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Purpose: Multi-modality deformable image registration (DIR) for head & neck (HN) radiotherapy is difficult, particularly when matching computed tomography (CT) scans with magnetic resonance imaging (MRI) scans. We hypothesized that the ‘shared information’ between images of different modalities was to be found in some form of edge-based transformation, and that novel edge-based DIR methods might outperform standard DIR methods. Methods: We propose a novel method that combines gray-scale edge-based morphology and mutual information (MI) in two stages. In the first step, we applied a modification of a previously published mathematical morphology method as an efficient gray scale edge estimator, with denoising function. The results were fed into a MI-based solver (plastimatch). The method was tested on 5 HN patients with pretreatment CT and MR datasets and associated follow-up weekly MR scans. The followup MRs showed significant regression in tumor and normal structure volumes as compared to the pretreatment MRs. The MR images used in this study were obtained using fast spin echo based T2w images with a 1 mm isotropic resolution and FOV matching the CT scan. Results: In all cases, the novel edge-based registration method provided better registration quality than MI-based DIR using the original CT and MRI images. For example, the mismatch in carotid arteries was reduced from 3–5 mm to within 2 mm. The novel edge-based method with different registration regulation parameters did not show any distorted deformations as compared to the non-realistic deformations resulting from MI on the original images. Processing time was 1.3 to 2 times shorter (edge vs. non-edge). In general, we observed quality improvement and significant calculation time reduction with the new method. Conclusion: Transforming images to an ‘edge-space,’ if designed appropriately, greatly increases the speed and accuracy of DIR.

Purpose: To investigate the changes in T1 perfusion coefficients derived from analysis of Dynamic contrast enhanced (DCE) MRI of lung cancer patients undergoing SBRT. Methods: 3D DCE-MRI scans utilizing fast-field echo sequences were used in this prospective IRB-approved study of lung cancer patients undergoing SBRT during simulation, 1h after first fraction (8 Gy), after 5 fractions (40 Gy) and 4 weeks after treatment. The scan acquisition parameters were TR/TE = 3.6/1.6 ms, flip angle= 12°, voxel size = 2.7×2.7×5 mm3, 3.2 sec temporal resolution with 60 dynamics. The modified Toft's model was used to perform a voxel-by-voxel fit of the perfusion coefficients Ktrans and Kep using the Levenberg-Marquardt algorithm. Initial area under the time-concentration curve (IAUC) was evaluated at 30, 60, 90 and 180 seconds. The GTV was drawn on each coronal volume after deformable registration to the pre-treatment anatomical MRI. Changes in Ktrans, Kep and IAUC's were evaluated to assess tumor response to SBRT. Results: All time points of the first patient were analyzed. The median Ktrans values of the tumor ROI were 0.76, 0.53, 0.89, 0.28 min−1 at pre-treatment, after 8 Gy, immediately and 4 weeks post-treatment. The median Kep values were 1.47, 1.27, 1.01, and 0.90 min−1 respectively. Both Ktrans and Kep histogram distributions shifted towards lower values 4 weeks post-treatment. After normalizing IAUC values to tumor volumes, median IAUC30 values were 0.032, 0.022, 0.05 and 0.026 mol.s/L and median IAUC180 values were 0.25, 0.26, 0.43 and 0.52 mol.s/L respectively. Conclusion: Decreased Ktrans values from pre-treatment to 4 weeks post-treatment signifies decreased microvessel density and permeability. The decreased Kep values indicate a shallower slope in the delayed phase of the kinetic curve. Both distribution shifts towards lower values signify reduced contrast perfusion, implying reduced tumor perfusion. Both quantities are indicative of tumor response to SBRT treatment.

Purpose: To develop a robust planning paradigm which incorporates a tumor regression model into the optimization process to ensure tumor coverage in head and neck radiotherapy. Methods: Simulation and weekly MR images were acquired for a group of head and neck patients to characterize tumor regression during radiotherapy. For each patient, the tumor and parotid glands were segmented on the MR images and the weekly changes were formulated with an affine transformation, where morphological shrinkage and positional changes are modeled by a scaling factor, and centroid shifts, respectively. The tumor and parotid contours were also transferred to the planning CT via rigid registration. To perform the robust planning, weekly predicted PTV and parotid structures were created by transforming the corresponding simulation structures according to the weekly affine transformation matrix averaged over patients other than him/herself. Next, robust PTV and parotid structures were generated as the union of the simulation and weekly prediction contours. In the subsequent robust optimization process, attainment of the clinical dose objectives was required for the robust PTV and parotids, as well as other organs at risk (OAR). The resulting robust plans were evaluated by looking at the weekly and total accumulated dose to the actual weekly PTV and parotid structures. The robust plan was compared with the original plan based on the planning CT to determine its potential clinical benefit. Results: For four patients, the average weekly change to tumor volume and position was −4% and 1.2 mm laterally-posteriorly. Due to these temporal changes, the robust plans resulted in an accumulated PTV D95 that was, on average, 2.7 Gy higher than the plan created from the planning CT. OAR doses were similar. Conclusion: Integration of a tumor regression model into target delineation and plan robust optimization is feasible and may yield improved tumor coverage. Part of this research is supported by Varian Medical System

Purpose: Pseudodiffusion arises from the microcirculation of blood in the randomly oriented capillary network and contributes to the signal decay acquired using a multi-b value diffusion weighted (DW)-MRI sequence. This effect is more significant at low b-values and should be properly accounted for in apparent diffusion coefficient (ADC) calculations. The purpose of this study was to separate perfusion and diffusion component based on a biexponential and a segmented monoexponential model using IVIM analysis Methods. The signal attenuation is modeled as S(b) = S0[(1−f)exp(−bD) + fexp(−bD*)]. Fitting the biexponetial decay leads to the quantification of D, the true diffusion coefficient, D*, the pseudodiffusion coefficient, and f, the perfusion fraction. A nonlinear least squares fit and two segmented monoexponential models were used to derive the values for D, D*,‘and f. In the segmented approach b = 200 s/mm{sup 2} was used as the cut-off value for calculation of D. DW-MRI's of a rectum cancer patient were acquired before chemotherapy, before radiation therapy (RT), and 4 weeks into RT and were investigated as an example case. Results: Mean ADC for the tumor drawn on the DWI cases was 0.93, 1.0 and 1.13 10{sup −3}×mm{sup 2}/s before chemotherapy, before RT and 4 weeks intomore » RT. The mean (D.10{sup −3} × mm{sup 2}/s, D* 10{sup −3} × mm{sup 2}/s, and f %) based on biexponential fit was (0.67, 18.6, and 27.2%), (0.72, 17.7, and 28.9%) and (0.83,15.1, and 30.7%) at these time points. The mean (D, D* f) based on segmented fit was (0.72, 10.5, and 12.1%), (0.72, 8.2, and 17.4%) and (.82, 8.1, 16.5%) Conclusion: ADC values are typically higher than true diffusion coefficients. For tumors with significant perfusion effect, ADC should be analyzed at higher b-values or separated from the perfusion component. Biexponential fit overestimates the perfusion fraction because of increased sensitivity to noise at low b-values.« less

Purpose: To investigate early changes in tumor Apparent Diffusion Coefficients derived from diffusion weighted (DW)-MRI of lung cancer patients undergoing SBRT, as a possible early predictor of treatment response. Methods: DW-MRI scans were performed in this prospective phase I IRB-approved study of inoperable lung tumors at various time-points during the course of SBRT treatments. Axial DW scan using multi b-values ranging from 0–1000 s/mm2 were acquired in treatment position on a 3T Philips MR scanner during simulation, one hour after the first fraction (8 Gy), after a total of 5 fractions (40 Gy) and 4 weeks after SBRT delivery. A monoexponential model based on a least square fit from all b values was performed on a pixel-by-pixel basis and ADC was calculated. GTVs drawn on 4DCT for planning were mapped on the T2w MRI (acquired at exhale) after deformable registration. These volumes were then mapped on DWI scan for ADC calculation after rigid registration between the anatomical scan and diffusion scan. T2w scan on followup time points were deformably registered to the pretreatment T2 scan. Results: The first two patients in this study were analyzed. Median ADC values were 1.48, 1.48, 1.62 and 1.83 (10−3×) mm2/s at pretreatment, after 8 Gy, after 40 Gy and 4 weeks posttreatment for the first patient and 1.57, 1.53, 1.66 and 1.72 (10−3×) mm2/s for the second patient. ADC increased more significantly after 4 weeks of treatment rather than immediately post treatment, implying that late ADC value may be a better predictor of tumor response for SBRT treatment. The fraction of tumor pixels at high ADC values increased at 4 weeks post treatment. Conclusion: The observed increase in ADC values before the end of radiotherapy may be a surrogate for tumor response, but further patient accrual will be necessary to determine its value.

Purpose: To analyze interfraction motion in cervical cancer patients based on repeated measures analysis of variance of patient‐specific daily uniform margins obtained using daily CBCTimaging.Methods and Materials: Ten cervical cancer patients (stage IB‐IVA) treated with IMRT were analyzed for this study. A CTV consisting of the regional lymph nodes, upper vagina, parametria, cervix and uterus was contoured on the planning CT and daily CBCTimages. Twenty‐five CBCT scans, corresponding to each fraction were planned and attempted for each patient. The acquired CBCTimages for all the fractions were rigidly registered to the planning CT with respect to bony anatomy. The initial planning CTV was then cast onto the registered daily CBCT and were modified to reflect changes due to organ motion and deformation. Patient‐specific daily margins covering 100% of daily CTV volumes were calculated. Repeated measures analysis of variance (ANOVA) was performed on the patient specific daily margins to quantify inter‐patient and intra‐patient variations in estimated margins. Mean values within the subjects were imputed for the missing fractions. F statistics was used to test the null hypothesis that day of the treatment or fraction number had no effect on daily margins. Results: Patient specific daily uniform margins were used to generate an ANOVA table. The data shows a grand mean of 15.3 mm among all patients and fractions which represents the estimate of the average population margin. In addition, the inter‐patient standard deviation in margins is 5.1 mm and intra‐patient standard deviation is 35.4 mm indicating that the variation between patients is significant. The F statistics (F = 0.654) was significant indicating that the day of the treatment or fraction number had no influence on margin. Conclusion: Margins varied substantially between patients indicating adaptive RT approaches are needed for the treatment of cervical cancer patients.

Purpose:Radiotherapytreatment planning studies often require the use of large patient CT datasets to extract conclusions of statistical significance. However, due to various reasons such as difficulties in acquiring a larger set of CT scans and in segmenting organs in all the images, real studies are usually performed using very limited datasets. The aim of this study is to develop a novel method for generating large datasets of realistic patient geometries for treatment planning studies using a computerized phantom. Method and Materials: A NURBS‐based cardiac‐torso (NCAT) phantom was built based upon data from the female Chinese Visible Human (CVH) datasets. The NURBS control points on the organs surface were deformed to match the organ surface obtained from the limited daily cone beam CT(CBCT) dataset of each patient undergoing adaptive radiotherapy. A principal component analysis (PCA) of control point deformations was performed for the individual patient and each geometry was fit to a combination of the principal components with their corresponding weighting factors. A statistical analysis of the weighting factors was performed, and a new larger set of statistically equivalent weighting factors can be constructed, which will result in a larger geometry dataset for the patient. Results: The NCAT pelvis phantom was developed based on the segmentation of organs in the Chinese Visible Human pelvic dataset. The method for generating new datasets was applied to 20 patients undergoing adaptive radiotherapy and a variety of realistic deformed pelvic geometries was developed. Conclusion: We present a novel method for automatic generation of large datasets of patient geometries from a set of limited image datasets. The new geometries should have the same statistical uncertainties as the original datasets, however much smaller random uncertainties, and can be used in the future for adaptive radiotherapy planning studies.

Purpose: To investigate the feasibility of modeling patient‐specific beam modifiers for electron beams using the DPM MC code. Methods and material: We investigate the applicability of modeling a beam modifier ‘applicator’(vs. a multiple source model or beam fitting approach) inside the DPM code for electron beam dose calculation. DPM is only used for in‐phantom calculations; however, we have included beam modifiers such as Jaws, MLCs and now applicators within the code to improve its clinical applicability. Varian 21EX (Type III) was modeled for beam energies (6, 9 and 12 MeV) using the BEAMnrc MC code. The patient‐independent components were simulated once and the phase space (PS) is stored just below the ion chamber. As a first approximation, a preliminary model of the applicator blocks all the particles through the applicators but allows those in the open beam region. Verification of the beam modifier model with diode measurements and full PS simulations was performed using percent depth doses (PDDs) and profiles for various energies for the standard 15×15 cm2 cutout, and for square and rectangular cutouts from 4×4 to 25×25 cm2 in a homogeneous geometry. Results: PDD calculations with DPM beam modifiers showed agreement within 2% with measurements and full PS simulation for the 6MeV beam. The 9 and 12 MeV PDD calculations showed discrepancies up to 4% in the surface buildup region, and a slightly larger difference in the tail. A full 3‐d simulation currently takes roughly 10mins on 8 processors to achieve a relative uncertainty of less than 1% in voxels > dmax/2 using a 2×2×2 mm3 voxel size. Conclusions: Preliminary investigation of dose calculations with beam modifiers in DPM MC code shows good agreement with measurement. Future work will focus on modeling 16 and 20 MeV beams and their verification in a homogeneous and heterogeneous medium.

Purpose: Development of novel multi‐modality dynamic phantom and technique designed to evaluate the accuracy of a time‐resolved ultrasound device (4D‐US), used for localization and motion monitoring of the prostate during hypo‐fractionated radiotherapy. Methods: A modified dynamic thorax phantom was designed using a spherical, ultrasound compatible, prostate phantom. The phantom end of the 90° arm was submerged in a water filled plastic container. A coupling cavity between the ultrasound probe and the container wall was developed. The motion of the dynamic phantom was pre‐programmed to test the 4D‐US capability to accurately reproduce the input trajectories. The Clarity 4D‐US presents two types of scanning modes: a volumetric mode for reference volume acquisition; and an auto‐scan mode for real‐time monitoring of the target. Using the monitoring mode, the amplitude detected was compared with the input amplitude in order to determine speed‐dependent, amplitude under‐sampling effects. Using the volumetric mode, the system time lag and manifestation of volumetric distortion due to increase in the phantom speed was determined. The difference between the largest dimension of the detected volume and the reference volume diameter divided by the phantom speed, determined the time lag of the 4D‐US system. Results: In monitoring mode, the system was capable to accurately reproduce a Sup‐Inf sinusoidal phantom trajectory with 2cm amplitude and a period of ∼15s ( speed∼2.7mm/s), showing an increased amplitude under‐sampling effect as the motion period was gradually decreased to 4s. In volumetric acquisition mode, the system average time lag, calculated based on the speed induced volumetric distortion, was found to be 0.31s. Conclusion: We have developed a novel kV‐US dynamic phantom to establish the 4D‐US scanning system parameters in order to accurately detect prostate motion. The novel phantom can be used in a multi‐modality concomitant imaging scheme allowing a direct, on‐line comparison of different real‐time tracking modalities.

Purpose: To propose a machine learning algorithm‐based risk stratification method of prostate gland lesions using quantitative and texture features derived from diffusion weighted MRI (DW‐MRI). Methods: 43 patients with biopsy‐proven prostate cancer underwent DW‐MRIs of the prostate using an optimized single‐shot echo planar imaging sequence at 3T. Quantitative apparent diffusion coefficient (ADC) maps were generated via a mono‐exponential fit using three b‐values (50, 400 and 800s/mm2). 43 lesions were identified in peripheral zone (pz) and 13 in central gland (cg). Statistical and textural parameters were extracted from these lesions. Patients were grouped into low, intermediate and high risk group based on NCCN risk scoring guideline. 16 quantitative parameters (mean, kurtosis, percentile etc) and 5 texture parameters (energy, entropy, correlation, contrast and homogeneity) were calculated for each patient. Artificial Neural Network (ANN) was applied to classify patients into one of the three risk groups based on their MRI parameters. A sub‐group of patients were used as ANN training samples, and the rest was used as test samples. Classification accuracy, precision, and sensitivity were reported. Results: Both statistical and texture parameters showed strong correlation to Gleason score for only PZ tumors and not CG tumors. Classification sensitivity performed using 5 texture parameters for all tumors, pz tumors and cg tumors was 100%, 98% and 91% respectively. Sensitivity using quantitative parameters only was 97%, 95% and 100%. Finally using all the parameters, sensitivity was 93%, 95% and 81% implying that adding texture information is not very sensitive for cg tumors as compared to pz tumors. The positive predictive value (PPV) was 100% for all the above cases. Conclusion: ANN is a reasonably accurate algorithm with high sensitivity and PPV to classify prostate MRIs into risk groups. Further analysis will be done on the minimum number of training points required for the proposed technique.

Purpose: To extend the 3DVH analysis to heterogeneous patient geometries for VMAT delivery and comparing its accuracy using machine log file derived dose reconstruction method Methods: A total of 10 patient plans were selected from a regular fractionation plan to complex SBRT plans. Treatment sites in the lung and abdomen were chosen to explore the effects of tissue heterogeneity on the respective dose reconstruction algorithms. Delivered plan in the patient geometry was reconstructed by using ArcCheck Planned Dose Perturbation (ACPDP) within 3DVH software, and by converting the machine logfile to Pinnacle3 9.0 treatment plan format. In addition, delivered gantry angles between machine logfile and 3DVH 4D measurement was also compared to evaluate the accuracy of the virtual inclinometer within the 3DVH. Results: Measured ion chamber and 3DVH derived isocenter dose agreed with planned dose within 0.3±0.7 Gy and −0.5±0.8 Gy respectively. Machine log file reconstructed doses and TPS dose agreed to within 2 Gy in PTV and OARs over the entire treatment course. 3DVH reconstructed dose showed a difference of up to 3.2 Gy in maximum PTV doses compared to planned dose in hypo lung patients due to plan heterogeneity. For majority of normal structures, dose differences were within 1 Gy except for few cases, where a maximum point dose difference of up to 2.2 Gy in proximal bronchial tree dose for a hypo lung patient was seen. The average Virtual Inclinometer Error (VIE) was − 0.65±1.6° for all patients, with a maximum error of −5.16±4.54° for an SRS case. Conclusion: Both methods are capable of taking into account the plan delivery errors. 3DVH is more sensitive to these errors compared to machine log file.

Purpose: To evaluate the feasibility of planning and delivering pulsed low dose radiotherapy (PLRT) using volumetric modulated arc therapy (VMAT) on Elektalinacs. Methods: Ten patients previously treated for glioblastomamultiforme (GBM) were replanned using PLRT VMAT to deliver ten 0.2 Gy pulses separated by 3 min intervals with an effective dose rate of 0.067 Gy/min. VMAT parameters such as leaf speed and arc length were investigated to deliver 2 Gy/fraction to a total of 60 Gy to the target volume in ten subfractions or pulses. Plan quality was assessed using conformity and homogeneity indices. Absolute dose distribution for individual pulses was measured using ArcCHECK diode array. Individual pulses were analyzed for reproducibility and stability using machine log files. Machine characteristics at low monitor units and low dose rate were also investigated. Results: An optimal arc length of 140°–160° and a leaf speed of 0.18–0.25 cm/° were sufficient to provide equivalent plan coverage and stable delivery. The average time and dose rate required to deliver a single 0.2 Gy pulse was 39.5 ± 2.3 s and 49 ± 32.3 cGy/min. Average reductions in MUs for the VMAT PLRT plan compared to IMRT for PTV was 16% (Range: −5.5%–36.1%) and 10.9% (Range: −18.4%–32.3%) for the initial and boost plan. A significant improvement was seen in maximum doses to all sensitive structures when planned with VMAT PLRT. The average absolute dose gamma passing rate for the 10 pulses combined and 2 Gy plan were 91.6 ± 2.5% and 97.3 ± 1.2%, respectively. Cumulative monitor units, dose rate, gantry angles, and leaf positions evaluated using machine log files were within 2% for all pulses. Conclusions: Elekta linacs are capable of delivering reproducible and stable PLRT plans. A prospective clinical study employing PLRT is currently in development.

Purpose: To evaluate the mechanical stability and dosimetric accuracy of planning and delivering pulsed low‐dose radiotherapy (PLRT) using volumetric modulated arc therapy (VMAT) on Elekta linacsMethods: Ten patients previously treated for Glioblastoma Multiforme were replanned using PLRT VMAT to deliver ten 0.2 Gy pulses separated by 3 min intervals with an effective dose rate of 0.067 Gy/min. VMAT parameters such as leaf speed and arc length were optimized to deliver 2 Gy/fraction to a total of 60 Gy to the target volume in ten sub‐fractions or pulses. Plan quality was assessed using conformity and homogeneity indices. Absolute dose distribution for individual pulses was measured using the Arccheck cylindrical diode array. Individual pulses were analyzed for reproducibility and stability using machine log file saved in clinical mode. Machine characteristics at low monitor units and low dose rate were also investigated. Results: An optimal arc length of 140 – 160 degree and a leaf speed of 0.18 − 0.25 cm/degree were sufficient to provide stable delivery and equivalent plan coverage to IMRT. The average time and dose rate required to deliver a single 0.2 Gy pulse was 39.5 ± 2.3 seconds and 49 ± 32.3 cGy/min. Average reduction in MUs for the PLRT plan compared to IMRT for PTV was 16.0% (Range: −5.5% to 36.1%). Significant improvement was seen in maximum doses to all sensitive structures when planned with VMAT PLRT. The average absolute dose gamma passing rate for the 10 pulses combined and 2 Gy plan were 91.6 ± 2.5% and 97.3 ± 1.2%. Cumulative monitor units, dose rate, gantry angles and leaf positions evaluated using machine log files were within 2% for all pulses. Flatness and symmetry were within Elekta specifications. Conclusions: Elekta linacs are capable of delivering reproducible and stable PLRT plans. Prospective clinical study employing PLRT is currently in process.

Purpose: To validate the dosimetric accuracy and sensitivity of ArcCheck for IMRT and VMAT QA. Methods: Arccheck is a cylindrical 3D phantom for IMRT and arc delivery. It is a 3D array of 1386 diodes arranged in a helical geometry at a depth of 2.9 cm. The dosimeter was evaluated for dose rate, angular dependence and systematic setup errors. Effect of planning parameters such as couch attenuation and control point (CP) spacing was investigated for various IMRT and VMAT plans. Diodes sensitivity to leaf position errors and gantry errors was studied by simulating errors in various arc delivery plans. Gamma evaluation criteria of 3%, 2mm was used for absolute dose comparison for clinical IMRT and VMAT plans. Results: Diodes show a dose rate dependency of 2% from 600 to 200 MU/min and 5% below 50 MU/min. An angular dependence of 5% was seen for a 20x30 cm2 field size. Prostate and HN IMRT plans delivered at planned angles showed lower gamma passing rate (94.3%) compared to mapcheck measurements (99.3%) delivered at 0° gantry angles. Accounting for couch attenuation in the calculation improved the average passing rate to 97 %. For a 3×40 cm2 static arc the gamma passing rate dropped from 92% to 83% when CP spacing changed from 2° to 6° but larger field sizes were not affected. Introducing a 1 mm setup error in AP, LR and SI direction resulted in 5%, 4.5% and 10% drop in passing rate for a HN VMAT plan. A 2mm leaf position error in one leaf and 3° error in one CP showed 6% and 5% difference in profile comparison for various arc deliveries. Conclusions: ArcCHECK is a robust and sensitive QA tool for IMRT and VMAT delivery. Future work will evaluate a CP by CP analysis to understand complex VMAT deliveries.

Purpose: To obtain cumulative doses from external beam (EB) IMRT and HDR interstitial brachytherapy (BT) for prostate cancer. Methods and Materials: A tool was developed for multimodality image manipulation and registration within our current treatment planning framework to obtain cumulative doses from BT and EB‐IMRT. The HDR BT boost performed under transrectal ultrasound guidance is planned using Nucletron Oncentra prostate TPS. A total implant dose of 21 Gy is administered in two 10.5 Gy fractions. The daily external beam dose to the PTV was 2 Gy given in 23 fractions and planned with IMRT using Pinnacle TPS. US DICOM images, structures and 3‐D dose matrix were pre‐processed for import with in Pinnacle TPS. US and CTimages were rigidly registered as a preliminiary step inside Pinnacle based on prostate contours on two imagesets and implanted visicoils markers. A linear‐quadratic model is applied to calculate the total equivalent dose in 2Gy fractions (EQD2) with αβ=4 for prostate. Tools were developed to perform deformable registration based on biomechanical model of human organs and the finite element analysis methods. Result: A biomechanical model of US and CT prostate volume was generated on a clinical case using 5544 nodes (5240 subvolumes). The deformation vector field generated after deforming prostate contours was used to deform 3D USdose matrix on to CT grid. Using EQD2 of 50.4 Gy from brachytherapy and 46 Gy from IMRT, the cumulative D99 and D95 to CT prostate volume was 98.2 Gy and 100.4Gy. Conclusion: There is a lack of availability of commercial TPS in which EBRT and BT plans can be fully integrated. The tool provides a robust and accurate means of obtaining cumulative doses from brachytherapy and EB‐ IMRT. It also has the potential to incorporate doses from brachytherapy during inverse planning adaptive optimization of external beam IMRT.

Purpose: To develop efficient single and partial arc VMAT plans in terms of dosimetric accuracy and mechanical stability. Methods and Materials: Delivery efficiency of single and partial arc plans for various treatment sites (2 HN, 3 Hypolung, 2 SRS and 1 Hypoprostate) optimized with SmartArc in Pinnacle3 ™ 9.0 were tested on Elekta synergy and Axesse machines. Mechanical accuracy was evaluated using an application that records real‐time VMAT delivery information using the MLC log files. Dosimetric accuracy was performed using Delta4 and Arccheck cylindrical QA phantoms. Results: Results based on various treatment sites showed that single and partial arc VMAT plans satisfied all the re‐planning objectives. For Elekta machines, dose rate fluctuated significantly for plans with higher leaf motion, and dynamic leaf gap of 2 mm and leaf speed of 0.46 cm/deg were optimum for all the plans. Constraining the leaf motion resulted in a longer delivery time for all the plans. Lowering the control points by choosing partial arcs 6° spacing saved MUs up to 25% compared to full arc 4° spacing. Segment shapes were typically not very smooth. Gantry and leaf positions were within tolerance for all delivered plans. Compared to IMRT, PTV coverage and OAR sparing were better or comparable in all cases. MUs were reduced by 40% and 27% for HN and SRS plans. Delivery times for HN and Hypoprostate cases were within 2 mins (5 mins for HypoLung). Dosimetric analysis with Delta4 and Arccheck QA resulted in 98% and 96.5% of detectors with a gamma index (3%/2mm) < 1 respectively for all cases. Conclusion: SmartArc generated efficient single and partial arc VMAT plans in terms of lower MUs, shorter treatment time and better target coverage and OAR sparing compared to IMRT. The dosimetric and mechanical accuracy of these plans were within QA and machine tolerances.

Purpose: Intra‐fraction variability of lungtumor position during radiotherapy treatment may cause large dose discrepancy especially for VMAT delivery. Purpose of this work is to reconstructtumor 3D motion trajectory during VMAT delivery in real‐time based on concomitant CBCT projection images and detect motion irregularities to quantify them for further treatment decision. Method and Materials: A tool was designed to reconstructlungtumor 3D trajectory during VMAT delivery in real‐time. Our method allows us to detect and quantify abrupt inter‐frame motion during treatment. Tumor 2D position in CBCT projection image is obtained from its registration to certain phase of 4DCT DRR. To assess the efficacy of our method, we developed a lung‐like phantom moving in pre‐determined motion patterns from regular to complex (a Lujan model and three real tumor trajectories). The complex tumor trajectory included an intentional abrupt change in position as well as baseline shifts, amplitude and period variations. Detected trajectory based on our method was compared with the tumor actual trajectory for two tumors, one regular in shape with good contrast (tumorl) and one irregular in shape with poor contrast to adjacent material (tumor2). Results: Experiments show good accuracy in phantom study. For tumorl, reconstructed mean position is (−0.36±0.4, −0.1±0.6, − 0.2±0.5) mm from actual mean position. For tumor2, reconstructed mean position is (−0.34±0.9, 0.0±0.2, −0.1±0.2) mm from actual mean position. When tumors move under abnormal patient breathing curve, the intentional position shift can be detected, which shows the potential of our tool in clinical usage. Conclusion: We successfully developed a tool that can be used to track tumor trajectory during VMAT delivery in real‐time. Our technique allows us to characterize tumor's motion during treatment and enables one to adjust or stop treatment when tumor motion is out of pre‐established tolerance range. Research sponsored by Elekta and Philips

Purpose: For small solitary lesions in the lung, we have adopted a protocol for hypofractionated treatment using multiple conformal or IMRT beams. Since the lesion is near unit density and the surrounding lungtissue is low density, the loss of scatter in the lungtissue can reduce the dose to the PTV, when, in reality, the CTV or ITV may be adequately covered with margin. We have performed calculations using several ways to create the PTV and to normalize the treatment plan to provide adequate coverage of the CTV or ITV with margin while minimizing hot spots which result from normalizing to the low density PTV. Method and Materials: Gated CT scans were obtained on 2 patients and target volumes were drawn on axial series representing several individual phases of the breathing cycle. An ITV was then created and used for planning. In addition, a PTV was created by adding a clinically relevant margin around the ITV. The resulting treatment plan was then used to calculate the dose delivered to the CTV for each of the phases of the breathing cycle using different normalization methods. ITV coverage was also studied by moving the ITV inside the PTV to simulate setup error. Results: 2 patients were planned using the above mentioned technique. Properly selected normalization results in adequate CTV and ITV coverage through all phases of the breathing cycle. Effect of setup error also results in adequate ITV coverage as the high dose region moves with the ITV. Conclusion: Small isolated lesions in the lung can be treated with hypofractionation, but care should be taken in selecting the normalization technique to minimize the creation of unnecessary hotspots. Coverage of the near unit density lesions is not predicted by the low density PTV coverage.

Purpose: To derive an appropriate CTV‐to‐PTV margin that will ensure delivery of 100% of the prescribed dose to 95% (on average) of daily CTV's during a treatment course. Method and Materials: Daily CBCTimages are taken for setup of Stage I–IVa cervical cancer patients at our institution. Daily CTV contours drawn on these images take into account the effect of interfraction motion due to organ motion/deformation and tumor regression. To date, we have acquired CBCT data for 15 patients out of which 5 have been analyzed so far. On each CBCT, expert radiation oncologists manually segment CTV, bladder and rectum of that particular day. The CTV contours from all the CBCT scans of the same patient are superimposed on the planning CT scan after rigidly registering each CBCT scan to the planning CT scan. A uniform margin of 0, 3, 5, 7, 10, or 15 mm etc is added around the planning CTV and the average volume of CTV missed over all fractions is analyzed. The isotropic margin that encompasses 95% of daily CTVs on an average is considered adequate for the patient. Results:Statistical analysis of 5 patient's shows that a 7 mm margin is sufficient to cover on an average 98% (min: 95.2, max: 99.6) of daily CTVs of these 5 patients. However a bigger margin of 15 mm is required to adequately cover the high risk cervix region. The mean of the standard deviations in average volume of CTV covered for these 5 patients at 7 mm margin is 2.4 % indicating large inter‐patient variation. Analysis of more patients is required to reduce variation in average CTV volumes among patients. Conclusion: Clinical implementation of CBCTimaging devices has made possible margin evaluation based on daily images that provides a more accurate means of accounting for interfraction motion.

Purpose: Multi‐leaf collimator(MLC) based intensity‐modulated radiosurgery (IMRS) often results in large number of monitor units (MU) for patients with multiple brain lesions. Compensator based IMRS, however, may dramatically reduce MU. The purpose of this study is to quantify the reduction of MU for IMRS of multiple brain lesions using solid tissue compensators. Method and Materials: Patients with multiple braintumors were selected for our study. For each patient, Varian Eclipse TPS was used to generate an MLC based IMRS plan consisting of 10–11 coplanar beams. The prescription dose for a typical IMRS treatment is 1800–2000 cGy delivered in 1 fraction using a 6 MV photon beam. IMRS plans were generated on 2 patients. The optimal fluence maps from IMRS plan were exported to the compensator generating system to generate compensators for each field. The compensator files are imported back to Eclipse to calculate MUs for the compensator fields. Eclipse TPS was modified to allow compensator based planning and evaluation inside Eclipse. Finally, we compared MLC and compensator plans in terms of MUs and target and normal structure coverage. Results: Compensators offer superior resolution compared to MLCs and are easier and faster to plan. DVH analysis from both patients shows adequate target coverage for both IMRS and compensator plans. More sparing of normal tissues in compensator plan was observed sometimes. The MUs were reduced by factors of more than 3 compared to an MLC based IMRS plan. Conclusion: Compensator based IMRS can dramatically reduce the number of MU needed for multiple brain lesion radiosurgery as compared to an MLC based IMRS plan while preserve prescribed dose coverage. Conflict of Interest: This work is partially supported by .decimal Inc.

IMRT often requires delivering small fields which may suffer from electronic disequilibrium effects. The presence of heterogeneities, particularly low-density tissues in patients, complicates such situations. In this study, we report on verification of the DPM MC code for IMRT treatment planning in heterogeneous media, using a previously developed model of the Varian 120-leaf MLC. The purpose of this study is twofold: (a) design a comprehensive list of experiments in heterogeneous media for verification of any dose calculation algorithm and (b) verify our MLC model in these heterogeneous type geometries that mimic an actual patient geometry for IMRT treatment. The measurements have been done using an IMRT head and neck phantom (CIRS phantom) and slab phantom geometries. Verification of the MLC model has been carried out using point doses measured with an A14 slim line (SL) ion chamber inside a tissue-equivalent and a bone-equivalent material using the CIRS phantom. Planar doses using lung and bone equivalent slabs have been measured and compared using EDR films (Kodak, Rochester, NY).

Purpose: To experimentally determine the impact of adaptive diffusion smoothing (ADS) on the delivery accuracy and efficiency of IMRT fields. Method and Materials:IMRToptimization was performed on several cases with and without the use of an ADS penalty applied within the objective function. The ADS penalty is based on diffusion principles and promotes smoothing in beam areas that are not essential to meeting the cost function objectives. Previous studies have shown that the use of the ADS penalty results in IMRT plans that are dosimetrically equivalent, less complex, and require fewer MU to deliver compared to standard IMRT. All plans were sequenced and delivered via step‐and‐shoot delivery. Film and ion‐chamber dosimetry were performed, and the total MU, delivery time, and differences between convolution/superposition calculations and film measurements for standard and ADS IMRT beams were evaluated. Results: Measurements verified that IMRT plans optimized using the ADS penalty were less likely to exhibit small regions of disagreement due to factors such as tongue‐and‐groove compared to standard IMRT plans. In particular, the in‐field agreement between calculations and measurements for the ADS plans was superior to the more modulated standard IMRT plans. The use of ADS resulted in the area outside a +/− 5 cGy criteria between calculations and film measurements decreasing from 3.7 to 1.8 % in a head/neck example and from 10.8 to 6.7 % in a prostate example. In addition, the total MU for SMLC delivery was reduced by 20 to 45 % in all cases with no loss in plan quality according to the DVHs and dose metrics. Conclusion: The use of the ADS penalty inside an inverse IMRT plan objective function reduces beam complexity without sacrificing dosimetric quality and results in significantly more efficient and accurate delivery of IMRT fields. Supported in part by NIH grant P01‐CA59827.

Purpose: To validate a method of retrospective dose reconstruction that uses real‐time intra‐treatment patient motion data that is synchronized with MLC leaf positions during IMRT treatments. Method and Materials:IMRT fields from an IRB‐approved prostate protocol were delivered to a water‐equivalent phantom on a programmable translation stage. Kodak XV film was placed at 5 cm depth with an SSD of 95cm and marked to register it with the Monte Carlo (MC) calculation grid. Motion was synchronized with beam delivery by using the target signal to trigger motion. Film measurements were repeated for each beam while the phantom was stationary, and while moving with both idealized and clinically measured motion profiles. MLC leaf positions and fluence state for each beam were obtained from the Varian DynaLog files. MC dose accumulation was performed which incorporated the real‐time phantom motion, DynaLog files and beam state. Films were digitized and compared to the results of the MC calculations. Results: Film measurements in stationary phantoms were measured three times on two machines. The measured dose distributions were compared and showed an average difference of 0.38 +/− 1.53 cGy. The average difference between MC and film measurements of the moving phantom was 0.44 +/− 3.4 cGy and was independent of the motion profile. Measured dose patterns, for both stationary and moving phantoms, were generally well reproduced by MC dose accumulation, including tongue‐and‐groove and motion related features. Doses in moving and static phantoms were compared, for both films and simulations. The measured dose deviations due to motion were well‐characterized by the MC dose accumulation method and not significantly different when a static phantom was compared to MC calculation. Conclusion: Real‐time motion and machine data may be used to reconstruct the dose delivered to the target volume, and may serve as a basis for dynamic refinement of treatments.

Purpose: To present a dose accumulation method that explicitly incorporates real‐time target volume motion, and real‐time machine configuration, MLC motion and fluence state. Method and Materials: Effects of inter‐ and intra‐fraction motion on dose distributions delivered to a target volume are typically estimated by statistical methods that rely on idealized assumptions such as constant random and systematic errors, and constant offsets, periods and amplitudes of motion over the entire course of therapy. In addition, implementation and delivery related issues such as leaf sequencing limitations, spatial interplay between MLC leaf and target volume motion, and temporal interplay of the fluence state in IMRT, are assumed to average out during treatment. To include these effects, a dose accumulation technique is proposed which explicitly incorporates real‐time target volume motion data, and real‐time treatment machine data. Several technologies are becoming available to continuously monitor (10–30 Hz) the patient position, organs‐at‐risk, and the target volume during therapy. Likewise, real‐time (20 Hz) machine configuration, leaf position and fluence state data, is currently available in the Varian DynaLog files. These datasets are synchronized at the beginning of each beam; and the Monte Carlo method, which inherently accounts for time dependence, was used for dose calculations. Results: By synchronizing target volume position, machine configuration, leaf positions and fluence state, with sub‐second resolution, the dose delivered by each beam, may be accumulated. Such accumulated dose distributions reflect the interplay between target volume motion, MLC leaf motion and other machine‐related delivery effects based on real‐time, patient specific, measured data. The delivered dose distribution to date may then be compared to the planned dose distribution to provide input for dynamic refinement of treatment planning and delivery.Conclusion:Delivered target volume dose may be reconstructed using real‐time motion and machine data, and serve as a basis for dynamic refinement of treatments.

Purpose: To report on use of a thoroughly benchmarked MC dose calculation algorithm as an accurate tool for IMRT delivery and QA, in patient‐like media, where direct measurements for routine QA are impractical. Methods and material: We have developed a source model to investigate dosimetric effects related to MLC based delivery techniques such as step‐and‐shoot and sliding window using the DPM MC code. The model incorporates details of the Varian, 120‐leaf MLC and has been comprehensively verified against measurements in homogeneous and heterogeneous phantoms. As part of this development, we have investigated an efficient algorithm, using adaptive kernel density estimation for sampling phase space files. Using this accurate source model, we have studied beams that were sequenced with 1% and 10% fluence intervals for prostate, brain, head and neck and breast IMRT beams. Dose differences between SMLC and DMLC delivery types were evaluated in homogeneous and heterogeneous media (bone, lung and low‐density slabs) using DPM. We have also investigated dosimetric differences between optimized planned leaf sequences and actual delivered sequences, using machine log (Dynalog) files, which capture the physical leaf positions during delivery. Results: Benchmarking of the source model showed average agreement with measurements within 1%/2 mm. For a given fluence interval, calculated dose differences between SMLC and DMLC delivery techniques are different in homogeneous and heterogeneous media. Dose differences of up to 10% were found between plans developed with 1% and 10% fluence intervals for either SMLC or DMLC delivered sequences. Calculated dose differences of up to ±3cGy were observed between planned and delivered sequences (computed using the Dynalog files) for a prostate beam; these differences were in good agreement with film measurements. Conclusions: A well commissioned MC‐based dose algorithm provides a useful tool to study dosimetric issues related to fluence modulation and static versus dynamic delivery in IMRT.

Purpose: To investigate the adaptive kernel density estimation (AKDE) method as an alternative to recycling phase‐space (PS) files or histogram binning during MC accelerator simulation. The AKDE approach has the potential to mitigate statistical “noise” issues (due to latent variance) in the PS data, without the need for an overwhelmingly large number of particles. Method and Materials: We have implemented a nonparametric density estimation technique, the AKDE method, to generate additional PS variables in the vicinity of simulated PS points in MC accelerator simulation. The method involves the placement of kernels at simulated PS points, and the window‐width is allowed to vary based on the density of the PS points. After successfully testing the method for sampling 1‐D and 2‐D exponentials we sampled PS files generated from accelerator simulations. The original PS (x,y,u,v,E) was reduced to a rotationally invariant PS (r,θα,E) assuming azimuthal symmetry above the collimating jaws. The new PS point (r',θ',α',E') is generated by sampling in the vicinity of (r,θ,α,E) To test the method we simulated dose profiles for a 10×10cm2 field using 1×106 particles in the PS file as input. Results: Results indicate that a minimum number of PS points are needed to allow accurate density estimation. Preliminary calculations using a global window‐width for each PS variable produce relatively smooth profiles even with as little as 10 million particles in the PS file. However, profiles with the Epanechnikov kernel appear to be smoother and in better agreement with the original PS versus either Gaussian or uniform kernels. Conclusion: By generating unbiased PS samples “on‐the‐fly” in the neighborhood of simulated PS points AKDE has been shown to be a promising alternative to PS recycling or histogram‐binning (source modeling), where binning effects and propagation of systematic latent uncertainties may pose potential problems.

Purpose: To investigate dosimetric differences between SMLC and DMLC IMRT delivery techniques in heterogeneous media using MC. We hypothesize that subtle differences in the energy spectra between SMLC and DMLC, which may not be detected in water, will be accentuated in inhomogeneous, patient-like tissues. With the ability to account for MLC design details and perform accurate transport in heterogeneous media, MC provides a powerful means to evaluate IMRT delivery methods and may be useful as a tool for IMRT QA. Method and Materials: The BEAMnrc code was used to simulate patient independent components of a Varian 21EX linac. The resultant Phase space file was then used as input for the DPM MC code which simulates the jaws, 120-leaf MLC geometry, and the patient or phantom. Transport through the jaws and MLC is accomplished using multiple scatter photon transport. Sampling algorithms have been developed for SMLC and DMLC by randomly sampling leaf segments based on the MU index. Accuracy of the simulations was assessed in a homogeneous media when compared to measurements in a solid water phantom. To assess the impact of MLC design and delivery technique in heterogeneous media, dose was calculated in a phantom consisting of material slabs ranging in density from lung to cortical bone using SMLC and DMLC leaf sequences. Results: Excellent agreement was seen between the Monte Carlo models for SMLC and DMLC sequencing when compared to the respective film measurements in homogenous media. Significant differences, up to 10%, were seen between the delivery techniques in lung equivalent media when calculated in heterogeneous slab geometry. Conclusion: This study provides evidence that heterogeneous tissue may exacerbate energy differences between SMLC and DMLC delivery methods. Such differences may be clinically important considering that they will not be detected during QA which is typically conducted using homogeneous phantoms.

regression adjustment revealed the Cb AUC 5 group was less likely to experience all-grade hem TEAEs, and grade 3/4 thrombocytopenia and anemia, compared with the AUC 6 group (all OR