Purpose: An open‐source, convolution/superposition based kV‐treatment planning system(TPS) was developed for small animal radiotherapy from previously existed in‐house MV‐TPS. It is flexible and applicable to both step and shoot and helical tomotherapy treatment delivery. For initial commissioning process, the dose calculation from kV‐TPS was compared with measurements and Monte Carlo(MC) simulations. Methods: High resolution, low energy kernels were simulated using EGSnrc user code EDKnrc, which was used as an input in kV‐TPS together with MC‐simulated x‐ray beam spectrum. The Blue Water™ homogeneous phantom (with film inserts) and heterogeneous phantom (with film and TLD inserts) were fabricated. Phantom was placed at 100cm SSD, and was irradiated with 250 kVp beam for 10mins with 1.1cm × 1.1cm open field (at 100cm) created by newly designed binary micro‐MLC assembly positioned at 90cm SSD. Gafchromic™ EBT3 film was calibrated in‐phantom following AAPM TG‐61 guidelines, and were used for measurement at 5 different depths in phantom. Calibrated TLD‐100s were obtained from ADCL. EGS and MNCP5 simulation were used to model experimental irradiation set up calculation of dose in phantom. Results: Using the homogeneous phantom, dose difference between film and kV‐TPS was calculated: mean(x)=0.9%; maximum difference(MD)=3.1%; standard deviation(σ)=1.1%. Dose difference between MCNP5 and kV‐TPS was: x=1.5%; MD=4.6%; σ=1.9%. Dose difference between EGS and kV‐TPS was: x=0.8%; MD=1.9%; σ=0.8%. Using the heterogeneous phantom, dose difference between film and kV‐TPS was: x=2.6%; MD=3%; σ=1.1%; and dose difference between TLD and kV‐TPS was: x=2.9%; MD=6.4%; σ=2.5%. Conclusion: The inhouse, open‐source kV‐TPS dose calculation system was comparable within 5% of measurements and MC simulations in both homogeneous and heterogeneous phantoms. The dose calculation system of the kV‐TPS is validated as a part of initial commissioning process for small animal radiotherapy. The kV‐TPS has the potential for accurate dose calculation for any kV treatment or imaging modalities.

Purpose:Proton beam profile measurement is more time‐consuming than photon beam. Due to the energy modulation during proton delivery, chambers have to move step‐by‐step instead of continuously. Multi‐ion chamber arrays are appealing to this task since multiple measurements can be performed at once. However, their utilization suffers from sparse spatial resolution and potential intrinsic volume‐averaging effect of the disk‐shaped ion chambers. We proposed an approach to measure proton beam profiles accurately and efficiently.Methods:Mevion S250 proton system and IBA Matrixx ion chamber arrays were used in this study. Matrixx has interchamber distance of 7.62 mm, and chamber diameter of 4.5 mm. We measured the same beam profile by moving the Matrixx seven times with 1 mm each time along y axis. All 7 measurements were superimposed to get a “finer” profile with 1 mm spatial resolution. Coarser resolution profiles of 2 mm and 3 mm were also generated by using subsets of measurements. Those profiles were compared to the TPS calculated beam profile. Gamma analysis was performed for 2D dose maps to evaluate the difference to TPS dose plane.Results:Preliminary results showed a large discrepancy between the TPS calculated profile and the single measurement profile with 7.6 mm resolution. A good match could be achieved when the resolution reduced to 3 mm by adding one extra measurement. Gamma analysis for 2D dose map of a 10×10 field showed a passing rate (γ ≤ 1) of 90.6% using a 3% and 3mm criterion for single measurement, which increased to 92.3% for 2‐measurement superimposition, and slightly further increased to 92.9% for 7‐measurement superimposition.Conclusion:The results indicated that 2 measurements shifted by 3mm using Matrixx generated a smooth proton beam profile with good matching to Eclipse beam profile. We suggest using this 2‐measurement approach in clinic for double scattering proton beam profile measurement.

Significant improvements on radiation dose planned and delivered have occurred with the advent of intensity modified radiation therapy (IMRT) and image guided RT (IGRT). However, the fundamental questions remains: is dose planned equivalent to dose delivered? In an effort to look at this fundamental question, a total of 490 prostate patients' plans were analyzed. Daily online computed tomographic (CT)-based image guidance was performed for all treatment fractions. To evaluate the actual dose deposited to the patient, an adaptive dose recalculation (ADR) process was used. As such, the dose was computed in each one of the daily CTs. A deformable registration code provides deformation maps that are used to map back the dose to the planning CT. Adding the dose mapped back can generate the cumulative dose. In this way we can compare the planned dose with the adaptive cumulative dose that was computed. Table 1 displays the comparison results between the delivered dose and the planned dose. For the delivered dose the one computed with the ADR process was used. For the target we consider as deviation (+) or (-) for the chosen criteria. For the organs at risk we consider as deviation when the actual dose is larger than the planned dose.Poster Viewing Abstracts 2593; Table 1Structure/Criteria% of patients where actual and planned dose differ by < 2%% of patients where actual and planned dose differ by < 5%% of patients where actual and planned dose differ by < 10%PTV Dmean86.9398.97100PTV D9564.2887.3596.33Rectum D2079.3490.5995.91Rectum D5074.0287.3294.48Rectum D6068.9186.0994.27Rectum D7564.2183.2393.86Bladder D2080.4992.4099.18Bladder D5075.5788.7197.33Bladder D6073.5186.0495.89Bladder D7570.2380.0494.66 Open table in a new tab For modern RT practice with routine online IGRT, there appears to be further room for improvement in delivered dose relative to planned dose. Only 80% to 90% of patients had variation ≤5% for most dosimetric endpoints due primarily to intrafraction motion and deformation.

Purpose/Objective(s)Evaluation of what happens during treatment is becoming common practice. The amount and quality of information that can be gathered depends on how complex and labor intense the techniques are along with access to various types of treatment information. We validated, in many clinics, a combined set of complementary techniques to analyze the pros and cons of each technique along with the potential benefits of using the techniques collectively.Materials/MethodsFor the IV, two different approaches where taken. The first one (named IV1) used a particular fraction portal picked by the user as the reference and was compared with subsequent daily fraction portals. With the second approach (named IV2), the portal reference fraction was validated by a portal dose calculation based on the planning information. In both approaches, IV1 and IV2, the metric to compare portals was the gamma metric in conjunction with an analysis procedure enacted when the threshold for the gamma metric was violated. We also used the ADR approach. When a CBCT was available, the dose on the daily CBCT was computed. Mapping back and adding the daily doses using the planning CT as the frame of reference generated a cumulative dose. The system allowed evaluation of daily and cumulative doses and DVHs.ResultsThe IV1 approach has been implemented in 84 clinics for 3 years. A total of more than 6 million portals have been analyzed. The IV1 technique enables improving dosimetric consistency by approximately 6%. The level of intervention needed depends on the anatomical site. For head and neck, lung, and breast, approximately 20% of the weekly portals triggered an action level that required analysis during the course of treatment. For pelvis, the trigger level was 13% and prostate less than 10%. The approach IV2 has been implemented, for several months, in 5 clinics so far. The level of consistency improvements and the behavior per anatomical site exhibited similar behavior to IV1; however IV2 enabled detection of potential issues arising from planning such as the entrance beams going through the moveable couch arms or daily couch position that potentially affected patient dosimetry. The weakness with IV1 and IV2 was that flagged issues may not be clinically relevant. Analyzing each IV failure with ADR as a surrogate to the actual dose delivered to the patient permitted a decision if action was potentially needed for a clinically relevant issue; ADR’s weakness was that it was based on a static CT that may not have represented the conditions measured via portals at the time of treatment.ConclusionDifferent implementations of IV and ADR have been evaluated in many clinics. Even though each technique is valuable on its own, when used collectively, the techniques provide a more comprehensive check with less false positive and/or negatives. Purpose/Objective(s)Evaluation of what happens during treatment is becoming common practice. The amount and quality of information that can be gathered depends on how complex and labor intense the techniques are along with access to various types of treatment information. We validated, in many clinics, a combined set of complementary techniques to analyze the pros and cons of each technique along with the potential benefits of using the techniques collectively. Evaluation of what happens during treatment is becoming common practice. The amount and quality of information that can be gathered depends on how complex and labor intense the techniques are along with access to various types of treatment information. We validated, in many clinics, a combined set of complementary techniques to analyze the pros and cons of each technique along with the potential benefits of using the techniques collectively. Materials/MethodsFor the IV, two different approaches where taken. The first one (named IV1) used a particular fraction portal picked by the user as the reference and was compared with subsequent daily fraction portals. With the second approach (named IV2), the portal reference fraction was validated by a portal dose calculation based on the planning information. In both approaches, IV1 and IV2, the metric to compare portals was the gamma metric in conjunction with an analysis procedure enacted when the threshold for the gamma metric was violated. We also used the ADR approach. When a CBCT was available, the dose on the daily CBCT was computed. Mapping back and adding the daily doses using the planning CT as the frame of reference generated a cumulative dose. The system allowed evaluation of daily and cumulative doses and DVHs. For the IV, two different approaches where taken. The first one (named IV1) used a particular fraction portal picked by the user as the reference and was compared with subsequent daily fraction portals. With the second approach (named IV2), the portal reference fraction was validated by a portal dose calculation based on the planning information. In both approaches, IV1 and IV2, the metric to compare portals was the gamma metric in conjunction with an analysis procedure enacted when the threshold for the gamma metric was violated. We also used the ADR approach. When a CBCT was available, the dose on the daily CBCT was computed. Mapping back and adding the daily doses using the planning CT as the frame of reference generated a cumulative dose. The system allowed evaluation of daily and cumulative doses and DVHs. ResultsThe IV1 approach has been implemented in 84 clinics for 3 years. A total of more than 6 million portals have been analyzed. The IV1 technique enables improving dosimetric consistency by approximately 6%. The level of intervention needed depends on the anatomical site. For head and neck, lung, and breast, approximately 20% of the weekly portals triggered an action level that required analysis during the course of treatment. For pelvis, the trigger level was 13% and prostate less than 10%. The approach IV2 has been implemented, for several months, in 5 clinics so far. The level of consistency improvements and the behavior per anatomical site exhibited similar behavior to IV1; however IV2 enabled detection of potential issues arising from planning such as the entrance beams going through the moveable couch arms or daily couch position that potentially affected patient dosimetry. The weakness with IV1 and IV2 was that flagged issues may not be clinically relevant. Analyzing each IV failure with ADR as a surrogate to the actual dose delivered to the patient permitted a decision if action was potentially needed for a clinically relevant issue; ADR’s weakness was that it was based on a static CT that may not have represented the conditions measured via portals at the time of treatment. The IV1 approach has been implemented in 84 clinics for 3 years. A total of more than 6 million portals have been analyzed. The IV1 technique enables improving dosimetric consistency by approximately 6%. The level of intervention needed depends on the anatomical site. For head and neck, lung, and breast, approximately 20% of the weekly portals triggered an action level that required analysis during the course of treatment. For pelvis, the trigger level was 13% and prostate less than 10%. The approach IV2 has been implemented, for several months, in 5 clinics so far. The level of consistency improvements and the behavior per anatomical site exhibited similar behavior to IV1; however IV2 enabled detection of potential issues arising from planning such as the entrance beams going through the moveable couch arms or daily couch position that potentially affected patient dosimetry. The weakness with IV1 and IV2 was that flagged issues may not be clinically relevant. Analyzing each IV failure with ADR as a surrogate to the actual dose delivered to the patient permitted a decision if action was potentially needed for a clinically relevant issue; ADR’s weakness was that it was based on a static CT that may not have represented the conditions measured via portals at the time of treatment. ConclusionDifferent implementations of IV and ADR have been evaluated in many clinics. Even though each technique is valuable on its own, when used collectively, the techniques provide a more comprehensive check with less false positive and/or negatives. Different implementations of IV and ADR have been evaluated in many clinics. Even though each technique is valuable on its own, when used collectively, the techniques provide a more comprehensive check with less false positive and/or negatives.

For stereotactic body radiation therapy (SBRT) of lung tumors, random setup, anatomical variations, and the small number of treatment fractions delivered, may together significantly impact the actual dose received by the tumor and surrounding normal structures. We compared planned and actual dosimetry for 101 SBRT lung treatments to determine the effect of setup and anatomical changes on actual dose. This was a retrospective review of prospectively collected data for 101 patients treated with lung SBRT. The number of fractions per patient ranged from 3 to 10. A surrogate for the actual dose was computed using an adaptive dose recalculation (ADR) code. The ADR used the acquired CT prior to each fraction and calculated the fractional dose. A deformable registration code was used to generate daily contours. The deformable registration deformation maps also allowed for cumulative doses (as surrogate of the actual dose) to be calculated and compared to the planned doses. Daily and cumulative doses and DVHs were then evaluated and compared to the planned dosimetry. In all cases, we were able to compare the planned dose to the actual dose delivered using the adaptive dose recalculation code. The mean dose to the PTV was within +/- 5% respective to plan for 93 percent of the patients. The maximum PTV mean dose deviation respective to plan was 7% for one patient. Lung mean dose was within +/- 4% for 91% of the patients with a maximum deviation of 8.1%. Maximum dose to the esophagus was within +/- 5% for 76% of the patients with a maximum deviation of 25%. The dose to 33% of the volume for the heart was within +/- 10% for 90% of the patients with a maximum deviation of 55%. Maximum dose to the spinal cord was within +/- 10% respect to plan for 88% of the patients with a maximum deviation of 46%. Plan and actual dosimetry may differ due to setup and anatomical variations among patients treated with lung SBRT. Even when all observed variations were within acceptable tolerances, the use of planned dose (rather than in actual dose) as the dosimetric surrogate may lead to inaccurate representations of the actual dosimetric outcome during treatment.

With the increasing need of evidence-based medicine, radiation oncology registries (ROR) are becoming a very useful tool to analyze patient outcome and complications outside academic randomized clinical trials. The dosimetric information used in ROR in general consists of information obtained from treatment planning that is a snapshot taken before treatment starts. Therefore, in order to try to understand the dosimetry correlation for a particular patient outcome result, it is desirable to have daily dosimetric information derived during the actual treatment. In our network of radiation therapy clinics distributed over multiple states, over 2,300 treatment fractions are delivered daily. Such a tool to analyze patient dosimetry is already in place in many of the network clinics. The results for more than 50,000 fractions will be presented. An automatic dose reconstruction system was put in place to compute the dose on the daily setup CT (when available). Daily contours sets are generated using deformable registration and doses are accumulated to a common frame of reference by warping daily doses to the planning CT using deformation vector fields. A flagging system is in place to trigger actions from dosimetric deviations compared to the treatment plan dose either for each particular fraction or cumulative doses using defined thresholds. A complementary in-vivo dosimetry system is in place to flag possible issues during treatment. The combination of in-vivo dosimetry and dose reconstruction enables detection of deviations relative to planning expectations, and in many cases permits discrimination if the deviations correspond to setup, machine and/or anatomical changes. Dose reconstruction enables magnitude quantification for deviations. More than 50,000 daily fractions (Fx) were prospectively computed and analyzed compared to plan. PTV mean doses deviated more than 10% for 1.4% of the Fx in abdominal cases, 0.06% for breast, 0.8% for lung and mediastinum, 0.4% for pelvis, 0.6% for prostate, 0% of the head and neck. For sensitive structures: a) in breast cases the flagging rate of D (mean, heart) > 20% and D (mean, lung) > 15% are 1.4 and 22% respectively; b) in H&N the flagging rate of D (mean, parotid) > 15%, D (max, cord) > 10%, D (max, lens) > 10%, D (max, brainstem) > 10% are 14, 2.6, 3.5, and 2.7%, respectively; c) in prostate cases the flagging rate of D (mean, rectum) > 15% and D (mean, bladder) > 15% are 5 and 1.2%, respectively.

Purpose/Objective(s)The conventional tomotherapy patient specific delivery QA (DQA) is performed by delivering the patient plan in a phantom and measuring the dose distribution. The measured dose distribution is compared with the calculated one in the phantom to determine if certain criterion can be satisfied. This practice involves phantom setup and thus is time-consuming and error-prone. Given confidence in couch motion and dose calculation, we can simplify the DQA process by performing an in-air fluence verification with the exit detector array. Therefore phantom setup can be eliminated and automated data analysis can be achieved.Materials/MethodsThe detector calibration consists of signal response calibration and dose rate calibration. A set of delivery procedures are performed to characterize the detector signal response with respect to jaw width and leaf patterns. A dose rate calibration is performed to determine the absolute dose rate with respect to the detector signal. The calibration process needs to be performed when changes occur along the beam line, such as target, MLC, or detector array. The detector calibration enables us to predict radiation fluence at each projection given the delivery plan. Once the detector calibration is done, the DQA workflow can be simplified as follows. Generate a DQA plan without a phantom on a DQA Station. Right click the DQA-TRMT procedure on the Operator Station to generate a static couch procedure. Deliver the procedure in air without couch motion and archive the patient. Then an automated data analysis is performed. The detector data are processed to generate 2D fluence maps at different angles. The fluence maps are then applied to the planning CT to calculate dose. A gamma-index analysis is applied with respect to the planning dose and a QA report is generated. A leaf sinogram is reconstructed and leaf errors are analyzed.ResultsTwelve randomly selected patient plans have been tested for 2D fluence map comparison. For the three fixed jaw settings, misuse of jaw size can be easily detected as a systematic change of detector signals. Averaged over all plans, 90% of leaf open time errors are within +/- 10 ms. Tallied where fluence is greater than 10% of its maximum, the minimum gamma pass rate of 2D fluence maps of 51 angles ranges from 93.9% to 100% using 2%-2mm criterion and from 84.4% to 100% using 2%-1mm. For the worst-case plan, the gamma pass rate is 98.1% averaged over 51 angles using 2%-2mm criterion and 93.7% using 2%-1mm. Without phantom placement, the DQA time is estimated to be reduced by 10% to 75% depending on specific situation.ConclusionsWith confidence in couch motion and dose calculation, tomotherapy DQA can be performed in-air and analyzed automatically. Without the need for phantom placement, positioning error is eliminated and setup/processing time significantly reduced. Purpose/Objective(s)The conventional tomotherapy patient specific delivery QA (DQA) is performed by delivering the patient plan in a phantom and measuring the dose distribution. The measured dose distribution is compared with the calculated one in the phantom to determine if certain criterion can be satisfied. This practice involves phantom setup and thus is time-consuming and error-prone. Given confidence in couch motion and dose calculation, we can simplify the DQA process by performing an in-air fluence verification with the exit detector array. Therefore phantom setup can be eliminated and automated data analysis can be achieved. The conventional tomotherapy patient specific delivery QA (DQA) is performed by delivering the patient plan in a phantom and measuring the dose distribution. The measured dose distribution is compared with the calculated one in the phantom to determine if certain criterion can be satisfied. This practice involves phantom setup and thus is time-consuming and error-prone. Given confidence in couch motion and dose calculation, we can simplify the DQA process by performing an in-air fluence verification with the exit detector array. Therefore phantom setup can be eliminated and automated data analysis can be achieved. Materials/MethodsThe detector calibration consists of signal response calibration and dose rate calibration. A set of delivery procedures are performed to characterize the detector signal response with respect to jaw width and leaf patterns. A dose rate calibration is performed to determine the absolute dose rate with respect to the detector signal. The calibration process needs to be performed when changes occur along the beam line, such as target, MLC, or detector array. The detector calibration enables us to predict radiation fluence at each projection given the delivery plan. Once the detector calibration is done, the DQA workflow can be simplified as follows. Generate a DQA plan without a phantom on a DQA Station. Right click the DQA-TRMT procedure on the Operator Station to generate a static couch procedure. Deliver the procedure in air without couch motion and archive the patient. Then an automated data analysis is performed. The detector data are processed to generate 2D fluence maps at different angles. The fluence maps are then applied to the planning CT to calculate dose. A gamma-index analysis is applied with respect to the planning dose and a QA report is generated. A leaf sinogram is reconstructed and leaf errors are analyzed. The detector calibration consists of signal response calibration and dose rate calibration. A set of delivery procedures are performed to characterize the detector signal response with respect to jaw width and leaf patterns. A dose rate calibration is performed to determine the absolute dose rate with respect to the detector signal. The calibration process needs to be performed when changes occur along the beam line, such as target, MLC, or detector array. The detector calibration enables us to predict radiation fluence at each projection given the delivery plan. Once the detector calibration is done, the DQA workflow can be simplified as follows. Generate a DQA plan without a phantom on a DQA Station. Right click the DQA-TRMT procedure on the Operator Station to generate a static couch procedure. Deliver the procedure in air without couch motion and archive the patient. Then an automated data analysis is performed. The detector data are processed to generate 2D fluence maps at different angles. The fluence maps are then applied to the planning CT to calculate dose. A gamma-index analysis is applied with respect to the planning dose and a QA report is generated. A leaf sinogram is reconstructed and leaf errors are analyzed. ResultsTwelve randomly selected patient plans have been tested for 2D fluence map comparison. For the three fixed jaw settings, misuse of jaw size can be easily detected as a systematic change of detector signals. Averaged over all plans, 90% of leaf open time errors are within +/- 10 ms. Tallied where fluence is greater than 10% of its maximum, the minimum gamma pass rate of 2D fluence maps of 51 angles ranges from 93.9% to 100% using 2%-2mm criterion and from 84.4% to 100% using 2%-1mm. For the worst-case plan, the gamma pass rate is 98.1% averaged over 51 angles using 2%-2mm criterion and 93.7% using 2%-1mm. Without phantom placement, the DQA time is estimated to be reduced by 10% to 75% depending on specific situation. Twelve randomly selected patient plans have been tested for 2D fluence map comparison. For the three fixed jaw settings, misuse of jaw size can be easily detected as a systematic change of detector signals. Averaged over all plans, 90% of leaf open time errors are within +/- 10 ms. Tallied where fluence is greater than 10% of its maximum, the minimum gamma pass rate of 2D fluence maps of 51 angles ranges from 93.9% to 100% using 2%-2mm criterion and from 84.4% to 100% using 2%-1mm. For the worst-case plan, the gamma pass rate is 98.1% averaged over 51 angles using 2%-2mm criterion and 93.7% using 2%-1mm. Without phantom placement, the DQA time is estimated to be reduced by 10% to 75% depending on specific situation. ConclusionsWith confidence in couch motion and dose calculation, tomotherapy DQA can be performed in-air and analyzed automatically. Without the need for phantom placement, positioning error is eliminated and setup/processing time significantly reduced. With confidence in couch motion and dose calculation, tomotherapy DQA can be performed in-air and analyzed automatically. Without the need for phantom placement, positioning error is eliminated and setup/processing time significantly reduced.

Purpose: The Gamma Index defines an asymmetric metric between the evaluated image and the reference image. It provides a quantitative comparison that can be used to indicate sample-wised pass/fail on the agreement of the two images. The Gamma passing/failing rate has become an important clinical evaluation tool. However, the presence of noise in the evaluated and/or reference images may change the Gamma Index, hence the passing/failing rate, and further, clinical decisions. In this work, we systematically studied the impact of the image noise on the Gamma Index calculation. Methods: We used both analytic formulation and numerical calculations in our study. The numerical calculations included simulations and clinical images. Three different noise scenarios were studied in simulations: noise in reference images only, in evaluated images only, and in both. Both white and spatially correlated noises of various magnitudes were simulated. For clinical images of various noise levels, the Gamma Index of measurement against calculation, calculation against measurement, and measurement against measurement, were evaluated. Results: Numerical calculations for both the simulation and clinical data agreed with the analytic formulations, and the clinical data agreed with the simulations. For the Gamma Index of measurement against calculation, its distribution has an increased mean and an increased standard deviation as the noise increases. On the contrary, for the Gamma index of calculation against measurement, its distribution has a decreased mean and stabilized standard deviation as the noise increases. White noise has greater impact on the Gamma Index than spatially correlated noise. Conclusions: The noise has significant impact on the Gamma Index calculation and the impact is asymmetric. The Gamma Index should be reported along with the noise levels in both reference and evaluated images. Reporting of the Gamma Index with switched roles of the images as reference and evaluated images or some composite metrics would be a good practice.

Computerized Record and Verify System (RVS) has been used to detect and prevent mistakes in the delivery of external beam radiation therapy for more than 20 years. Conventional dose tracking mechanism in RVS is monitor unit (MU) based, assuming patients are treated exactly as planned. Without incorporating parameters such as machine behavior, patient setup, and patient anatomy, the cumulative doses do not accurately represent the actual doses delivered to patients, which is the goal of a RVS. We developed an automatic system to compute and accumulate daily doses through re-computation using daily imaging, and verify dose volume information in the context of RVS. Our system consists of several components, including a DICOM data retriever, dosimetric computation engine, and review platform. The workflow begins after each treatment; the DICOM daemon automatically retrieves daily volumetric images from patient records. The delivered dose is computed using a convolution-superposition based dose calculator implemented in-house on graphic cards. The deformation field between the daily and planning CT is computed using Morphons algorithm to warp the planning structures and delivered doses. The dose volume histograms of the daily and cumulative doses are generated for dose tracking and reporting. These data are queryable and a triggering system using a configurable tolerance table can be set up to prompt a clinical review. Possible root causes are analyzed such as machine output drifting, patient set up error. A user-friendly 4D image viewer is integrated for efficient review of images and doses. Various aspects of the system were validated. Our dose calculator passed 97% gamma test evaluated in 3D with 2% dose difference and 2mm distance-to-agreement compared with tomotherapy calculated dose. With proper calibration of the imaging systems on tomotherapy machines, re-computed daily doses agreed with measurements in phantom within 2% for all jaw sizes. Visual inspection shows acceptable and consistent deformable registration results, except for cases with large or unrealistic deformations. This system is used clinically and more than 35,000 fractions were computed. More than 50 incidences, which would otherwise not be caught with conventional MU-based dose tracking system, were detected. The automatic triggering system is able to detect significant patient setup errors and anatomical changes. We developed a system to automatically accumulate and verify the actual distribution of delivered dose over the course of radiation therapy. This system helps detect and prevent further deviations in treatments, quantifies the dosimetric impact of the deviations, provides necessary information for adaptive radiation therapy, and hence is invaluable in improving the quality of patient care.

Purpose:To efficiently calculate the head scatter fluence for an arbitrary intensity‐modulated field with any source distribution using the source occlusion model.Method:The source occlusion model with focal and extra focal radiation (Jaffray et al, 1993) can be used to account for LINAC head scatter. In the model, the fluence map of any field shape at any point can be calculated via integration of the source distribution within the visible range, as confined by each segment, using the detector eye's view. A 2D integration would be required for each segment and each fluence plane point, which is time‐consuming, as an intensity‐modulated field contains typically tens to hundreds of segments. In this work, we prove that the superposition of the segmental integrations is equivalent to a simple convolution regardless of what the source distribution is. In fact, for each point, the detector eye's view of the field shape can be represented as a function with the origin defined at the point's pinhole reflection through the center of the collimator plane. We were thus able to reduce hundreds of source plane integration to one convolution. We calculated the fluence map for various 3D and IMRT beams and various extra‐focal source distributions using both the segmental integration approach and the convolution approach and compared the computation time and fluence map results of both approaches.Results:The fluence maps calculated using the convolution approach were the same as those calculated using the segmental approach, except for rounding errors (<0.1%). While it took considerably longer time to calculate all segmental integrations, the fluence map calculation using the convolution approach took only ∼1/3 of the time for typical IMRT fields with ∼100 segments.Conclusions:The convolution approach for head scatter fluence calculation is fast and accurate and can be used to enhance the online process.

The current radiation therapy community puts significant efforts on optimization and QA for each treatment plan but lacks tools and resources on QA and evaluation for each treatment. We developed an efficient workflow and software suite that enables in vivo verification and adaptive evaluation for every treatment for every patient. Two software suites, “GAMMA” and “ADAPTIVE” were developed in house. The “GAMMA” program is for in vivo verification, whose main function is to collect and analyze in vivo delivery information, including information from patient setup, RTRecord and RTImage. It compares predicted detector signals that are calculated based on RTPlan and plan CT with in vivo detector signals. A table of Gamma passing rates with respect to different criteria is generated to evaluate how well the delivery matches the plan. The “ADAPTIVE” software further evaluates each treatment in a comprehensive fashion. It recalculates delivered dose based on pre-treatment volume images and patient setup information. Daily doses are accumulated in a common reference frame via deformable image registration. Fractional DVHs, cumulative DVHs and various tracking matrices are subsequently calculated. Both programs generate a web report, which summarizes the results of the plan, patient setup and registration, treatment delivery status, portal dose comparison, volume dose comparison, as well as the trend of multiple metrics. All results are stored in a central database for future data mining. Both the “GAMMA” and “ADAPTIVE” programs have been gradually integrated into clinical workflow in multiple clinics, including 14 tomotherapy machines and multiple commercial linacs As soon each daily treatment finishes, all clinical data are automatically retrieved from various sources, and the calculation begins. Based on the collected data, thresholds of various tracking matrices were constructed and a flagging system was developed so that corresponding clinicians can be notified to initiate a case review or alerted when deviations occur based on the defined thresholds. For the tomotherapy treatment alone, more than 130K fractions have been verified since the launch of GAMMA program in 2011. An efficient workflow for automated in vivo verification and adaptive evaluation was developed and implemented. The clinicians were able to see the results of every treatment in few minutes after the delivery. This opens up great opportunities for early error detection, accident prevention, and treatment adaptation. As a by-product, a centralized database, essential for radiation therapy data mining, was generated, which facilitates flagging system development, clinical workflow optimization, and comprehensive outcome studies.

Purpose: An accurate leaf fluence model can be used in applications such as patient specific delivery QA and in‐vivo dosimetry for TomoTherapy systems. It is known that the total fluence is not a linear combination of individual leaf fluence due to leakage‐transmission, tongue‐and‐groove, and source occlusion effect. Here we propose a method to model the nonlinear effects as linear terms thus making the MLC‐detector system a linear system. Methods: A leaf pattern basis (LPB) consisting of no‐leaf‐open, single‐leaf‐open, double‐leaf‐open and triple‐leaf‐open patterns are chosen to represent linear and major nonlinear effects of leaf fluence as a linear system. An arbitrary leaf pattern can be expressed as (or decomposed to) a linear combination of the LPB either pulse by pulse or weighted by dwelling time. The exit detector responses to the LPB are obtained by processing returned detector signals resulting from the predefined leaf patterns for each jaw setting. Through forward transformation, detector signal can be predicted given a delivery plan. An equivalent leaf open time (LOT) sinogram containing output variation information can also be inversely calculated from the measured detector signals. Twelve patient plans were delivered in air. The equivalent LOT sinograms were compared with their planned sinograms. Results: The whole calibration process was done in 20 minutes. For two randomly generated leaf patterns, 98.5% of the active channels showed differences within 0.5% of the local maximum between the predicted and measured signals. Averaged over the twelve plans, 90% of LOT errors were within +/−10 ms. The LOT systematic error increases and shows an oscillating pattern when LOT is shorter than 50 ms. Conclusion: The LPB method models the MLC‐detector response accurately, which improves patient specific delivery QA and in‐vivo dosimetry for TomoTherapy systems. It is sensitive enough to detect systematic LOT errors as small as 10 ms.

Purpose:With the prescription method moving from point A to 3D volume based in cervical cancer HDR brachytherapy, the traditional pear‐shaped isodose lines are desired to be sculptured to conform to the irregular shaped target. The standard single channel tandem cannot generate asymmetric isodose lines. Most of the directionally shielded sources proposed in literature are challenging to manufacture and operate. In this study, we proposed a novel internally shielded tandem applicator design which gave users more freedom to manipulate isodose lines while planning.Methods:The proposed tandem design has one centrally located lead cylindrical rod of 8 mm in diameter serving as the internal shield. Multiple source channels with the diameter of 2 mm are evenly spaced and engraved on the central cylindrical rod. The overall diameter of the tandem with polymer encapsulation was kept to be 10 mm. Various number of channels and engraving depths have been tested in the design process. Geant4 Monte Carlo toolkit was used for dose calculation assuming a Varian VS2000 source was placed inside the applicator. A Monte Carlo based planning system has been developed in‐house to generate brachytherapy plans. Test plans by using this internally shielded tandem were generated for 3 clinical cases.Results:Water phantom results shown the dose distribution from a VS2000 source in the tandem was strongly distorted towards one direction due to the presence of shielding material. Conformal plans with asymmetric isodose distributions around the tandem can be generated by optimizing dwell times in different channels.Conclusion:An effective and easy‐to‐use internally shielded tandem was developed. It gave user the freedom to sculpt isodose lines to generate conformal plans for cervical cancer brachytherapy.

Use the information generated during the in-vivo verification process to define IGRT strategies and flag possible needs for adaptive therapy. Report information during treatment to infer the impact of machine behavior, patient setup and anatomical changes. Create a system and metrics to flag possible issues and trending. Create procedures to assist in identifying the clinical impact and troubleshoot possible problems. Generate suggestions for daily IGRT based on the in-vivo verification findings and possible adaptive re-planning.

PURPOSEPortal measurement is becoming an important tool for in vivo dosimetric verification, and Calypso provides real-time tracking capability; however, when both work simultaneously, large interference arises for portal measurement. The purpose of this study is to investigate the interference of Calypso on portal measurement and mitigation of the interference by applying aluminum shielding over portal panels.METHODSFor the same IMRT field and phantom setup, we acquired portal measurements at every 15 degree gantry angle, without and with Calypso, and for those measurements with Calypso, we also acquired portal measurements without and with aluminum shielding over the portal panels. The aluminum shielding consists of a layer of aluminum foil of 0.1 mm thickness covering the portal panel. The measurements without Calypso and without aluminum shielding were regarded as the reference images. All other measurements were regarded as the test images. We measured the deviation of the test images from the reference images by the amplitude difference and using the Gamma Index (3%, 3 mm).RESULTSWith Calypso interference and without aluminum shielding, the signals are larger than the reference, and in some unfavorable gantry angles, the signals can be as much as 15% larger. With aluminum shielding, the interference was much reduced to ∼3% for those unfavorable angles, and the Gamma passing rate achieves 95% for most of the angles.CONCLUSIONThe Calypso interference on portal measurements is gantry angle dependent due to panel orientation and proximity with respect to the Calypso transducer. Shielding on the portal panel can largely reduce electronic interference, and it is anticipated that with an improved complete shielding over the entire portal panel, the interference could be further reduced.

Purpose: To validate a simple portal dose calculator for plan QA and in‐vivo dosimetry. Methods: We model portal dose as a function of the fluence map, patient attenuation, patient scatter and portal response. Fluence maps are reconstructed using control‐point sequence in RTPlan. Patient attenuation is calculated via ray‐tracing through the patient CT. The effect of patient scatter and portal response is modeled by convolution, where the convolution kernel is derived from the commissioning measurements of different beam energies, different field sizes, different phantom thickness, and different source to image distances (SIDs). For various IMRT/3D plan, phantom and patient geometry, both in‐air and in‐transit portals were calculated. The calculations were compared with portal measurements. The Gamma Index of measurements against predicted portals with various dose difference (DD) criteria (1%, 2%, 3%, 4%, 5%, etc) and distance to agreement (DTA) criteria (1 mm, 2 mm, 3 mm, 4 mm, 5 mm, etc) were calculated. The Gamma pass rates of various DD and DTA criteria were evaluated and formed a Gamma table. Results: For various IMRT beams, the head, body and lung phantoms, the in‐air and in‐transit portal calculations matched well with portal measurements. The Gamma pass rates for in‐air portal are above 97% for 2 mm, 2% criteria and above 99% for 3 mm, 3% criteria. The Gamma pass rates for in‐transit portal were above 90% for 2 mm, 2% criteria and above 95% for 3 mm, 3% criteria. Conclusion: The simple portal dose calculation model is validated via phantom measurements. The model could be used in clinic for in‐air and intransit portal prediction.

Online volume dose reconstruction that accounts for both machine and patient errors is desired but not yet clinically available. The existing methods/workflows can be time/resource consuming and/or lack of clinical accuracy. We present a simple workflow that is suitable for fast and accurate volume dose reconstruction and in-vivo verification. We model the volume dose as a function of the machine instructions and patient anatomy. Machine discrepancies, such as output variation, jaw/leaf position errors, etc., and patient discrepancies, such as setup error, weight loss, etc., between plan and delivery are modeled as perturbation of the fluence map and patient anatomy, respectively. In vivo dose reconstruction is modeled as perturbation of the planned dose due to change of the fluence map and patient anatomy image. We develop an in-house software suite that performs in-vivo delivery verification and dose reconstruction and connects to the clinic network, which includes the TPS, the record and verification (R&V) system, and the DICOM image server. A watchdog is used to monitor the R&V system. As soon as a treatment finishes, all clinical data, such as the machine commissioning data, RTPlan, RTStruct, plan CT, plan dose, verification images (e.g. cone beam CT, or OBI), and portal image are automatically retrieved from various sources and the calculation is kicked off right away. A volume dose is reconstructed and compared with the planned dose. A web report is generated, which summarizes the results of the plan, patient registration and setup, treatment delivery status, portal dose comparison, volume dose comparison, as well as the trend of multiple metrics. The whole workflow is implemented in two clinics for alpha testing. The algorithm of in vivo volume dose reconstruction is validated via simulated and clinical data. The reconstruction was compared with the ground truth via isodose lines and the Gamma Index. The reconstruction generally matches well with the ground truth, with the Gamma pass rate of 95% for 3 mm, 3% criteria. For various plans and geometries, the volume doses are reconstructed in just few seconds. The clinicians (physicians, physicists, dosimetrists, etc.) can access the report minutes after the treatment. A novel dose reconstruction algorithm utilizing reference dose perturbation and an efficient workflow for in-vivo treatment verification are developed and implemented. The clinicians are able to see the results of treatment verification minutes after the treatment, opening up great opportunities for early error detection, accident prevention, and treatment adaptation.

In our network of radiation therapy clinics distributed over multiple states, over 2,300 treatment fractions are delivered daily. To serve the Quality Assurance needs of this widespread, high volume network, we concluded that automatic tools to assess treatment delivery quality are paramount. Therefore, an in-house system operating in the background of normal clinic workflows was developed to flag and evaluate treatment quality during delivery using in vivo verification (IV), in vivo dosimetry (ID) and adaptive dose recalculation (ADR). The system's flagging function prompts different actions from medical physics, physicians, dosimetrists and therapists. Herein, we summarize the result of this system for approximately 100,000 daily fractions delivered using linear accelerators (LA) and tomotherapy (TT). In vivo verification at the treatment time is performed via accessing sensors/detectors from LA and TT to compare planned values to the actual reading during treatment delivery. The exit detector is used to perform ID and dose reconstruction. Adaptive dose recalculation is now being performed for some of the anatomical sites by computing the dose on the daily setup CT (whenever available). Daily contours sets are generated using deformable registration and doses are accumulated by warping daily doses to the planning CT using deformation vectors fields. The flagging system is design to discriminate between possible machine, setup or anatomical changes issues. In vivo verification flags include, output and hardware/software location consistency, registration similarity metrics, gamma on the portal dosimetry. ADR has automatic flags on dose values, dose volume histogram (DVH) points, structure set and deformation metrics. Approximately 100,000 daily fractions were analyzed. The numbers of in vivo flags was independent of the patient load in each clinic. The number of flags reduced considerably as a function of the length of time the system was used by tailoring IGRT procedures to specific anatomical sites and specific patients based on the information provided by the system. The number of in vivo verification flags depended upon the length of the daily setup CT. For certain anatomical sites, long CTs may not reduce the number of in vivo verification flags; however, in vivo dosimetry still indicated possible problem locations, thus is used to tailor the daily IGRT. The sites with a greater number of flags were head and neck, breast and lung. Thus, it is critical that IV, ID, and ADR work in synergy. These data suggest IV and ID are important to trigger flags of possible issues and ADR further assesses clinical impact of those flags. Further work will serve to optimize these techniques.

PURPOSE:To evaluate the dosimetric difference between helical tomotherapy (HT) and intensity modulated proton therapy (IMPT) treatment for lung cancer patients.METHODS:Five patients treated by HT at University of Wisconsin Carbone Cancer Center were selected. HT plans were generated on TomoTherapy treatment planning station (TomoTherapy Inc., USA). The field widths were set to 2.5 cm for all patients in this study. The IMPT plans were generated using the same planning CT and contours with our in-house treatment planning system. Three to five field spot scanning IMPT were used to deliver uniform doses to the targets while minimizing the irradiated lung volume. The proton spots used has a Gaussian sigma of 6mm and are placed on a rectangular grid. The dose distribution of each proton spot is calculated using a pencil beam algorithm with tissue heterogeneity corrections. All the dosimetric analyses are performed using normalized total dose. Alpha/beta ratios were set to 3 for normal tissues and 10 for tumors.RESULTS:IMPT plans showed improvement of critical structure avoidance and target dose uniformity for all patients. Reductions in mean lung doses of between 81% to 27% were observed in the IMPT plans relative to the HT. The equivalent uniform dose of the target improved from 49.2 Gy in HT plan to 60.04 Gy in IMPT for patient #2, and equivalent for other cases. The maximum doses to cord were reduced by 20.5 Gy on average using IMPT. In two patient cases, the normal tissue complication probabilities were reduced by 53% and 14% with IMPT.CONCLUSION:IMPT provides improved dose homogeneity on the target and normal structure sparing compared with HT in the treatment of non-small cell carcinoma in lung. Significant reduction of mean lung dose was demonstrated, as well as toxicity to organs at risk adjacent to the target.

Purpose: Fiducial markers are commonly used in prostate radiotherapy to improve accuracy of target localization. However, in proton therapy, since the proton range is sensitive to the material it travels through, under‐dose to the prostate has been reported in conventional passive scattering treatment and 3D‐modulation based intensity modulated proton therapy (IMPT), in the presence of high density fiducial markers inside the prostate. This study investigated the ability of using multiple beam angles to minimize the dosimetric impact of fiducial markers in 3D‐modulation and distal edge tracking (DET) based IMPT. Methods: CT images of a typical prostate patient with three gold markers (3mm by 1mm, density = 19.3 g/cc) were used in this study. 2‐field lateral and 4‐field box type 3D‐modulation proton treatment plans as well as DET plans with evenly spaced 18, 10, and 6 beam angles were generated to deliver 70Gy to 95% of the PTV. Geant4 Monte Carlo code was used for dose calculation. The plan was performed on images with fiducial markers artificially removed. Then the delivered dose distribution was calculated with fiducial markers present in the patient. The dose distributions between the planed and delivered were then compared to evaluate the efficacy of using multiple beam angles to minimize the dosimetric deviations. The tumor control probability (TCP) was also used to quantify the dose variation. Results: The preliminary result shows all the 3D‐modulation and DET plans yielded equivalent PTV coverage. The planned and delivered D98% of prostate was 68.68Gy vs. 68.32Gy in 18‐field DET, 67.83Gy vs. 65.53Gy in 10‐field DET, and 67.82Gy vs. 58.19Gy in 2‐field 3D‐Modulation. No significant dose change to rectum and bladder was observed. Conclusions: With high density fiducial markers present in the target, multiple beam angles are recommended in proton treatment plans to avoid significant under‐dose to the target.