Technical note: Consistency of IAEA's TRS-483 and AAPM's extended TG-51 protocols for clinical reference dosimetry of the CyberKnife M6 machine.

BACKGROUND: While IAEA's TRS-483 code of practice is adapted for the calibration of CyberKnife machines, AAPM's TG-51 is still the protocol recommended by the manufacturer for their calibration. The differences between both protocols could lead to differences in absorbed dose to water during the calibration process. PURPOSE: The aims of this work are to evaluate the difference resulting from the application of TG-51 (including the manufacturer's adaptations) and TRS-483 in terms of absorbed dose to water for a CyberKnife M6, and to evaluate the consistency of TRS-483. METHODS: Measurements are performed on a CyberKnife M6 unit under machine-specific reference conditions using a calibrated Exradin A12 ionization chamber. Monte Carlo (MC) simulations are performed to estimate k Q msr , Q 0 f msr , f ref $k_{Q_{\mathrm{msr}},Q_0}^{f_{\mathrm{msr}},f_{\mathrm{ref}}}$ and k vol $k_{\text{vol}}$ using a fully modeled detector and an optimized CyberKnife M6 beam model. The latter is also estimated experimentally. Differences between the adapted TG-51 and TRS-483 protocols are identified and their impact is quantified. RESULTS: When using an in-house experimentally-evaluated volume averaging correction factor, a difference of 0.11% in terms of absorbed dose to water per monitor unit is observed when applying both protocols. This disparity is solely associated to the difference in beam quality correction factor. If a generic volume averaging correction factor is used during the application of TRS-483, the difference in calibration increases to 0.14%. In both cases, the disparity is not statistically significant according to TRS-483's reported uncertainties on their beam quality correction factor (i.e., 1%). MC results lead to k Q msr , Q 0 f msr , f ref = 1.0004 ± 0.0002 $k_{Q_{\mathrm{msr}},Q_0}^{f_{\mathrm{msr}},f_{\mathrm{ref}}}=1.0004\pm 0.0002$ and k vol = 1.0072 ± 0.0009 $k_{\text{vol}}=1.0072\pm 0.0009$ . Results illustrate that the generic beam quality correction factor provided in the TRS-483 might be overestimated by 0.36% compared to our specific model and that this overestimation could be due to the volume averaging component. CONCLUSIONS: For clinical reference dosimetry of the CyberKnife M6, the application of TRS-483 is found to be consistent with TG-51.

Automated Detection of Brain Metastases on T1-Weighted MRI Using a Convolutional Neural Network: Impact of Volume Aware Loss and Sampling Strategy.

BACKGROUND: Detection of brain metastases (BM) and segmentation for treatment planning could be optimized with machine learning methods. Convolutional neural networks (CNNs) are promising, but their trade-offs between sensitivity and precision frequently lead to missing small lesions. HYPOTHESIS: Combining volume aware (VA) loss function and sampling strategy could improve BM detection sensitivity. STUDY TYPE: Retrospective. POPULATION: A total of 530 radiation oncology patients (55% women) were split into a training/validation set (433 patients/1460 BM) and an independent test set (97 patients/296 BM). FIELD STRENGTH/SEQUENCE: 1.5 T and 3 T, contrast-enhanced three-dimensional (3D) T1-weighted fast gradient echo sequences. ASSESSMENT: Ground truth masks were based on radiotherapy treatment planning contours reviewed by experts. A U-Net inspired model was trained. Three loss functions (Dice, Dice + boundary, and VA) and two sampling methods (label and VA) were compared. Results were reported with Dice scores, volumetric error, lesion detection sensitivity, and precision. A detected voxel within the ground truth constituted a true positive. STATISTICAL TESTS: McNemar's exact test to compare detected lesions between models. Pearson's correlation coefficient and Bland-Altman analysis to compare volume agreement between predicted and ground truth volumes. Statistical significance was set at P ≤ 0.05. RESULTS: Combining VA loss and VA sampling performed best with an overall sensitivity of 91% and precision of 81%. For BM in the 2.5-6 mm estimated sphere diameter range, VA loss reduced false negatives by 58% and VA sampling reduced it further by 30%. In the same range, the boundary loss achieved the highest precision at 81%, but a low sensitivity (24%) and a 31% Dice loss. DATA CONCLUSION: Considering BM size in the loss and sampling function of CNN may increase the detection sensitivity regarding small BM. Our pipeline relying on a single contrast-enhanced T1-weighted MRI sequence could reach a detection sensitivity of 91%, with an average of only 0.66 false positives per scan. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Efficient dose-rate correction of silicon diode relative dose measurements.

PURPOSE: Silicon diodes are often the detector of choice for relative dose measurements, particularly in the context of radiotherapy involving small photon beams. However, a major drawback lies in their dose-rate dependency. Although ionization chambers are often too large for small field output factor (OF) measurements, they are valuable instruments to provide reliable percent-depth dose (PDD) curves in reference beams. The aim of this work is to propose a practical and accurate method for the characterization of silicon diode dose-rate dependence correction factors using ionization chamber measurements as a reference. METHODS: The robustness of ionization chambers for PDD measurements is used to quantify the dose-rate dependency of a diode detector. A mathematical formalism, which exploits the error induced in percent-depth ionization (PDI) curves for diodes by their dose-rate dependency, is developed to derive a dose-rate correction factor applicable to diode relative measurements. The method is based on the definition of the recombination correction factor given in the addendum to TG 51 and is applied to experimental measurements performed on a CyberKnife M6 radiotherapy unit using a PTW 60012 diode detector. A measurement-based validation is provided by comparing corrected PDI curves to measurements performed with a PTW 60019 diamond detector, which does not exhibit dose-rate dependence. RESULTS: Results of dose-rate correction factors for PDI curves, off-axis ratios (OARs), tissue-phantom ratios, and small field OFs are coherent with the expected behavior of silicon diode detectors. For all considered setups and field sizes, the maximum correction and the maximum impact of the uncertainties induced by the correction are obtained for OARs for the 60 mm collimator, with a correction of 2.5% and an uncertainty of 0.34%. For OFs, corrections range from 0.33% to 0.82% for all field sizes considered, and increase with the reduction of the field size. Comparison of PDI curves corrected for dose-rate and for in-depth beam quality variations illustrates excellent agreement with measurements performed using the diamond detector. CONCLUSION: The proposed method allows the efficient and precise correction of the dose-rate dependence of silicon diode detectors in the context of clinical relative dosimetry.

A probabilistic approach for determining Monte Carlo beam source parameters: I. Modeling of a CyberKnife M6 unit.

Objective.During Monte Carlo modeling of external radiotherapy beams, models must be adjusted to reproduce the experimental measurements of the linear accelerator being considered. The aim of this work is to propose a new method for the determination of the energy and spot size of the electron beam incident on the target of a linear accelerator using a maximum likelihood estimation.Approach.For that purpose, the method introduced by Francesconet al(2008Med. Phys.35504-13) is expanded upon in this work. Simulated tissue-phantom ratios and uncorrected output factors using a set of different detector models are compared to experimental measurements. A probabilistic formalism is developed and a complete uncertainty budget, which includes a detailed simulation of positioning errors, is evaluated. The method is applied to a CyberKnife M6 unit using four detectors (PTW 60012, PTW 60019, Exradin A1SL and IBA CC04), with simulations being performed using the EGSnrc suite.Main results.The likelihood distributions of the electron beam energy and spot size are evaluated, leading toEˆ=7.42±0.17MeVandFˆ=2.15±0.06mm. Using these results and a 95% confidence region, simulations reproduce measurements in 13 out of the 14 considered setups.Significance.The proposed method allows an accurate beam parameter optimization and uncertainty evaluation during the Monte Carlo modeling of a radiotherapy unit.

A probabilistic approach for determining Monte Carlo beam source parameters: II. Impact of beam modeling uncertainties on dosimetric functions and treatment plans.

Objective.The Monte Carlo method is recognized as a valid approach for the evaluation of dosimetric functions for clinical use. This procedure requires the accurate modeling of the considered linear accelerator. In Part I, we propose a new method to extract the probability density function of the beam model physical parameters. The aim of this work is to evaluate the impact of beam modeling uncertainties on Monte Carlo evaluated dosimetric functions and treatment plans in the context of small fields.Approach.Simulations of output factors, output correction factors, dose profiles, percent-depth doses and treatment plans are performed using the CyberKnife M6 model developed in Part I. The optimized pair of electron beam energy and spot size, and eight additional pairs of beam parameters representing a 95% confidence region are used to propagate the uncertainties associated to the source parameters to the dosimetric functions.Main results.For output factors, the impact of beam modeling uncertainties increases with the reduction of the field size and confidence interval half widths reach 1.8% for the 5 mm collimator. The impact on output correction factors cancels in part, leading to a maximum confidence interval half width of 0.44%. The impact is less significant for percent-depth doses in comparison to dose profiles. For these types of measurement, in absolute terms and in comparison to the reference dose, confidence interval half widths less than or equal to 1.4% are observed. For simulated treatment plans, the impact is more significant for the treatment delivered with a smaller field size with confidence interval half widths reaching 2.5% and 1.4% for the 5 and 20 mm collimators, respectively.Significance.Results confirm that AAPM TG-157's tolerances cannot apply to the field sizes studied. This study provides an insight on the reachable dose calculation accuracy in a clinical setup.

Dosimetric verification of lung phantom calculated by collapsed cone convolution: A Monte Carlo and experimental evaluation.

OBJECTIVE: To evaluate the dose calculation accuracy in the Prowess Panther treatment planning system (TPS) using the collapsed cone convolution (CCC) algorithm. METHODS: The BEAMnrc Monte Carlo (MC) package was used to predict the dose distribution of photon beams produced by the Oncor® linear accelerator (linac). The MC model of an 18 MV photon beam was verified by measurement using a p-type diode dosimeter. Percent depth dose (PDD) and dose profiles were used for comparison based on three field sizes: 5×5, 10×10, and 20×20cm2. The accuracy of the CCC dosimetry was also evaluated using a plan composed of a simple parallel-opposed field (11×16cm2) in a lung phantom comprised of four tissue simulating media namely, lung, soft tissue, bone and spinal cord. The CCC dose calculation accuracy was evaluated by MC simulation and measurements according to the dose difference and 3D gamma analysis. Gamma analysis was carried out through comparison of the Monte Carlo simulation and the TPS calculated dose. RESULTS: Compared to the dosimetric results measured by the Farmer chamber, the CCC algorithm underestimated dose in the planning target volume (PTV), right lung and lung-tissue interface regions by about -0.11%, -1.6 %, and -2.9%, respectively. Moreover, the CCC algorithm underestimated the dose at the PTV, right lung and lung-tissue interface regions in the order of -0.34%, -0.4% and -3.5%, respectively, when compared to the MC simulation. Gamma analysis results showed that the passing rates within the PTV and heterogeneous region were above 59% and 76%. For the right lung and spinal cord, the passing rates were above 80% for all gamma criteria. CONCLUSIONS: This study demonstrates that the CCC algorithm has potential to calculate dose with sufficient accuracy for 3D conformal radiotherapy within the thorax where a significant amount of tissue heterogeneity exists.

A 4D biomechanical lung phantom for joint segmentation/registration evaluation.

At present, there exists few openly available methods for evaluation of simultaneous segmentation and registration algorithms. These methods allow for a combination of both techniques to track the tumor in complex settings such as adaptive radiotherapy. We have produced a quality assurance platform for evaluating this specific subset of algorithms using a preserved porcine lung in such that it is multi-modality compatible: positron emission tomography (PET), computer tomography (CT) and magnetic resonance imaging (MRI). A computer controlled respirator was constructed to pneumatically manipulate the lungs in order to replicate human breathing traces. A registration ground truth was provided using an in-house bifurcation tracking pipeline. Segmentation ground truth was provided by synthetic multi-compartment lesions to simulate biologically active tumor, background tissue and a necrotic core. The bifurcation tracking pipeline results were compared to digital deformations and used to evaluate three registration algorithms, Diffeomorphic demons, fast-symmetric forces demons and MiMVista's deformable registration tool. Three segmentation algorithms the Chan Vese level sets method, a Hybrid technique and the multi-valued level sets algorithm. The respirator was able to replicate three seperate breathing traces with a mean accuracy of 2-2.2%. Bifurcation tracking error was found to be sub-voxel when using human CT data for displacements up to 6.5 cm and approximately 1.5 voxel widths for displacements up to 3.5 cm for the porcine lungs. For the fast-symmetric, diffeomorphic and MiMvista registration algorithms, mean geometric errors were found to be [Formula: see text], [Formula: see text] and [Formula: see text] voxels widths respectively using the vector field differences and [Formula: see text], [Formula: see text] and [Formula: see text] voxel widths using the bifurcation tracking pipeline. The proposed phantom was found sufficient for accurate evaluation of registration and segmentation algorithms. The use of automatically generated anatomical landmarks proposed can eliminate the time and potential innacuracy of manual landmark selection using expert observers.

Novel multimodality segmentation using level sets and Jensen-Rényi divergence.

PURPOSE: Positron emission tomography (PET) is playing an increasing role in radiotherapy treatment planning. However, despite progress, robust algorithms for PET and multimodal image segmentation are still lacking, especially if the algorithm were extended to image-guided and adaptive radiotherapy (IGART). This work presents a novel multimodality segmentation algorithm using the Jensen-Rényi divergence (JRD) to evolve the geometric level set contour. The algorithm offers improved noise tolerance which is particularly applicable to segmentation of regions found in PET and cone-beam computed tomography. METHODS: A steepest gradient ascent optimization method is used in conjunction with the JRD and a level set active contour to iteratively evolve a contour to partition an image based on statistical divergence of the intensity histograms. The algorithm is evaluated using PET scans of pharyngolaryngeal squamous cell carcinoma with the corresponding histological reference. The multimodality extension of the algorithm is evaluated using 22 PET/CT scans of patients with lung carcinoma and a physical phantom scanned under varying image quality conditions. RESULTS: The average concordance index (CI) of the JRD segmentation of the PET images was 0.56 with an average classification error of 65%. The segmentation of the lung carcinoma images had a maximum diameter relative error of 63%, 19.5%, and 14.8% when using CT, PET, and combined PET/CT images, respectively. The estimated maximal diameters of the gross tumor volume (GTV) showed a high correlation with the macroscopically determined maximal diameters, with a R(2) value of 0.85 and 0.88 using the PET and PET/CT images, respectively. Results from the physical phantom show that the JRD is more robust to image noise compared to mutual information and region growing. CONCLUSIONS: The JRD has shown improved noise tolerance compared to mutual information for the purpose of PET image segmentation. Presented is a flexible framework for multimodal image segmentation that can incorporate a large number of inputs efficiently for IGART.

Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT.

Target definition is the largest source of geometric uncertainty in radiation therapy. This is partly due to a lack of contrast between tumor and healthy soft tissue for computed tomography (CT) and due to blurriness, lower spatial resolution, and lack of a truly quantitative unit for positron emission tomography (PET). First-, second-, and higher-order statistics, Tamura, and structural features were characterized for PET and CT images of lung carcinoma and organs of the thorax. A combined decision tree (DT) with K-nearest neighbours (KNN) classifiers as nodes containing combinations of 3 features were trained and used for segmentation of the gross tumor volume. This approach was validated for 31 patients from two separate institutions and scanners. The results were compared with thresholding approaches, the fuzzy clustering method, the 3-level fuzzy locally adaptive Bayesian algorithm, the multivalued level set algorithm, and a single KNN using Hounsfield units and standard uptake value. The results showed the DTKNN classifier had the highest sensitivity of 73.9%, second highest average Dice coefficient of 0.607, and a specificity of 99.2% for classifying voxels when using a probabilistic ground truth provided by simultaneous truth and performance level estimation using contours drawn by 3 trained physicians.

Technical note: calculation of normal tissue complication probability using Gaussian error function model.

PURPOSE: The Gaussian error function was first used and verified in normal tissue complication probability (NTCP) calculation to reduce the dose-volume histogram (DVH) database by replacing the dose-volume bin set with the error function parameters for the differential DVH (dDVH). METHODS: Seven-beam intensity modulated radiation therapy (IMRT) treatment planning was performed in three patients with small (40 cm3), medium (53 cm3), and large (87 cm3) prostate volume, selected from a group of 20 patients. Rectal dDVH varying with the interfraction prostate motion along the anterior-posterior direction was determined by the treatment planning system (TPS) and modeled by the Gaussian error function model for the three patients. Rectal NTCP was then calculated based on the routine dose-volume bin set of the rectum by the TPS and the error function model. The variations in the rectal NTCP with the prostate motion and volume were studied. RESULTS: For the ranges of prostate motion of 8-2, 4-8, and 4-3 mm along the anterior-posterior direction for the small, medium, and large prostate patient, the rectal NTCP was determined varying in the ranges of 4.6%-4.8%, 4.5%-4.7%, and 4.6%-4.7%, respectively. The deviation of the rectal NTCP calculated by the TPS and the Gaussian error function model was within +/- 0.1%. CONCLUSIONS: The Gaussian error function was successfully applied in the NTCP calculation by replacing the dose-volume bin set with the model parameters. This provides an option in the NTCP calculation using a reduced size of dose-volume database. Moreover, the rectal NTCP was found varying in about +/- 0.2% with the interfraction prostate motion along the anterior-posterior direction in the radiation treatment. The dependence of the variation in the rectal NTCP with the interfraction prostate motion on the prostate volume was found to be more significant in the patient with larger prostate.

The effect of interfraction prostate motion on IMRT plans: a dose-volume histogram analysis using a Gaussian error function model.

The Gaussian error function model, containing pairs of error and complementary error functions, was used to carry out cumulative dose-volume histogram (cDVH) analysis on prostate intensity modulated radiation therapy (IMRT) plans with interfraction prostate motion. Cumulative DVHs for clinical target volumes (CTVs) shifted in the anterior-posterior directions based on a 7-beam IMRT plan were calculated and modeled using the Pinnacle3 treatment planning system and a Gaussian error function, respectively. As the parameters in the error function model, namely, a, b and c were related to the shape of the cDVH curve, evaluation of cDVHs corresponding to the prostate motion based on the model parameters becomes possible as demonstrated in this study. It was found that deviations of the cDVH for the CTV were significant, when the CTV-planning target volume (PTV) margin was underestimated in the anterior-posterior directions, particularly in the posterior direction for a patient with relatively small prostate volume (39 cm3). Analysis of the cDVH for the CTV shifting in the anterior-posterior directions using the error function model showed that parameters a1,2, which were related to the maximum relative volume of the cDVH, changed symmetrically when the prostate was shifted in the anterior and posterior directions. This change was more significant for the larger prostate. For parameters b related to the slope of the cDVH, b1,2 changed symmetrically from the isocenter, when the CTV was within the PTV. This was different from parameters c (c1,2 are related to the maximum dose of the cDVH), which did not vary significantly with the prostate motion in the anterior-posterior directions and prostate volume. Using the patient data, this analysis validates the error function model, and further verified the clinical application of this mathematical model on treatment plan evaluations.

Technical note: dose-volume histogram analysis in radiotherapy using the Gaussian error function.

A mathematical model based on the Gaussian error and complementary error functions was proposed to describe the cumulative dose-volume histogram (cDVH) for a region of interest in a radiotherapy plan. Parameters in the model (a, b, c) are related to different characteristics of the shape of a cDVH curve such as the maximum relative volume, slope and position of a curve drop off, respectively. A prostate phantom model containing a prostate, the seminal vesicle, bladder and rectum with cylindrical organ geometries was used to demonstrate the effect of interfraction prostate motion on the cDVH based on this error function model. The prostate phantom model was planned using a five-beam intensity modulated radiotherapy (IMRT), and a four-field box (4FB), technique with the clinical target volume (CTV) shifted in different directions from the center. In the case of the CTV moving out of the planning target volume (PTV), that is, the margin between the CTV and PTV is underestimated, parameter c (related to position of curve drop off) in the 4FB plan and parameters b (related to the slope of curve) and c in the IMRT plan vary significantly with CTV displacement. This shows that variation of the cDVH is present in the 4FB plan and such variation is more serious in the IMRT plan. These variations of cDVHs for 4FB and IMRT are due to the different dose gradients at the CTV edges in the anterior and posterior directions for the 4FB and IMRT plan. It is believed that a mathematical representation of the dose-volume relationship provides another viewpoint from which to illustrate problems with radiotherapy delivery such as internal organ motion that affect the dose distribution in a treatment plan.