hypofractionation. In the current study we related the local spatial distribution of radiation dose in the rectum to late patient-reported gastrointestinal (GI) toxicities for conventionally fractionated (CF) and hypofractionated (HF) modern radiotherapy in the randomized HYPRO trial.Material and Methods: Patients treated to 78 Gy in 2 Gy fractions (n = 298) or 64.6 Gy in 3.4 Gy fractions (n = 295) with available late toxicity questionnaires (n ≥ 2 within 1–5 years post-treatment) and available 3D planning data were eligible for this study. The majority received intensity modulated radiotherapy (IMRT). We calculated two types of dose surface maps: (1) the total delineated rectum with its central axis scaled to unity, and (2) the delineated rectum with a length of 7 cm along its central axis aligned on the prostate's half-height point (prostate-half). For each patient-reported GI symptom, dose difference maps were constructed by subtracting average co-registered EQD2 (equivalent dose in 2 Gy) dose maps of patients with and without the symptom of interest, separately for HF and CF. P-values were derived from permutation tests. We evaluated patient-reported moderate to severe GI symptoms.Results: Observed incidences of rectal bleeding and increased stool frequency were significantly higher in the HF group. For rectal bleeding (p = 0.016), mucus discharge (p = 0.015), and fecal incontinence (p = 0.001), significant local dose-effects were observed in HF patients but not in CF patients. For rectal pain, similar local dose-effects (p < 0.05) were observed in both groups. No significant local dose-effects were observed for increased stool frequency. Total rectum mapping vs. prostate-half mapping showed similar results.Conclusion: We demonstrated significant local dose-effect relationships for patient-reported late GI toxicity in patients treated with modern RT. HF patients were at higher risk for increased stool frequency and rectal bleeding, and showed the most pronounced local dose-effects in intermediate-high dose regions. These findings suggest that improvement of current treatment optimization protocols could lead to clinical benefit, in particular for HF treatment.

External beam patient dose verification based on the integral quality monitor (IQM®) output signals

Background: The Integral Quality Monitor (IQM®) can essentially measure the integral fluence through a segment and provide real-time information about the accuracy of radiation delivery based on comparisons of measured segment signals and pre-calculated reference values. However, the present IQM chamber cannot calculate the dose in the patient. Aim: This study aims to make use of IQM field output signals to calculate the number of monitor units (MUs) delivered through an arbitrary treatment field in order to convert Monte Carlo (MC)-generated dose distributions in a patient model into absolute dose. Methods: XiO and Monaco treatment planning systems (TPSs) were used to define treatment beam portals for cervix and esophagus conformal radiotherapy as well as prostate intensity-modulated radiotherapy for the translation of patient and beam setup information from DICOM to DOSXYZnrc. The planned beams were simulated in a patient model built from actual patient CT images and each simulated integral field/segment was weighted with its MUs before summation to get the total dose in the plan. The segment beam weights (MUs) were calculated as the ratio of the open-field IQM measured signal and the calculated signal per MU extracted from chamber sensitivity maps. These are the actual MUs delivered not just MUs set. The beam weighting method was evaluated by comparing weighted MC doses with original planned doses using profile and isodose comparisons, dose difference maps, γ analysis and dose-volume histogram (DVH) data. Results: γ pass rates of up to 98% were found, except for the esophagus plan where the γ pass rate was below 45%. DVH comparisons showed good agreement for most organs, with the largest differences observed in low-density lung. However, these discrepancies can result from differences in dose calculation algorithms or differences in MUs used for dose weighting planned by the TPS and MUs calculated using IQM field output signals. To test this, a 4-field box DOSXYZnrc MC simulation weighted with planned (XiO) MUs was compared with the same simulation weighted with IQM-based MUs. Dose differences of up to 5% were found on the isocentre slice. For XiO versus MC, up to 7% dose differences were found, indicating additional error due to limitations of XiO’s superposition algorithm. Dose differences between MC Monaco and MC EGSnrc were less than 3%. Conclusions: The most valuable comparison was MC versus MC as it eliminated algorithm discrepancies and evaluated dose differences precisely according to beam weighting. For XiO TPS, care must be taken as dose differences may also arise due to limitations in XiO’s planning software, not merely due to differences in MUs. Overall, the IQM was successfully used to compute beam dose weights to accurately reconstruct the patient dose using unweighted MC beams. Our technique can be used for pre-treatment QA provided each segment output is known and an accurate linac source model is available.

Comparison of dose distributions between transverse magnetic fields of 0.35 T and 1.5 T for radiotherapy in lung tumor using Monte Carlo calculation

We investigated the impact of the transverse magnetic fields of 0.35 T and 1.5 T on the dose distributions for a 6 MV beam, by using a thorax phantom with a lung tumor. First, the dose distributions in the magnetic flux densities of 0 T, 0.35 T, and 1.5 T were compared by increasing the number of irradiation fields. Next, the dose distributions for stereotactic body radiotherapy (SBRT) with 5-fields for an isolated lung tumor was compared in transverse magnetic fields. All dose distributions were calculated by the Monte Carlo method. The prescription doses for SBRT with 5-fields was 48 Gy for D95 (dose covering 95% volume) in the planning target volume (PTV). The dose distributions were analyzed by the dose difference map (DD map), dose volume histogram (DVH), and dose indices. For the 1-field, the dose distributions were more affected at 1.5 T rather than 0.35 T. The DVHs for PTV at 1.5 T almost agreed with those at 0 T for more than 5-fields. In contrast, the D98 in the PTV at 0.35 T reduced constantly by 6.0% with more than 5-fields. The D95 in PTV for SBRT with 5-fields was 9.0% lower at 0.35 T and 2.5% higher at 1.5 T, in comparison with that at 0 T. For dispersed irradiation angles of more than 5-fields, it is more desirable to use the magnetic flux density of 1.5 T than 0.35 T for the radiotherapy in the lung tumor.

Dose distribution prediction in isodose feature-preserving voxelization domain using deep convolutional neural network.

To implement a framework for dose prediction using a deep convolutional neural network (CNN) based on the concept of isodose feature-preserving voxelization (IFPV) in simplifying the representation of the dose distribution. The concept of IFPV was introduced for concise representation of a treatment plan. IFPV is a sparse voxelization scheme that partitions the voxels into subgroups according to their geometric, anatomical, and dosimetric features. In this study a deep CNN was constructed to buildup a dose prediction model in IFPV domain based on 60 volumetric modulated arc therapy (VMAT) treatment plans from a database of previously treated 70 prostate cancer patients. The dose prediction model learns the contour to dose relationship and predicts the dose distribution in IFPV domain given the input contours. Additional ten independent prostate cases were selected as testing data.DVH comparison, dose difference maps, and residual analysis with the sum of absolute residual (SAR) were used to evaluate the performance of the proposed method. The proposed IFPV-based method achieved good prediction performance in terms of DVH comparison and dose difference maps. Statistical results of SARs showed that the IFPV-based method is comparable with voxel-based method even though the number of dose representation points used in the IFPV-based method was substantially reduced. The proposed approach achieved mean SARs of 0.029±0.020 and 0.077±0.030 for bladder and rectum, respectively, compared with mean SARs of 0.039±0.029 and 0.069±0.028 in the conventional voxel-based method. A novel deep CNN-based dose prediction method in IFPV domain was proposed. The proposed approach has great potential to significantly improve the efficiency of dose prediction and facilitate the treatment planning workflow.

Incorporating dosimetric features into the prediction of 3D VMAT dose distributions using deep convolutional neural network

An accurate prediction of achievable dose distribution on a patient specific basis would greatly improve IMRT/VMAT planning in both efficiency and quality. Recently machine learning techniques have been proposed for IMRT dose prediction based on patient’s contour information from planning CT. In these existing prediction models geometric/anatomic features were learned for building the dose prediction models and few features that characterize the dosimetric properties of the patients were utilized. In this study we propose a method to incorporate the dosimetric features in the construction of a more reliable dose prediction model based on the deep convolutional neural network (CNN). In addition to the contour information, the dose distribution from a PTV-only plan (i.e. the plan with the best PTV coverage by sacrificing the OARs sparing) is also employed as the model input to build a deep learning based dose prediction model. A database of 60 volumetric modulated arc therapy (VMAT) plans for the prostate cancer patients was used for training. The trained prediction model was then tested on a cohort of ten cases. Dose difference maps, DVHs, dosimetric endpoints and statistical analysis of the sum of absolute residuals (SARs) were used to evaluate the proposed method. Our results showed that the mean SARs for the PTV, bladder and rectum using our method were 0.007 ± 0.003, 0.035 ± 0.032 and 0.067 ± 0.037 respectively, lower than the SARs obtained with the contours-based method, indicating the potential of the proposed approach in accurately predicting dose distribution.

Dependence of volume dose indices on dose calculation algorithms for VMAT-SBRT plans for peripheral lung tumor

The purpose of this study was to investigate the dependence of volume dose indices on dose calculation algorithms for volumetric modulated arc therapy (VMAT) for stereotactic body radiotherapy (SBRT) plans to treat peripheral lung tumors by comparing them with those of Monte Carlo (MC) calculations. VMAT-SBRT plans for peripheral lung tumors were created using the Eclipse treatment planning system (TPS) for 24 patients with nonsmall cell lung cancer. VMAT dose distributions for gross tumor volume (GTV), internal target volume (ITV), and planning target volume (PTV) were calculated using the analytical anisotropic algorithm (AAA), the Acuros XB (AXB) algorithm, and a MC algorithm. VMAT dose distributions of the 3 algorithms were compared using their volume dose indices from dose volume histograms (DVHs), a dose difference map, and 3-dimensional gamma analysis. The DVHs for GTV and ITV from AAA, AXB, and MC were in good agreement. The difference between the ITV and PTV volume dose indices from AAA and MC increased as D98, D95, D80, D50, and D2. In particular, the difference between D98 for PTV from AAA and MC was up to 48%. A >5% difference between D95 for PTV from AAA and MC was 11 patients, but only 2 patients for ITV. The volume dose indices for AXB were near those of MC. AAA tended to overestimate the PTV volume dose indices compared to AXB and MC. Thus, we propose that the volume dose indices for the ITV be used because they are independent of dose calculation algorithms.

Patient-Specific QA of Spot-Scanning Proton Beams using Radiochromic Film.

Radiochromic film for spot-scanning QA provides high spatial resolution and efficiency gains from one-shot irradiation for multiple depths. However, calibration can be a tedious procedure which may limit widespread use. Moreover, since there may be an energy dependence, which manifests as a depth dependence, this may require additional measurements for each patient. We present a one-scan protocol to simplify the procedure. A calibration using an EBT3 film, exposed by a 6-level step-wedge plan on a Proteus®PLUS proton system (IBA, Belgium), was performed at depths of 18, 20, 24cm using Plastic Water® (CIRS, Norfolk, VA). The calibration doses ranged from 65-250 cGy(RBE) (relative biological effectiveness) for proton energies of 170-200 MeV. A clinical prostate+nodes plan was used for validation. The planar doses at selected depths were measured with EBT3 films and analyzed using One-scan protocol (one-scan digitization of QA film and at least one film exposed to a known dose). The gamma passing rates, dose-difference maps, and profiles of 2D planar doses measured with EBT3 film and IBA MatriXX-PT, versus the RayStation TPS calculations were analyzed and compared. The EBT3 film measurement results matched well with the TPS calculation data with an average passing rate of ~95% for 2%/2mm and slightly lower passing rates were obtained from an ion chamber array detector. We were able to demonstrate that the use of a proton step-wedge provided clinically acceptable results and minimized variations between film-scanner orientation, inter-scan, and scanning conditions. Furthermore, for relative dosimetry (calibration is not done at the time of experiment) it could be derived from no more than two films exposed to known doses (one could be zero) for rescaling the master calibration curve at each depth. The sensitivity of the calibration to depth variations has been explored. One-scan protocol results appear to be comparable to that of the ion chamber array detector. The use of a proton step-wedge for calibration of EBT3 film potentially increases efficiency in patient-specific QA of proton beams.

Dose–surface maps identifying local dose–effects for acute gastrointestinal toxicity after radiotherapy for prostate cancer

Background and purposeWe evaluated dose distributions in the anorectum and its relation to acute gastrointestinal toxicities using dose surface maps in an image-guided (IG) IMRT and 3D-conformal radiotherapy (3D-CRT) population. Material and methodsFor patients treated to 78Gy with IG-IMRT (n=260) or 3D-CRT (n=215), for whom acute toxicity data were available, three types of surface maps were calculated: (1) total anorectum using regular intervals along a central axis with perpendicular slices, (2) the rectum next to the prostate, and (3) the anal canal (horizontal slicing). For each toxicity, an average dose map was calculated for patients with and without the toxicity and subsequently dose difference maps were constructed, 3D-CRT and IG-IMRT separately. P-values were based on permutation tests. ResultsDose distributions in patients with grade ⩾2 acute proctitis were significantly different from dose distributions in patients without toxicity, for IG-IMRT and 3D-CRT. At the cranial and posterior rectal site, in areas receiving moderate dose levels (≈25–50Gy), dose differences up to 10Gy were identified for IG-IMRT. For pain, cramps, incontinence, diarrhea and mucus loss significant differences were found as well. ConclusionsWe demonstrated significant relationships between acute rectal toxicity and local dose distributions. This may serve as a basis for subsequent dose–effect modeling in IG-IMRT, and improved dose constraints in current clinical practice.

Multidimensional dosimetry of ¹⁰⁶Ru eye plaques using EBT3 films and its impact on treatment planning.

The purpose of this study was to establish a method to perform multidimensional radiochromic film measurements of (106)Ru plaques and to benchmark the resulting dose distributions against Monte Carlo simulations (MC), microdiamond, and diode measurements. Absolute dose rates and relative dose distributions in multiple planes were determined for three different plaque models (CCB, CCA, and COB), and three different plaques per model, using EBT3 films in an in-house developed polystyrene phantom and the mcnp6 MC code. Dose difference maps were generated to analyze interplaque variations for a specific type, and for comparing measurements against MC simulations. Furthermore, dose distributions were validated against values specified by the manufacturer (BEBIG) and microdiamond and diode measurements in a water scanning phantom. Radial profiles were assessed and used to estimate dosimetric margins for a given combination of representative tumor geometry and plaque size. Absolute dose rates at a reference depth of 2 mm on the central axis of the plaque show an agreement better than 5% (10%) when comparing film measurements (mcnp6) to the manufacturer's data. The reproducibility of depth-dose profile measurements was <7% (2 SD) for all investigated detectors and plaque types. Dose difference maps revealed minor interplaque deviations for a specific plaque type due to inhomogeneities of the active layer. The evaluation of dosimetric margins showed that for a majority of the investigated cases, the tumor was not completely covered by the 100% isodose prescribed to the tumor apex if the difference between geometrical plaque size and tumor base ≤4 mm. EBT3 film dosimetry in an in-house developed phantom was successfully used to characterize the dosimetric properties of different (106)Ru plaque models. The film measurements were validated against MC calculations and other experimental methods and showed a good agreement with data from BEBIG well within published tolerances. The dosimetric information as well as interplaque comparison can be used for comprehensive quality assurance and for considerations in the treatment planning of ophthalmic brachytherapy.