Dosimetric evaluation of the feasibility of utilizing a reduced number of interstitial needles in combined intracavitary and interstitial brachytherapy for cervical cancer.

PURPOSE: To evaluate the ability of the Venezia advanced multichannel tandem and ring applicator to consistently produce dosimetrically comparable plans utilizing a reduced number of needle channels, to reduce the risk of secondary complications when boosting cervical cancer treatments with high dose rate (HDR) brachytherapy. METHODS: We evaluated 26 fractions from 13 patients who were treated with HDR brachytherapy using the Venezia (Elekta) applicator. The original plans included a full load of 12-16 needles, including both parallel and 30-degree oblique needles. We replanned each original to nine new configurations, with a reduced number of two, three, four, or six needles. Comparisons included differences in percentage dose coverage to 90% of the high-risk clinical target volume, and percentage dose to 2 cm3 of the bladder, rectum, sigmoid, and bowel. We considered new plans "passing" if they remained within our standards (D90 > 100%; D2 cm3  < 85% bladder, <65% rectum, sigmoid, bowel) or did not perform worse than original. RESULTS: Removing only the two most anterior or the two most posterior needles from both sides showed 80.8% and 61.5% overall passing rate. Removal of the most anterior and posterior four needles together showed 65.4% overall passing rate. Removing all oblique needles showed 19.2% overall passing rate. Removing only left-sided or only right-sided oblique needles showed 46.2% and 23.1% overall passing, respectively. Removing only right-sided or only left-sided parallel needles separately showed 19.2% and 34.6% overall passing, respectively. Removing all parallel needles showed 11.5% overall passing rate. CONCLUSIONS: As only two replans required a full needle load to maintain dosimetric quality and 40 (76.9%), 36 (34.6%), 18 (69.2%), and 10 (19.2%) replans passed with 2, 3, 4, and 6 needles removed respectively, this indicates the potential for using a lesser number of interstitial needles during combined intracavitary and interstitial HDR brachytherapy while maintaining dosimetric quality.

Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine learning.

The purpose of this work is to assess the robustness of treatment plans when spot delivery errors were predicted with a machine learning (ML) model for intensity modulated proton therapy (IMPT). Over 6000 machine log files from delivered IMPT treatment plans were included in this study. From these log files, over 4.1  × $ \times \ $ 106 delivered proton spots were used to train the ML model. The presented model was tested and used to predict the spot position as well as the monitor units (MU) per spot, based on the original planning parameters. Two patient plans (one accelerated partial breast irradiation [APBI] and one ependymoma) were recalculated with the predicted spot position/MUs by the ML model and then were re-analyzed for robustness. Plans with ML predicted spots were less robust than the original clinical plans. In the APBI plan, dosimetric changes to the left lung and heart were not clinically relevant. In the ependymoma plan, the hot spot in the brainstem decreased and the hot spot in the cervical cord increased. Despite these differences, after robustness analysis, both ML spot delivery error plans resulted in >95% of the CTV receiving >95% of the prescription dose. The presented workflow has the potential benefit of including realistic spots information for plan quality checks in IMPT. This work demonstrates that in the two example plans, the plans were still robust when accounting for spot delivery errors as predicted by the ML model.

Quantitative evaluation of dosimetric uncertainties in electron therapy by measurement and calculation using the electron Monte Carlo dose algorithm in the Eclipse treatment planning system.

In the electron beam radiation therapy, customized blocks are mostly used to shape treatment fields to generate conformal doses. The goal of this study is to investigate quantitatively dosimetric uncertainties associated with heterogeneities, detectors used in the measurement of the beam data commissioning, and modeling of the interactions of high energy electrons with tissue. These uncertainties were investigated both by measurements with different detectors and calculations using electron Monte Carlo algorithm (eMC) in the Eclipse treatment planning system. Dose distributions for different field sizes were calculated using eMC and measured with a multiple-diode-array detector (MapCheck2) for cone sizes ranging from 6 to 25 cm. The dose distributions were calculated using the CT images of the MapCheck2 and water-equivalent phantoms. In the umbra region (<20% isodose line), the eMC underestimated dose by a factor of 3 for high energy electron beams due to lack of consideration of bremsstrahlung emitted laterally that was not accounted by eMC in the low dose region outside the field. In the penumbra (20%-80% isodose line), the eMC overestimated dose (40%) for high energy 20 MeV electrons compared to the measured dose with small diodes in the high gradient dose region. This was mainly due to lack of consideration of volume averaging of the ion chamber used in beam data commissioning which was input to the eMC dose calculation algorithm. Large uncertainties in the CT numbers (25%) resulted from the image artifacts in the CT images of the MapCheck2 phantom due to metal artifacts. The eMC algorithm used the electron and material densities extracted from the CT numbers which resulted large dosimetric uncertainties (10%) in the material densities and corresponding stopping power ratios. The dose calculations with eMC are associated with large uncertainties particularly in penumbra and umbra regions and around heterogeneities which affect the low dose level that cover nearby normal tissue or critical structures.

Prediction of the output factor using machine and deep learning approach in uniform scanning proton therapy.

PURPOSE: The purpose of this work is to develop machine and deep learning-based models to predict output and MU based on measured patient quality assurance (QA) data in uniform scanning proton therapy (USPT). METHODS: This study involves 4,231 patient QA measurements conducted over the last 6 years. In the current approach, output and MU are predicted by an empirical model (EM) based on patient treatment plan parameters. In this study, two MATLAB-based machine and deep learning algorithms - Gaussian process regression (GPR) and shallow neural network (SNN) - were developed. The four parameters from patient QA (range, modulation, field size, and measured output factor) were used to train these algorithms. The data were randomized with a training set containing 90% and a testing set containing remaining 10% of the data. The model performance during training was accessed using root mean square error (RMSE) and R-squared values. The trained model was used to predict output based on the three input parameters: range, modulation, and field size. The percent difference was calculated between the predicted and measured output factors. The number of data sets required to make prediction accuracy of GPR and SNN models' invariable was also evaluated. RESULTS: The prediction accuracy of machine and deep learning algorithms is higher than the EM. The output predictions with [GPR, SNN, and EM] within ± 2% and ± 3% difference were [97.16%, 97.64%, and 92.95%] and [99.76%, 99.29%, and 97.18%], respectively. The GPR model outperformed the SNN with a smaller number of training data sets. CONCLUSION: The GPR and SNN models outperformed the EM in terms of prediction accuracy. Machine and deep learning algorithms predicted the output factor and MU for USPT with higher predictive accuracy than EM. In our clinic, these models have been adopted as a secondary check of MU or output factors.

A complete workflow for utilizing Monte Carlo toolkits in clinical cases for a double-scattering proton therapy system.

The methods described in this paper allow end users to utilize Monte Carlo (MC) toolkits for patient-specific dose simulation and perform analysis and plan comparisons for double-scattering proton therapy systems. The authors aim to fill two aspects of this process previously not explicitly published. The first one addresses the modeling of field-specific components in simulation space. Patient-specific compensator and aperture models are exported from treatment planning system and converted to STL format using a combination of software tools including Matlab and Autodesk's Netfabb. They are then loaded into the MC geometry for simulation purpose. The second details a method for easily visualizing and comparing simulated doses with the dose calculated from the treatment planning system. This system is established by utilizing the open source software 3D Slicer. The methodology was demonstrated with a two-field proton treatment plan on the IROC lung phantom. Profiles and two-dimensional (2D) dose planes through the target isocenter were analyzed using our in-house software tools. This present workflow and set of codes can be easily adapted by other groups for their clinical practice.

Shielding verification and neutron dose evaluation of the Mevion S250 proton therapy unit.

For passive scattering proton therapy systems, neutron contamination is the main concern both from an occupational and patient safety perspective. The Mevion S250 compact proton therapy system is the first of its kind, offering an in-room cyclotron design which prompts more concern for shielding assessment. The purpose of this study was to accomplish an in-depth evaluation of both the shielding design and in-room neutron production at our facility using both Monte Carlo simulation and measurement. We found that the shielding in place at our facility is adequate, with simulated annual neutron ambient dose equivalents at 30 cm outside wall/door perimeter ranging from background to 0.07 mSv and measured dose equivalents ranging from background to 0.06 mSv. The in-room measurements reveal that the H*/D decreases when the distance from isocenter and field size increases. Furthermore, the H*/D generally increases when the angle around isocenter increases. Our results from in-room measurements show consistent trends with our Monte Carlo model of the Mevion system.

Quantitative evaluation of the performance of different deformable image registration algorithms in helical, axial, and cone-beam CT images using a mobile phantom.

The goal of this project is to investigate quantitatively the performance of different deformable image registration algorithms (DIR) with helical (HCT), axial (ACT), and cone-beam CT (CBCT). The variations in the CT-number values and lengths of well-known targets moving with controlled motion were evaluated. Four DIR algorithms: Demons, Fast-Demons, Horn-Schunck and Lucas-Kanade were used to register intramodality CT images of a mobile phantom scanned with different imaging techniques. The phantom had three water-equivalent targets inserted in a low-density foam with different lengths (10-40 mm) and moved with adjustable motion amplitudes (0-20 mm) and frequencies (0-0.5 Hz). The variations in the CT-number level, volumes and shapes of these targets were measured from the spread-out of the CT-number distributions. In CBCT, most of the DIR algorithms were able to produce the actual lengths of the mobile targets; however, the CT-number values obtained from the DIR algorithms deviated from the actual CT-number of the targets. In HCT, the DIR algorithms were successful in deforming the images of the mobile targets to the images of the stationary targets producing the CT-number values and lengths of the targets for motion amplitudes <20 mm. Similarly in ACT, all DIR algorithms produced the actual CT-number values and lengths of the stationary targets for low-motion amplitudes <15 mm. The optical flow-based DIR algorithms such as the Horn-Schunck and Lucas-Kanade performed better than the Demons and Fast-Demons that are based on attraction forces particularly at large motion amplitudes. In conclusion, most of the DIR algorithms did not reproduce well the CT-number values and lengths of the targets in images that have artifacts induced by large motion amplitudes. The deviations in the CT-number values and variations in the volume of the mobile targets in the deformed CT images produced by the different DIR algorithms need to be considered carefully in the treatment planning for accurate dose calculation dose coverage of the tumor, and sparing of critical structures.

TOPAS Simulation of the Mevion S250 compact proton therapy unit.

As proton therapy becomes increasingly popular, so does the need for Monte Carlo simulation studies involving accurate beam line modeling of proton treatment units. In this study, the 24 beam configurations of the Mevion S250 proton therapy system installed recently at our institution were modeled using the TOolkit for PArticle Simulation (TOPAS) code. Pristine Bragg peak, spread out Bragg peak (SOBP), and lateral beam profile dose distributions were simulated and matched to the measurements taken during commissioning of the unit. Differences in the range for all Percent Depth Dose (PDD) curves between measured and simulated data agreed to within 0.1 cm. For SOBP scans, the SOBP widths all agreed to within 0.3 cm. With regards to lateral beam profile comparisons between the measured and simulated data, the penumbras differed by less than 1 mm and the flatness differed by less than 1% in nearly all cases. This study shows that Monte Carlo simulation studies involving the Mevion S250 proton therapy unit can be a viable tool in commissioning and verification of the proton treatment planning system.

Comparability of three output prediction models for a compact passively double-scattered proton therapy system.

The purpose of this study was to investigate comparability of three output prediction models for a compact double-scattered proton therapy system. Two published output prediction models are commissioned for our Mevion S250 proton therapy system. Model A is a correction-based model (Sahoo et al., Med Phys, 2008;35(11):5088-5097) and model B is an analytical model which employs a function of r = (R'-M')/M' (Kooy et al., Phys Med Biol, 2005;50:5487-5456) where R' is defined as depth of distal 100% dose with straggling and M' is the width between distal 100% dose and proximal 100% dose with straggling instead of the theoretical definition due to more accurate output prediction. The r is converted to ((R-0.31)-0.81 × M)/(0.81 × M) with the vendor definition of R (distal 90% dose) and M (distal 90% dose-to-proximal 95% dose), where R' = R-0.31 (g cm-2 ) and M' = 0.81 × M (g cm-2 ). In addition, a quartic polynomial fit model (model C) mathematically converted from model B is studied. The outputs of 272 sets of R and M covering the 24 double scattering options are measured. Each model's predicted output is compared to the measured output. For the total dataset, the percent difference between predicted (P) and measured (M) outputs ((P-M)/M × 100%) were within ±3% using the three different models. The average differences (±standard deviation) were -0.13 ± 0.94%, -0.13 ± 1.20%, and -0.22 ± 1.11% for models A, B, and C, respectively. The p-values of the t-test were 0.912 (model A vs. B), 0.061 (model A vs. C), and 0.136 (model B vs. C). For all the options, all three models have clinically acceptable predictions. The differences between models A, B, and C are statistically insignificant; however, model A generally has the potential to more accurately predict the output if a larger dataset for commissioning is used. It is concluded that the models can be comparably used for the compact proton therapy system.

Quantitative evaluation by measurement and modeling of the variations in dose distributions deposited in mobile targets.

The objective of this study is to quantitatively evaluate variations of dose distributions deposited in mobile target by measurement and modeling. The effects of variation in dose distribution induced by motion on tumor dose coverage and sparing of normal tissues were investigated quantitatively. The dose distributions with motion artifacts were modeled considering different motion patterns that include (a) motion with constant speed and (b) sinusoidal motion. The model predictions of the dose distributions with motion artifacts were verified with measurement where the dose distributions from various plans that included three-dimensional conformal and intensity-modulated fields were measured with a multiple-diode-array detector (MapCheck2), which was mounted on a mobile platform that moves with adjustable motion parameters. For each plan, the dose distributions were then measured with MapCHECK2 using different motion amplitudes from 0-25 mm. In addition, mathematical modeling was developed to predict the variations in the dose distributions and their dependence on the motion parameters that included amplitude, frequency and phase for sinusoidal motions. The dose distributions varied with motion and depended on the motion pattern particularly the sinusoidal motion, which spread out along the direction of motion. Study results showed that in the dose region between isocenter and the 50% isodose line, the dose profile decreased with increase of the motion amplitude. As the range of motion became larger than the field length along the direction of motion, the dose profiles changes overall including the central axis dose and 50% isodose line. If the total dose was delivered over a time much longer than the periodic time of motion, variations in motion frequency and phase do not affect the dose profiles. As a result, the motion dose modeling developed in this study provided quantitative characterization of variation in the dose distributions induced by motion, which can be employed in radiation therapy to quantitatively determine the margins needed for treatment planning considering dose spillage to normal tissue.

Dosimetric effects of positioning shifts using 6D-frameless stereotactic Brainlab system in hypofractionated intracranial radiotherapy.

Dosimetric consequences of positional shifts were studied using frameless Brainlab ExacTrac X-ray system for hypofractionated (3 or 5 fractions) intracranial stereo-tactic radiotherapy (SRT). SRT treatments of 17 patients with metastatic intracranial tumors using the stereotactic system were retrospectively investigated. The treatments were simulated in a treatment planning system by modifying planning parameters with a matrix conversion technique based on positional shifts for initial infrared (IR)-based setup (XC: X-ray correction) and post-correction (XV: X-ray verification). The simulation was implemented with (a) 3D translational shifts only and (b) 6D translational and rotational shifts for dosimetric effects of angular correction. Mean translations and rotations (± 1 SD) of 77 fractions based on the initial IR setup (XC) were 0.51 ± 0.86 mm (lateral), 0.30 ± 1.55 mm (longitudinal), and -1.63 ± 1.00 mm (vertical); -0.53° ± 0.56° (pitch), 0.42° ± 0.60° (roll), and 0.44°± 0.90° (yaw), respectively. These were -0.07 ± 0.24 mm, -0.07 ± 0.25 mm, 0.06± 0.21 mm, 0.04° ± 0.23°, 0.00° ± 0.30°, and -0.02° ± 0.22°, respectively, for the postcorrection (XV). Substantial degradation of the treatment plans was observed in D95 of PTV (2.6% ± 3.3%; simulated treatment versus treatment planning), Dmin of PTV (13.4% ± 11.6%), and Dmin of CTV (2.8% ± 3.8%, with the maximum error of 10.0%) from XC, while dosimetrically negligible changes (< 0.1%) were detected for both CTV and PTV from XV simulation. 3D angular correction significantly improved CTV dose coverage when the total angular shifts (|pitch| + |roll| + |yaw|) were greater than 2°. With the 6D stereoscopic X-ray verification imaging and frameless immobilization, submillimeter and subdegree accuracy is achieved with negligible dosimetric deviations. 3D angular correction is required when the angular deviation is substantial. A CTV-to-PTV safety margin of 2 mm is large enough to prevent deterioration of CTV coverage.

Quantitative evaluation of patient setup uncertainty of stereotactic radiotherapy with the frameless 6D ExacTrac system using statistical modeling.

The purpose of this study is to evaluate patient setup accuracy and quantify indi-vidual and cumulative positioning uncertainties associated with different hardware and software components of the stereotactic radiotherapy (SRS/SRT) with the frameless 6D ExacTrac system. A statistical model is used to evaluate positioning uncertainties of the different components of SRS/SRT treatment with the Brainlab 6D ExacTrac system using the positioning shifts of 35 patients having cranial lesions. All these patients are immobilized with rigid head-and-neck masks, simu-lated with Brainlab localizer and planned with iPlan treatment planning system. Stereoscopic X-ray images (XC) are acquired and registered to corresponding digitally reconstructed radiographs using bony-anatomy matching to calculate 6D translational and rotational shifts. When the shifts are within tolerance (0.7 mm and 1°), treatment is initiated. Otherwise corrections are applied and additional X-rays (XV) are acquired to verify that patient position is within tolerance. The uncertain-ties from the mask, localizer, IR -frame, X-ray imaging, MV, and kV isocentricity are quantified individually. Mask uncertainty (translational: lateral, longitudinal, vertical; rotational: pitch, roll, yaw) is the largest and varies with patients in the range (-2.07-3.71 mm, -5.82-5.62 mm, -5.84-3.61 mm; -2.10-2.40°, -2.23-2.60°, and -2.7-3.00°) obtained from mean of XC shifts for each patient. Setup uncer-tainty in IR positioning (0.88, 2.12, 1.40 mm, and 0.64°, 0.83°, 0.96°) is extracted from standard deviation of XC. Systematic uncertainties of the frame (0.18, 0.25, -1.27mm, -0.32°, 0.18°, and 0.47°) and localizer (-0.03, -0.01, 0.03mm, and -0.03°, 0.00°, -0.01°) are extracted from means of all XV setups and mean of all XC distributions, respectively. Uncertainties in isocentricity of the MV radiotherapy machine are (0.27, 0.24, 0.34 mm) and kV imager (0.15, -0.4, 0.21 mm). A statisti-cal model is developed to evaluate the individual and cumulative systematic and random positioning uncertainties induced by the different hardware and software components of the 6D ExacTrac system. The uncertainties from the mask, local-izer, IR frame, X-ray imaging, couch, MV linac, and kV imager isocentricity are quantified using statistical modeling.

A simulation study investigating a Cherenkov material for use with the prompt gamma range verification in proton therapy.

In vivo range verification methods will reveal information about the penetration depth into a patient for an incident proton beam. The prompt gamma (PG) method is a promising in vivo technique that has been shown to yield this range information by measuring the escaping MeV photons given a suitable detector system. The majority of current simulations investigating PG detectors utilize common scintillating materials ideal for photons within a low neutron background radiation field using complex geometries or novel designs. In this work we simulate a minimal detector system using a material ideal for MeV photon detection in the presence of a significant neutron field based on the Cherenkov phenomenon. The response of this selected material was quantified for the escaping particles commonly found in proton therapy applications and the feasibility of using the PG technique for this detector material was studied. Our simulations found that the majority of the range information can be determined by detecting photons emitted with a timing window less than ∼50 ns after the interaction of the proton beam with the water phantom and with an energy threshold focusing on the energy range of the de-excitation of 16O photons (∼6 MeV). The Cherenkov material investigated is able to collect these photons and estimate the range with timescales on the order of tens of nanoseconds as well as greatly suppress the signal due to neutron.

A motion algorithm to extract physical and motion parameters of mobile targets from cone-beam computed tomographic images.

PURPOSE: A motion algorithm has been developed to extract length, CT number level and motion amplitude of a mobile target from cone-beam CT (CBCT) images. MATERIALS AND METHODS: The algorithm uses three measurable parameters: Apparent length and blurred CT number distribution of a mobile target obtained from CBCT images to determine length, CT-number value of the stationary target, and motion amplitude. The predictions of this algorithm are tested with mobile targets having different well-known sizes that are made from tissue-equivalent gel which is inserted into a thorax phantom. The phantom moves sinusoidally in one-direction to simulate respiratory motion using eight amplitudes ranging 0-20 mm. RESULTS: Using this motion algorithm, three unknown parameters are extracted that include: Length of the target, CT number level, speed or motion amplitude for the mobile targets from CBCT images. The motion algorithm solves for the three unknown parameters using measured length, CT number level and gradient for a well-defined mobile target obtained from CBCT images. The motion model agrees with the measured lengths which are dependent on the target length and motion amplitude. The gradient of the CT number distribution of the mobile target is dependent on the stationary CT number level, the target length and motion amplitude. Motion frequency and phase do not affect the elongation and CT number distribution of the mobile target and could not be determined. CONCLUSION: A motion algorithm has been developed to extract three parameters that include length, CT number level and motion amplitude or speed of mobile targets directly from reconstructed CBCT images without prior knowledge of the stationary target parameters. This algorithm provides alternative to 4D-CBCT without requirement of motion tracking and sorting of the images into different breathing phases. The motion model developed here works well for tumors that have simple shapes, high contrast relative to surrounding tissues and move nearly in regular motion pattern that can be approximated with a simple sinusoidal function. This algorithm has potential applications in diagnostic CT imaging and radiotherapy in terms of motion management.

Modeling and measurement of the variations of CT number distributions for mobile targets in cone-beam computed tomographic imaging.

The purpose of this study was to investigate quantitatively by measurement and modeling the variations in CT number distributions of mobile targets in cone-beam CT (CBCT) imaging. CBCT images were acquired for three targets manufactured from homogenous water-equivalent gel that was inserted into a commercial mobile thorax phantom. The phantom moved with a controlled cyclic motion in one-dimension along the superior-inferior direction to simulate patient respiratory motion. Profiles of the CT number distributions of the static and mobile targets were obtained using CBCT images. A mathematical model was developed that predicted the variations in CT number distributions and their dependence on the motion parameters of targets moving in one-dimension using CBCT imaging. The measured CT number distributions for the mobile targets varied considerably, depending on the motion parameters. The extension of the CT number distribution increased linearly with motion amplitude where maximum target elongation reached twice the motion amplitude. The CT number levels of the mobile targets were smeared over a longer distribution; for example, the CT number level for the 20 mm target dropped by nearly 30% at motion amplitude (A) equal to 20 mm in comparison with the CT number distribution of stationary targets. Frequency of motion played an important role in spatial and level variations of the CT number distributions. For example, the level of the CT number profile for the medium target (20 mm) decreased evenly by nearly 50% at A = 20 mm with high motion frequencies. Motion phase did not affect the CT number distributions for prolonged projection acquisition that included several respiratory cycles. The mathematical model of the CT number distributions of mobile targets in CBCT reproduced well the measured CT number distributions and predicted their dependence on the target size and phantom motion parameters such as speed, amplitude, frequency, and phase. The CT number distributions varied considerably with motion in CBCT. A motion model of CT number distribution for mobile targets has been developed in this work that predicted well the variations in the measured CT number profiles and their dependence on motion parameters. The model corrected the CT number distribution retrospective to CT image reconstruction where it used a first-order linear relationship between the number of projections collected in the imaging window of a mobile voxel to obtain the cumulative CT number. This model provides quantitative characterization of motion artifacts on CT number distributions in CBCT that is useful to determine the validity of CT numbers and the accuracy of localization and volume measurement of tumors in diagnostic imaging and interventional applications, such as radiotherapy.

Changes in Non-Small Cell Lung Cancer Tumor Location Secondary to Gastric Distension, Implications in the Context of Stereotactic Body Radiation Therapy.

OBJECTIVE: Stereotactic body radiation therapy (SBRT) facilitates highly conformal dose distributions to a targe tumor volume. Accurate tumor localization is extremely important, and lung tumors pose a unique challenge due to respiratory motion. Patients are required to fast before PET/CT but not before CT simulation and daily treatment, introducing potential variability from gastric distension. METHODS: A case was reviewed involving a patient with early-stage NSCLC which was simulated and treated with SBRT. PET/CT performed while fasting showed an isolated left lower lobe lesion. Following CT simulation, CT and PET/CT images were superimposed for comparison and treatment planning. RESULTS: Tumor location variation was apparent following image superimposition. Simulation CT showed significant gastric distension compared to PET/CT. The patient was resimulated while fasting, resulting in accurate and reproducible tumor localization for treatment planning. CONCLUSIONS: Gastric distension can alter tumor location and treatment volumes for radiotherapy planning, possibly resulting in inaccurate treatment administration.

Dependency of planned dose perturbation (PDP) on the spatial resolution of MapCHECK 2 detectors.

The purpose of this study is to determine the dependency of the planned dose perturbation (PDP) algorithm (used in Sun Nuclear 3DVH software) on spatial resolution of the MapCHECK 2 detectors. In this study, ten brain (small target), ten brain (large target), ten prostate, and ten head-and-neck (H&N) cases were retrospectively selected for QA measurement. IMRT validation plans were delivered using the field-by-field technique with the MapCHECK 2 device. The measurements were performed using standard detector density (standard resolution; SR) and a doubled detector density (high resolution; HR) by merging regular with shifted measurements. SR and HR measurements were fed into the 3DVH software and ROI (region of interest), planning target volume (PTV), and organ at risk (OAR)) dose statistics (D95, Dmean, and Dmax) were determined for each. Differences of the dose statistics normalized to prescription dose for ROIs between original planning and PDP-perturbed planning were calculated for SR (ΔDSR) and HR (ΔDHR), and difference between ΔDSR and ΔDHR (ΔDSR-HR = ΔDSR - ΔDHR) was also calculated. In addition, 2D and 3D γ passing rates (GPRs) were determined for both resolutions, and a correlation between GPRs and ΔDSR or ΔDHR for PTV dose metrics was determined. No considerably high mean differences between ΔDSR and ΔDHR were found for almost all ROIs and plans (< 2%); however, |ΔDSR|, |ΔDHR|, and |ΔDSR-HR| for PTV were found to significantly increase as the PTV size decreased (e.g., PTV size < 5 cc). And statistically significant differences between SR and HR were observed for OARs proximal to targets in large brain target and H&N cases. As plan modulation represented by fractional MU/prescription dose (MU/cGy) became more complex, the 2D/3D GPRs tended to decrease; however, the modulation complexity did not make any noticeable distinctions in the DVH statistics of PTV between SR and HR, excluding the small brain cases whose PTVs were extremely small (PTV = 11.0 ± 10.1 cc). Moderate to strong negative correlations (-1 < r < -0.3) between GPRs and PTV dose metrics indicated that small clinical errors for PTV occur at the higher GPRs. In conclusion, doubling the detector density of the MapCHECK 2 device is recommended for small targets (i.e., PTV < 5 cc) and multiple targets with complex geometry with minimum setup error in the DVH-based plan evaluation.

Quantitative assessment by measurement and modeling of mobile target elongation in cone-beam computed tomographic imaging.

The purpose of this study was to assess quantitatively elongation of mobile targets in cone-beam CT (CBCT) imaging by measurement and modeling. A mathematical model was derived that predicts the measured lengths of mobile targets and its dependence on target size and motion patterns in CBCT imaging. Three tissue-equivalent targets of differing sizes were inserted in an artificial thorax phantom to simulate lung lesions. Respiratory motion was mimicked with a mobile phantom that moves in one-dimension along the superior-inferior direction at a respiration frequency of 0.24 Hz for eight different amplitudes in the range 0-40 mm. A mathematical model was derived to quantify the variations in target lengths and its dependence on phantom motion parameters in CBCT. Predictions of the model were verified by measurement of the lengths of mobile targets in CBCT images. The model predicts that target lengths increased linearly with increase in speed and amplitude of phantom motion in CBCT. The measured lengths of mobile targets imaged with CBCT agreed with the calculated lengths within half-slice thickness spatial resolution. The maximal length of a mobile target was independent of the frequency and phase of motion. Elongation of mobile targets was similar in half-fan and full-fan CBCT for similar motion patterns, as long as the targets remained within the imaging view. Mobile targets elongated linearly with phantom speed and motion amplitude in CBCT imaging. The model introduced in this work assessed quantitatively the variation in target lengths induced by motion, which may be a useful tool to consider elongations of mobile targets in CBCT applications in diagnostic imaging and radiotherapy.

Interplay effect of angular dependence and calibration field size of MapCHECK 2 on RapidArc quality assurance.

The purpose of this study is to investigate an effect of angular dependence and calibration field size of MapCHECK 2 on RapidArc QA for 6, 8, 10, and 15 MV. The angular dependence was investigated by comparing MapCHECK 2 measurements in MapPHAN-MC2 to the corresponding Eclipse calculations every 10° using 10× 10 cm2 and 3 × 3 cm2 fields. Fourteen patients were selected to make RapidArc plans using the four energies, and verification plans were delivered to two phantom setups: MapCHECK 2/MapPHAN phantom (MapPHAN QA) and MapCHECK 2 on an isocentric mounting fixture (IMF QA). Migration of MapCHECK 2 on IMF was simulated by splitting arcs every 10° and displacing an isocenter of each partial arc in the Eclipse system (IMFACTUAL QA). To investigate the effect of calibration field size, MapCHECK 2 was calibrated by two field sizes (10 × 10 cm2 and 3 × 3 cm2) and applied to all QA measurements. The γ test was implemented using criteria of 1%/1 mm, 2%/2 mm, and 3%/3 mm. A mean dose of all compared points for each plan was compared with respect to a mean effective field size of the RapidArc plan. The angular dependence was considerably high at gantry angles of 90° ± 10° and 270° ± 10° (for 10 × 10/3 × 3 cm2 at 90°, 30.6% ± 6.6%/33.4%± 5.8% (6 MV), 17.3% ± 5.3%/15.0% ± 6.8% (8 MV), 8.9%± 2.9%/7.8% ± 3.2% (10 MV), and 2.2% ± 2.3%/-1.3% ± 2.6% (15 MV)). For 6 MV, the angular dependence significantly deteriorated the γ passing rate for plans of large field size in MapPHAN QA (< 90% using 3%/3 mm); however, these plans passed the γ test in IMFACTUAL QA (> 95%). The different calibration field sizes did not make any significant dose difference for both MapPHAN QA and IMFACTUAL QA. For 8, 10, and 15 MV, the angular dependence does not make any clinically meaningful impact on MapPHAN QA. Both MapPHAN QA and IMFACTUAL QA presented clinically acceptable γ passing rates using 3%/3 mm. MapPHAN QA showed better passing rates than IMFACTUAL QA for the tighter criteria. The 10 × 10 cm2 calibration showed better agreement for plans of small effective field size (< 5 × 5 cm2) in MapPHAN QA. There was no statistical difference between IMF QA and IMFACTUAL QA. In conclusion, MapPHAN QA is not recommended for plans of large field size, especially for 6 MV, and MapCHECK2 should be calibrated using a field size similar to a mean effective field size of a RapidArc plan for better agreement for IMF QA.

Theoretical modeling of mobile target broadening in helical and axial computed tomographic imaging.

PURPOSE: To investigate variations in mobile target length induced by sinusoidal motion in helical (HCT) and axial CT (ACT) imaging. A mathematical model was derived that predicts the measured broadening of the apparent lengths of mobile targets and its dependence on motion parameters, target size, and imaging couch speed in CT images. MATERIALS AND METHODS: Three mobile targets of differing lengths and sizes were constructed of tissue-equivalent gel material and embedded into artificial lung phantom. Respiratory motion was mimicked with a mobile phantom that moves in one-dimension along the superior-inferior direction with sinusoidal motion patterns. A mathematical model was derived to predict quantitatively the variations of apparent lengths for mobile targets and its dependence on phantom and imaging couch motion parameters in HCT and ACT. The model predictions were verified by length measurements of the mobile phantom targets that were imaged with the different motion patterns using CT imaging. RESULTS: The measured lengths of mobile targets enlarged or shrunk depending on the phantom motion parameters that include phantom speed, amplitude, frequency, phase and speed of the imaging couch. The target length variations were significant where some targets doubled lengths or shrunk to less than half of their actual length. The apparent lengths of mobile targets decreased if the target was moving in the same direction as the imaging couch motion and increased if the mobile target was moving opposed to imaging couch in both HCT and ACT. The model predicts well the variations in the mobile target apparent lengths and their dependence on the motion parameters. CONCLUSION: The measured and model variations of apparent lengths of mobile targets are considerable and may affect the accuracy of tumor volumes obtained from HCT and ACT. This mathematical model provides a method to quantitatively assess the length variations of mobile targets and their dependence on motion parameters of the phantom and imaging system which may have potential applications in the fields of diagnostic imaging and radiotherapy.