Collapsed Cone Convolution Superposition Research Articles

SU‐DD‐A1‐03: On the Quantification of the Dosimetric Accuracy of Collapsed Cone Convolution Superposition Algorithm for Small Lung Volumes Using IMRT

Purpose: To quantify the accuracy of a collapsed cone convolution superposition (CCCS) algorithm against Monte Carlo (MC) simulations for small lung lesions subject to electronic disequilibrium when very small segments are used during the IMRT optimization process. Method and Materials: IMRT plans for eleven (n=11) lung patients were created using Pinnacle3 7.6 planning system (TPS). Lung lesions measuring <3 cm in max. diameter and <27 cm3 were previously treated in our institution with SBRT techniques. The optimized intensity maps of each plan were then used to calculate the dose distributions using the CCCS algorithm. For each patient, seven optimized plans were created with varying MLC minimum segment sizes—0.25cm2 to 6cm2. The linear accelerator was modeled using MC code EGSnrc\BEAMnrc and verified against commissioned measured data. Intensity maps for each plan and the patient CT dataset from the TPS were exported to our MC software. All patients were planned using a 5‐field IMRT plan (Millennium 120‐leaf MLC and Varian 2100C 6MV beam). Dose distributions were calculated and normalized so that the isocenter receives 45.0Gy. Isodose distributions, DVH, and ROI statistics were used for comparison between the two calculation methods. Results: Comparison of the DVHs from Pinnacle3 and MC show similar target coverage between CCCS algorithm and MC. Differences in the minimum, maximum and mean dose of the PTV were less than ±5% while doses to critical structures agreed within ±6% for all cases investigated. Discrepancies in the dose to very small structures have been observed and due to volume effects between the two systems. Conclusion: Good agreement exists in the dose distributions predicted by the CCCS algorithm and MC method. The CCCS algorithm can accurately predict doses to small lung tumors.

TU‐D‐AUD‐02: Comparison Between Collapsed Cone Convolution and Monte Carlo Dose Distributions for Small Lung Lesions

Purpose: To quantify the accuracy of a collapsed cone convolution superposition (CCCS) algorithm against Monte Carlo (MC) simulations for small lung lesions subject to electronic disequilibrium. Method and materials: IMRT plans for several lung patients were created using Pinnacle 7.6 planning system. The lung lesions measured less than 3 cm diameter and less than 27 cm3 Method and were treated in our institution with SBRT. The optimized intensity maps for each plan were then used to calculate the dose distributions using the CCCS algorithm. The linear accelerator was modeled using MC code EGSnrc\BEAMnrc and verified against commissioned measured data. Phase space file information was then scored at the plane above the collimating jaws. The intensity maps for each plan and the patient CT data from the treatment planning system were exported to our MC software. All patients were planned using 5 field IMRT on a 120 leaf MLC and Varian 2100C 6 MV beam. The dose distributions were calculated and normalized so that the isocenter receives 45Gy. The isodose distributions, DVH, and ROI statistics were used for comparison between the two methods. Results: MC results show that the PTV coverage is worse than the Pinnacle CCCS algorithm. The differences in the PTV coverage is about 10% (43Gy covers 90% of PTV on Pinnacle, but only 39Gy covers 90% of PTV on Monte Carlo) The DVH differences in the lungs are not as profound since these organs are a lot larger when compared to the GTV. Conclusion: There is about a 15% difference in the coverage of the PTV as shown by the DVHs between CCCS algorithm and MC for small lung lesions. The differences arise because of the limitations of the CCCS algorithm to accurately account for electronic disequilibrium.

Accuracy of two heterogeneity dose calculation algorithms for IMRT in treatment plans designed using an anthropomorphic thorax phantom

With the advent of intensity-modulated radiation therapy (IMRT), the inclusion of heterogeneity corrections is further complicated by the conformal delivery of many small beams forming steep dose gradients. Radiation treatment planning has evolved to take into account even small changes in tissue density so that the dose to tumor can be further optimized. However, different treatment planning systems incorporate different heterogeneity correction algorithms, and it is unclear whether any of these algorithms are superior to others in terms of accurately predicting delivered radiation doses relative to measurement in a clinical setting. The purpose of this study was to determine the accuracy of heterogeneity dose calculations from two widely used IMRT treatment planning systems (Pinnacle and Corvus) against measurement. These two systems handle heterogeneity dose corrections by means of a collapsed-cone convolution superposition algorithm and a finite-size pencil-beam algorithm with one-dimensional depth scaling correction, respectively. Treatment plans were generated by each system using an anthropomorphic thorax phantom, routine clinical lung tumor constraints, and a common prescribed dose. Dose measurements made by thermoluminescent detectors (TLDs) and radiochromic film positioned within the phantom's lung and offset tumor insert were then compared with the calculated values. The collapsed cone convolution superposition dose calculation algorithm provided clinically acceptable results (+/-5% of the normalization dose or 3 mm distance to agreement) in the designed treatment plan and delivery. The pencil-beam algorithm with an effective pathlength correction showed reasonable agreement within the gross tumor volume, overestimated dose within a majority of the planning target volume, and underestimated the extent of the penumbral broadening, yielding only about 60% accuracy when judged by the above criterion. Even judged by a more generous criterion (+/-7% /7 mm), the results were clinically unfavorable (at only about 80% accuracy). To ascertain the dose in heterogeneous regions such as the tumor-lung interface and the peripheral lung dose near the tumor, the superposition convolution algorithm that accounts for lateral scatter and electron transport should be used. The use of the pencil-beam algorithm with only an effective pathlength correction may result in the dose to the target being overestimated. As a result, a full understanding of any treatment planning system's heterogeneity algorithm is required prior to clinical implementation.

Interinstitutional Variations in Planning for Stereotactic Body Radiation Therapy for Lung Cancer

The aim of this study was to assess interinstitutional variations in planning for stereotactic body radiation therapy (SBRT) for lung cancer before the start of the Japan Clinical Oncology Group (JCOG) 0403 trial. Eleven institutions created virtual plans for four cases of solitary lung cancer. The created plans should satisfy the target definitions and the dose constraints for the JCOG 0403 protocol. FOCUS/XiO (CMS) was used in six institutions, Eclipse (Varian) in 3, Cadplan (Varian) in one, and Pinnacle3 (Philips/ADAC) in one. Dose calculation algorithms of Clarkson with effective path length correction and superposition were used in FOCUS/XiO; pencil beam convolution with Batho power law correction was used in Eclipse and Cadplan; and collapsed cone convolution superposition was used in Pinnacle3. For the target volumes, the overall coefficient of variation was 16.6%, and the interinstitutional variations were not significant. For maximal dose, minimal dose, D95, and the homogeneity index of the planning target volume, the interinstitutional variations were significant. The dose calculation algorithm was a significant factor in these variations. No violation of the dose constraints for the protocol was observed. There can be notable interinstitutional variations in planning for SBRT, including both interobserver variations in the estimate of target volumes as well as dose calculation effects related to the use of different dose calculation algorithms.

SU‐FF‐T‐448: Validation of a New Photon Dose Calculation Model—Analytical Anisotropic Algorithm

Purpose: To validate a new photon dose calculation model Analytical Anisotropic Algorithm (AAA) on Eclipse™ treatment planning system (TPS). Comparison of AAA dose calculation was performed with measurements and other two conventional algorithms, Pencil Beam Convolution (PBC) algorithm on Eclipse™ and Collapsed Cone Convolution/Superposition (CCC) algorithm on Pinnacle3.0 TPS.Method and Materials: Four phantoms were CT scanned and the image set was imported into both TPS for dose computation and analysis. The four phantoms were: 1) homogenous tissue equivalent phantom, 2) tissue equivalent phantom with infinite lung heterogeneity, 3) tissue equivalent phantom with finite lung, 4) IMRT dose verification phantom. Measurements were made by exposing the phantom using Varian Linac. Point measurement and film measurements were compared with calculated results from the three algorithms. Dose responses for high and low energy photon beams were investigated for several different depths and PDD curves were compared in the phantom for various field sizes. The IMRT plans were generated by both TPS and were performed on the IMRT phantom to compare fluence maps. Results: AAA dose prediction fits the film measurements well except that there is up to ±6% discrepancy for dose profile perpendicular to the interface of tissue and lung. Point measurements support the AAA algorithm calculations. AAA also accurately predicts the decrease in PDD curves due to the lung inhomogeneity for 6MV energy. For the high energy photon beam and very small field size (2cm*2cm) in lung region, AAA prediction is up to 8% lower than the measurements. Conclusion: AAA algorithm accounts for attenuation corrections and electron transport, and models the deposited dose in the lung with greater accuracy than PBC. It is also faster than CCC algorithm. AAA algorithm can not accurately model the lateral scattering in tissue heterogeneity, but it can still give a reasonably close (within ±6%) prediction.

TH-C-T-6C-01: A Collapsed-Cone Convolution/superposition Dose Computation Algorithm for Sliding Window Beams

Purpose: An algorithm was developed to compute the incident energy fluence array used when computing dose for a Sliding Window beam using the collapsed-cone convolution/superposition (CCCS) dose computation algorithm. Method and Materials: The first step of a CCCS dose computation is the computation of the energy fluence array incident on the patient. The incident energy fluence array computation for a Sliding Window beam is the product of an opening density array (an integration over space-time of the multi-leaf collimator (MLC) leaf trajectories) and an open-field energy fluence array. An opening density represents the fraction of the time a pixel is exposed during treatment (i.e. not covered by the MLC leaves). Models of the MLC leaf end shape, tongue-and-groove and interleaf leakage effects are used by the algorithm. For an MLC with rounded leaf ends, partial transmission based on the leaf end profile model is applied to the areas that are covered by the rounded tip of an MLC leaf. Transmission corresponding to half of the leaf thickness is applied to the areas that are covered by only the tongue or the groove of a leaf. Interleaf leakage is applied to the areas that are covered by the tongue of a leaf and the groove of an adjacent (or opposing adjacent) leaf. Linear accelerator head scatter effects are also taken into account by convolving the head scatter array by the total transmission array (opening density array + leaf transmission array). Results: The algorithm to compute dose for a Sliding Window was implemented and released in a commercial treatment planning system. Validation results showed point dose measurement accuracy of 4 % or better in the high dose areas. Conclusion: The Sliding Window CCCS dose computation algorithm meets the AAPM TG 53 dose accuracy criteria (Med Phys, 25, 1773–1829).

Dose volume histogram comparison between ADAC Pinnacle and Nomos Corvus systems for IMRT

This paper compares dose volume histograms (DVHs) generated by the ADAC Pinnacle and the Nomos Corvus planning systems. Seven prostate cases and seven head and neck cases were selected for review. Plans computed on both systems possessed exactly the same anatomical contours and IMRT segments. The Pinnacle system used the collapsed cone convolution superposition, while Corvus employed a finite size pencil beam (FSPB) convolution. Prostate DVH results demonstrated similar DVH curves from both systems. For each structure, the ratio of Pinnacle dose value divided by Corvus value was calculated. The high dose structures (which might contain tumour) had ratios close to unity, while the low dose structures (the critical organs) had ratios farther away from unity. Almost all ratios were less than unity, indicating a systematic difference that Pinnacle calculated doses were lower than Corvus ones. Head and neck data provided similar findings. A possible cause for this discrepancy could be the beam modelling. The difference in DVH parameters that we discovered between the two systems was about the same order of magnitude as the measurement-computation difference. When low dose is critical, such difference may affect the clinical planning decision.

Evaluation of dose calculation algorithms for dynamic arc treatments of head and neck tumors

Background and purpose: To investigate if the Pencil Beam (PB) algorithm takes the disturbance of the dose distribution due to tissue inhomogeneities sufficiently into account in dynamic field shaping rotation therapy (called the dynamic arc treatment modality) for fractionated stereotactic radiation therapy of head and neck tumors. Material and methods: A treatment plan using the dynamic arc treatment modality of an oropharynx lesion on a humanoid phantom was evaluated. The same plan was calculated with three different calculation algorithms: the Clarkson and the PB algorithm (both available on the planning system of the Novalis ® system used for dynamic arc treatments), and the Collapsed Cone Convolution Superposition (CC) algorithm (used by the Pinnacle ® planning system). The three resulting plans are compared using isodose distributions and cumulative dose volume histograms (CDVHs). An intercomparison of the results of the three algorithms was performed to investigate how accurately each of them takes the influence of tissue inhomogeneities into account such as bony structures and air cavities often appearing in the head and neck region. Additionally, the resulting plans were compared with absolute and relative dosimetric measurements of the treatment plan on the humanoid phantom with thermoluminescent detectors and radiographic film, respectively. Results: All calculated dose distributions show a good agreement with the measured distribution except in the planning target volume (PTV) in and at the border of the air cavity. All three algorithms overestimate the dose in the PTV at the boundary with the low-density tissue, with 12, 10 and 7% for the Clarkson, the PB and the CC algorithm, respectively. The correspondence between the calculated dose distributions is reflected in the graphs of the CDVHs. They show similar curves for the PTV and the structures except for the left parotic gland and the myelum. Conclusions: The PB algorithm of the Novalis ® system calculates a treatment plan for the dynamic arc treatment modality adequately for fractionated stereotactic radiation therapy of head and neck tumors, except in the PTV in and at the border of the air cavity where the actual dose is overestimated. Care needs to be taken in clinical cases where it is critical to irradiate the air–tissue boundary to a sufficient dose.

Comparison of commercially available three-dimensional treatment planning algorithms for monitor unit calculations in the presence of heterogeneities

This study uses an anthropomorphic phantom and its computed tomography (CT) data set to evaluate monitor unit (MU) calculations using the CMS Focus Clarkson, the CMS Focus Multigrid Superposition Model, the CMS Focus FFT Convolution Model, and the ADAC Pinnacle3 Collapsed Cone Convolution Superposition Algorithms. Using heterogeneity corrections, a treatment plan and corresponding MU calculations were generated for several typical clinical situations. A diode detector, placed in an anthropomorphic phantom, was used to compare the treatment planning algorithms' predicted doses with measured data. Differences between diode measurements and the algorithms' calculations were within reasonable levels of acceptability as recommended by Van Dyk et al. [Int. J. Rad. Onc. Biol. Phys. 26, 261–273 (1993)], except for the CMS Clarkson algorithm, which predicted too few MU for delivery of the intended dose to chest wall fields. PACS number(s): 87.53.Bn, 87.53.Dq