H. Omar Wooten

Quality of Intensity Modulated Radiation Therapy Treatment Plans Using a 60Co Magnetic Resonance Image Guidance Radiation Therapy System

PURPOSE This work describes a commercial treatment planning system, its technical features, and its capabilities for creating (60)Co intensity modulated radiation therapy (IMRT) treatment plans for a magnetic resonance image guidance radiation therapy (MR-IGRT) system. METHODS AND MATERIALS The ViewRay treatment planning system (Oakwood Village, OH) was used to create (60)Co IMRT treatment plans for 33 cancer patients with disease in the abdominal, pelvic, thorax, and head and neck regions using physician-specified patient-specific target coverage and organ at risk (OAR) objectives. Backup plans using a third-party linear accelerator (linac)-based planning system were also created. Plans were evaluated by attending physicians and approved for treatment. The (60)Co and linac plans were compared by evaluating conformity numbers (CN) with 100% and 95% of prescription reference doses and heterogeneity indices (HI) for planning target volumes (PTVs) and maximum, mean, and dose-volume histogram (DVH) values for OARs. RESULTS All (60)Co IMRT plans achieved PTV coverage and OAR sparing that were similar to linac plans. PTV conformity for (60)Co was within <1% and 3% of linac plans for 100% and 95% prescription reference isodoses, respectively, and heterogeneity was on average 4% greater. Comparisons of OAR mean dose showed generally better sparing with linac plans in the low-dose range <20 Gy, but comparable sparing for organs with mean doses >20 Gy. The mean doses for all (60)Co plan OARs were within clinical tolerances. CONCLUSIONS A commercial (60)Co MR-IGRT device can produce highly conformal IMRT treatment plans similar in quality to linac IMRT for a variety of disease sites. Additional work is in progress to evaluate the clinical benefit of other novel features of this MR-IGRT system.

Benchmark IMRT evaluation of a Co-60 MRI-guided radiation therapy system

A device for MRI-guided radiation therapy (MR-IGRT) that uses cobalt-60 sources to deliver intensity modulated radiation therapy is now commercially available. We investigated the performance of the treatment planning and delivery system against the benchmark recommended by the American Association of Physicists in Medicine (AAPM) Task Group 119 for IMRT commissioning and demonstrated that the device plans and delivers IMRT treatments within recommended confidence limits and with similar accuracy as linac IMRT.

Patient-Specific Quality Assurance for the Delivery of 60Co Intensity Modulated Radiation Therapy Subject to a 0.35-T Lateral Magnetic Field

PURPOSE This work describes a patient-specific dosimetry quality assurance (QA) program for intensity modulated radiation therapy (IMRT) using ViewRay, the first commercial magnetic resonance imaging-guided RT device. METHODS AND MATERIALS The program consisted of: (1) a 1-dimensional multipoint ionization chamber measurement using a customized 15-cm(3) cube-shaped phantom; (2) 2-dimensional (2D) radiographic film measurement using a 30- × 30- × 20-cm(3) phantom with multiple inserted ionization chambers; (3) quasi-3D diode array (ArcCHECK) measurement with a centrally inserted ionization chamber; (4) 2D fluence verification using machine delivery log files; and (5) 3D Monte Carlo (MC) dose reconstruction with machine delivery files and phantom CT. RESULTS Ionization chamber measurements agreed well with treatment planning system (TPS)-computed doses in all phantom geometries where the mean ± SD difference was 0.0% ± 1.3% (n=102; range, -3.0%-2.9%). Film measurements also showed excellent agreement with the TPS-computed 2D dose distributions where the mean passing rate using 3% relative/3 mm gamma criteria was 94.6% ± 3.4% (n=30; range, 87.4%-100%). For ArcCHECK measurements, the mean ± SD passing rate using 3% relative/3 mm gamma criteria was 98.9% ± 1.1% (n=34; range, 95.8%-100%). 2D fluence maps with a resolution of 1 × 1 mm(2) showed 100% passing rates for all plan deliveries (n=34). The MC reconstructed doses to the phantom agreed well with planned 3D doses where the mean passing rate using 3% absolute/3 mm gamma criteria was 99.0% ± 1.0% (n=18; range, 97.0%-100%), demonstrating the feasibility of evaluating the QA results in the patient geometry. CONCLUSIONS We developed a dosimetry program for ViewRays patient-specific IMRT QA. The methodology will be useful for other ViewRay users. The QA results presented here can assist the RT community to establish appropriate tolerance and action limits for ViewRays IMRT QA.

Characterization of the onboard imaging unit for the first clinical magnetic resonance image guided radiation therapy system.

PURPOSE To characterize the performance of the onboard imaging unit for the first clinical magnetic resonance image guided radiation therapy (MR-IGRT) system. METHODS The imaging performance characterization included four components: ACR (the American College of Radiology) phantom test, spatial integrity, coil signal to noise ratio (SNR) and uniformity, and magnetic field homogeneity. The ACR phantom test was performed in accordance with the ACR phantom test guidance. The spatial integrity test was evaluated using a 40.8 × 40.8 × 40.8 cm(3) spatial integrity phantom. MR and computed tomography (CT) images of the phantom were acquired and coregistered. Objects were identified around the surfaces of 20 and 35 cm diameters of spherical volume (DSVs) on both the MR and CT images. Geometric distortion was quantified using deviation in object location between the MR and CT images. The coil SNR test was performed according to the national electrical manufacturers association (NEMA) standards MS-1 and MS-9. The magnetic field homogeneity test was measured using field camera and spectral peak methods. RESULTS For the ACR tests, the slice position error was less than 0.10 cm, the slice thickness error was less than 0.05 cm, the resolved high-contrast spatial resolution was 0.09 cm, the resolved low-contrast spokes were more than 25, the image intensity uniformity was above 93%, and the percentage ghosting was less than 0.22%. All were within the ACR recommended specifications. The maximum geometric distortions within the 20 and 35 cm DSVs were 0.10 and 0.18 cm for high spatial resolution three-dimensional images and 0.08 and 0.20 cm for high temporal resolution two dimensional cine images based on the distance-to-phantom-center method. The average SNR was 12.0 for the body coil, 42.9 for the combined torso coil, and 44.0 for the combined head and neck coil. Magnetic field homogeneities at gantry angles of 0°, 30°, 60°, 90°, and 120° were 23.55, 20.43, 18.76, 19.11, and 22.22 ppm, respectively, using the field camera method over the 45 cm DSV. CONCLUSIONS The onboard imaging unit of the first commercial MR-IGRT system meets ACR, NEMA, and vendor specifications.

Automated radiation therapy treatment plan workflow using a commercial application programming interface

PURPOSE The objective of this study was to create a workflow for the automation and standardization of treatment plan generation and evaluation using an application programming interface (API) to access data from a commercial treatment planning system (Varian Medical Systems, Inc, Palo Alto, CA). METHODS AND MATERIALS The automation workflow begins with converting electronic patient-specific physician treatment planning orders that specify demographics, simulation instructions, and dosimetric objectives for targets and organs at risk into XML files. These XML files are used to generate standard contour names, beam, and patient-specific intensity modulated radiation therapy (IMRT) optimization templates to be executed in a commercial treatment planning system (TPS) by the user. A set of computer programs have been developed to provide quality control (QC) reports that verify demographic information in the TPS against the treatment planning orders, ensure the existence and proper naming of organs at risk, and generate patient-specific plan evaluation reports that provide real-time feedback on the concordance of an active treatment plan to the physician-specified treatment planning goals. RESULTS A workflow for lung IMRT was chosen as a test scenario. Contour, beam, and patient-specific IMRT optimization templates were automatically generated from the physician treatment planning orders and loaded into the planning system. The QC reports were developed for lung IMRT, including the option of patient-specific modifications to the standard templates. The API QC reporting includes a dynamic program that runs in parallel to the TPS during the planning process, providing real-time feedback as to whether physician-specified treatment plan parameters have improved or worsened from previous iterations. CONCLUSIONS User-created computer programs to access information in the TPS database by means of a commercial TPS API enable automation and standardization of treatment plan generation and evaluation.

A GPU-accelerated Monte Carlo dose calculation platform and its application toward validating an MRI-guided radiation therapy beam model.

PURPOSE The clinical commissioning of IMRT subject to a magnetic field is challenging. The purpose of this work is to develop a GPU-accelerated Monte Carlo dose calculation platform based on penelope and then use the platform to validate a vendor-provided MRIdian head model toward quality assurance of clinical IMRT treatment plans subject to a 0.35 T magnetic field. METHODS penelope was first translated from fortran to c++ and the result was confirmed to produce equivalent results to the original code. The c++ code was then adapted to cuda in a workflow optimized for GPU architecture. The original code was expanded to include voxelized transport with Woodcock tracking, faster electron/positron propagation in a magnetic field, and several features that make gpenelope highly user-friendly. Moreover, the vendor-provided MRIdian head model was incorporated into the code in an effort to apply gpenelope as both an accurate and rapid dose validation system. A set of experimental measurements were performed on the MRIdian system to examine the accuracy of both the head model and gpenelope. Ultimately, gpenelope was applied toward independent validation of patient doses calculated by MRIdians kmc. RESULTS An acceleration factor of 152 was achieved in comparison to the original single-thread fortran implementation with the original accuracy being preserved. For 16 treatment plans including stomach (4), lung (2), liver (3), adrenal gland (2), pancreas (2), spleen(1), mediastinum (1), and breast (1), the MRIdian dose calculation engine agrees with gpenelope with a mean gamma passing rate of 99.1% ± 0.6% (2%/2 mm). CONCLUSIONS A Monte Carlo simulation platform was developed based on a GPU- accelerated version of penelope. This platform was used to validate that both the vendor-provided head model and fast Monte Carlo engine used by the MRIdian system are accurate in modeling radiation transport in a patient using 2%/2 mm gamma criteria. Future applications of this platform will include dose validation and accumulation, IMRT optimization, and dosimetry system modeling for next generation MR-IGRT systems.

A software tool to automatically assure and report daily treatment deliveries by a cobalt-60 radiation therapy device.

The aims of this study were to develop a method for automatic and immediate verification of treatment delivery after each treatment fraction in order to detect and correct errors, and to develop a comprehensive daily report which includes delivery verification results, daily image‐guided radiation therapy (IGRT) review, and information for weekly physics reviews. After systematically analyzing the requirements for treatment delivery verification and understanding the available information from a commercial MRI‐guided radiotherapy treatment machine, we designed a procedure to use 1) treatment plan files, 2) delivery log files, and 3) beam output information to verify the accuracy and completeness of each daily treatment delivery. The procedure verifies the correctness of delivered treatment plan parameters including beams, beam segments and, for each segment, the beam‐on time and MLC leaf positions. For each beam, composite primary fluence maps are calculated from the MLC leaf positions and segment beam‐on time. Error statistics are calculated on the fluence difference maps between the plan and the delivery. A daily treatment delivery report is designed to include all required information for IGRT and weekly physics reviews including the plan and treatment fraction information, daily beam output information, and the treatment delivery verification results. A computer program was developed to implement the proposed procedure of the automatic delivery verification and daily report generation for an MRI guided radiation therapy system. The program was clinically commissioned. Sensitivity was measured with simulated errors. The final version has been integrated into the commercial version of the treatment delivery system. The method automatically verifies the EBRT treatment deliveries and generates the daily treatment reports. Already in clinical use for over one year, it is useful to facilitate delivery error detection, and to expedite physician daily IGRT review and physicist weekly chart review. PACS number(s): 87.55.kmThe aims of this study were to develop a method for automatic and immediate verification of treatment delivery after each treatment fraction in order to detect and correct errors, and to develop a comprehensive daily report which includes delivery verification results, daily image-guided radiation therapy (IGRT) review, and information for weekly physics reviews. After systematically analyzing the requirements for treatment delivery verification and understanding the available information from a commercial MRI-guided radiotherapy treatment machine, we designed a procedure to use 1) treatment plan files, 2) delivery log files, and 3) beam output information to verify the accuracy and completeness of each daily treatment delivery. The procedure verifies the correctness of delivered treatment plan parameters including beams, beam segments and, for each segment, the beam-on time and MLC leaf positions. For each beam, composite primary fluence maps are calculated from the MLC leaf positions and segment beam-on time. Error statistics are calculated on the fluence difference maps between the plan and the delivery. A daily treatment delivery report is designed to include all required information for IGRT and weekly physics reviews including the plan and treatment fraction information, daily beam output information, and the treatment delivery verification results. A computer program was developed to implement the proposed procedure of the automatic delivery verification and daily report generation for an MRI guided radiation therapy system. The program was clinically commissioned. Sensitivity was measured with simulated errors. The final version has been integrated into the commercial version of the treatment delivery system. The method automatically verifies the EBRT treatment deliveries and generates the daily treatment reports. Already in clinical use for over one year, it is useful to facilitate delivery error detection, and to expedite physician daily IGRT review and physicist weekly chart review. PACS number(s): 87.55.km.

Magnetic resonance imaging-based treatment planning for prostate cancer: Use of population average tissue densities within the irradiated volume to improve plan accuracy

PURPOSE The purpose of this study was to investigate the feasibility of using population average tissue densities within the irradiated volume to improve the dosimetric accuracy of magnetic resonance imaging-based treatment plans for prostate cancer. METHODS AND MATERIALS Computed tomography images and radiation therapy treatment plans from 20 patients with prostate cancer were reviewed retrospectively. Patient anatomy was segmented into fat, nonfat soft tissue, and bone. Population average tissue densities within the irradiated volume were obtained. Two bulk density override plans were generated using the tissue densities reported in International Commission on Radiation Units & Measurements Report 46 and those obtained in this study, respectively. Both plans were compared to the clinically approved computed tomography-based plan to assess dosimetric accuracy. RESULTS The population average tissue densities within the irradiated volume obtained in this study were found to be different from those reported in International Commission on Radiation Units & Measurements Report 46. Use of the population average tissue densities within the irradiated volume reduced dosimetric errors for all dose metrics, for example, V100 (percentage of prostate volume receiving 100% of the prescription dose; 0.32% vs 1.73%), D95 (dose covering 95% of the target volume; 0.32% vs 0.92%), D50 (dose covering 50% of the target volume; 0.30% vs 0.89%), and maximum dose to bladder (0.37% vs 0.78%), rectum (0.35% vs 0.95%), and penile bulb (0.23% vs 0.49%). All improvements were statistically significant. CONCLUSIONS Use of population average tissue densities within the irradiated volume by the density override technique can improve the dosimetric accuracy of magnetic resonance imaging-based treatment plans for prostate cancer.

Methods to model and predict the ViewRay treatment deliveries to aid patient scheduling and treatment planning

A software tool is developed, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance image-guided radiation therapy (MR-IGRT) delivery system. This tool is necessary for managing patient treatment scheduling in our clinic. The predicted treatment delivery time and the assessment of plan complexities could also be useful to aid treatment planning. A patients total treatment delivery time, not including time required for localization, is modeled as the sum of four components: 1) the treatment initialization time; 2) the total beam-on time; 3) the gantry rotation time; and 4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected dose rate. To predict the remain-ing components, we retrospectively analyzed the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, linear regression is applied to predict the gantry rotation time. The MLC motion time is calculated using the leaves delay modeling method and the leaf motion speed. A quantitative analysis was performed to understand the correlation between the total treatment time and the plan complexity. The proposed algorithm is able to predict the ViewRay treatment delivery time with the average prediction error 0.22 min or 1.82%, and the maximal prediction error 0.89 min or 7.88%. The analysis has shown the correlation between the plan modulation (PM) factor and the total treatment delivery time, as well as the treatment delivery duty cycle. A possibility has been identified to significantly reduce MLC motion time by optimizing the positions of closed MLC pairs. The accuracy of the proposed prediction algorithm is sufficient to support patient treatment appointment scheduling. This developed software tool is currently applied in use on a daily basis in our clinic, and could also be used as an important indicator for treatment plan complexity. PACS number(s): 87.55.N.A software tool is developed, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance image‐guided radiation therapy (MR‐IGRT) delivery system. This tool is necessary for managing patient treatment scheduling in our clinic. The predicted treatment delivery time and the assessment of plan complexities could also be useful to aid treatment planning. A patients total treatment delivery time, not including time required for localization, is modeled as the sum of four components: 1) the treatment initialization time; 2) the total beam‐on time; 3) the gantry rotation time; and 4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam‐on time can be calculated using both the planned beam‐on time and the decay‐corrected dose rate. To predict the remain‐ing components, we retrospectively analyzed the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, linear regression is applied to predict the gantry rotation time. The MLC motion time is calculated using the leaves delay modeling method and the leaf motion speed. A quantitative analysis was performed to understand the correlation between the total treatment time and the plan complexity. The proposed algorithm is able to predict the ViewRay treatment delivery time with the average prediction error 0.22 min or 1.82%, and the maximal prediction error 0.89 min or 7.88%. The analysis has shown the correlation between the plan modulation (PM) factor and the total treatment delivery time, as well as the treatment delivery duty cycle. A possibility has been identified to significantly reduce MLC motion time by optimizing the positions of closed MLC pairs. The accuracy of the proposed prediction algorithm is sufficient to support patient treatment appointment scheduling. This developed software tool is currently applied in use on a daily basis in our clinic, and could also be used as an important indicator for treatment plan complexity. PACS number(s): 87.55.N

The use of exit detector sinograms to detect anatomical variations for patients extending beyond the TomoTherapy field of view: a feasibility study.

PURPOSE This work describes an independent method to use the TomoTherapy Hi-ART megavoltage CT imaging system for daily monitoring of anatomical changes of cancer patients whose anatomy extends beyond the imaging field of view. METHODS The imaging detector response to changes in attenuating media was measured using water-equivalent plastic. Weight loss was simulated using an anthropomorphic phantom and determining the systems ability to detect the weight loss. Layers of tissue-equivalent bolus were added to an anthropomorphic pelvis phantom and CT simulations of the phantom were conducted, one in which the phantom and bolus were both within the TomoTherapy imaging field of view, and another in which the couch was raised so that the bolus was outside the field of view. Gynecological treatment plans were developed using the TomoTherapy treatment planning system, and successive fractions of the plan were then delivered to the phantom. Weight loss was simulated by removing a 0.5 cm layer of bolus following each fraction. The exit detector sinograms were obtained from each fraction, and ratios of sinograms were calculated relative to a reference sinogram for which all bolus was in place. Histograms of ratio sinograms were determined and used to correlate with simulated weight loss. Exit detector sinograms and ratio histograms were also retrospectively analyzed for five patients all of whose anatomies extended beyond the imaging field of view and all of whom experienced weight variations exceeding 10% during treatment. RESULTS Exit detector signal is well correlated to changes in attenuator thickness as demonstrated in both slab and anthropomorphic phantom geometries. Measured and expected signal increases agreed to within less than 2% for simulated weight loss on the anthropomorphic phantom. Exit detector signals for pelvic patients with significant weight loss variations were consistent with phantom measurements. CONCLUSIONS The analysis of the ratio sinograms for the phantom measurements and real patients indicated that exit detector sinograms can be used to detect relative changes in patient anatomy for each fraction as a means of in vivo quality assurance.