The advent of dynamic radiotherapy modeling and treatment techniques requires an infrastructure to weigh the merits of various interventions (breath holding, gating, tracking). The creation of treatment planning models that account for motion and deformation can allow the relative worth of such techniques to be evaluated. In order to develop a treatment planning model of a moving and deforming organ such as the lung, registration tools that account for deformation are required. We tested the accuracy of a mutual information based image registration tool using thin-plate splines driven by the selection of control points and iterative alignment according to a simplex algorithm. Eleven patients each had sequential CT scans at breath-held normal inhale and exhale states. The exhale right lung was segmented from CT and served as the reference model. For each patient, thirty control points were used to align the inhale CT right lung to the exhale CT right lung. Alignment accuracy (the standard deviation of the difference in the actual and predicted inhale position) was determined from locations of vascular and bronchial bifurcations, and found to be 1.7, 3.1, and 3.6 mm about the RL, AP, and IS directions. The alignment accuracy was significantly different from the amount of measured movement during breathing only in the AP and IS directions. The accuracy of alignment including thin-plate splines was more accurate than using affine transformations and the same iteration and scoring methodology. This technique shows promise for the future development of dynamic models of the lung for use in four-dimensional (4-D) treatment planning.

Inverse planned intensity modulated radiotherapy (IMRT) fields can be highly modulated due to the large number of degrees of freedom involved in the inverse planning process. Additional modulation typically results in a more optimal plan, although the clinical rewards may be small or offset by additional delivery complexity and/or increased dose from transmission and leakage. Increasing modulation decreases delivery efficiency, and may lead to plans that are more sensitive to geometrical uncertainties. The purpose of this work is to assess the use of maximum intensity limits in inverse IMRT planning as a simple way to increase delivery efficiency without significantly affecting plan quality. Nine clinical cases (three each for brain, prostate, and head/neck) were used to evaluate advantages and disadvantages of limiting maximum intensity to increase delivery efficiency. IMRT plans were generated using in-house protocol-based constraints and objectives for the brain and head/neck, and RTOG 9406 dose volume objectives in the prostate. Each case was optimized at a series of maximum intensity ratios (the product of the maximum intensity and the number of beams divided by the prescribed dose to the target volume), and evaluated in terms of clinical metrics, dose-volume histograms, monitor units (MU) required per fraction (SMLC and DMLC delivery), and intensity map variation (a measure of the beam modulation). In each site tested, it was possible to reduce total monitor units by constraining the maximum allowed intensity without compromising the clinical acceptability of the plan. Monitor unit reductions up to 38% were observed for SMLC delivery, while reductions up to 29% were achieved for DMLC delivery. In general, complicated geometries saw a smaller reduction in monitor units for both delivery types, although DMLC delivery required significantly more monitor units in all cases. Constraining the maximum intensity in an inverse IMRT plan is a simple way to improve delivery efficiency without compromising plan objectives.

No abstract

Purpose: To report on the implementation of a Monte Carlo (MC) based algorithm and to compare this system with a convolution/superposition‐based algorithm (CS) for IMRT inverse planning. Method and Materials: The DPM MC code was modified using a fluence matrix approach to perform beamlet calculations for IMRT planning. The code was integrated within our in‐house inverse treatment planning system and compared with the TPS (CS) algorithm. Initial testing involved the computation of 6 MV beamlet depth doses for 1×1, 2×2 and 10×10 (100, 1 cm beamlets) in a water phantom. MC and CS calculations were then performed for an example lung treatment plan to examine dosimetric differences between these algorithms. MC statistical uncertainties were on average less than 2% (in the depth doses) for all beamlet calculations. Optimization of beamlet doses is carried out using simulated annealing with quadratic cost functions derived from our clinical protocols. Results: Beamlet depth doses calculated with MC and CS are in good absolute agreement for field sizes larger than 2×2 cm2. Significant differences exist for 1×1 beamlets because CS is unable to accurately model lateral electron transport. For the example lung plan, much smaller differences were found. This is likely due to the fact that with larger field sizes (∼10×10 cm in the example), effects of lateral electron scattering are much less pronounced. Conclusion: We have implemented a fluence matrix method to perform MC‐based beamlet calculations for IMRT planning. Initial testing for an example lung plan and field sizes larger than 2×2, revealed good agreement between MC and CS. However, larger differences were found for 1×1 beamlets due to lateral electron transport issues. Testing is currently being performed for a variety of treatment plans, spanning a range of field sizes to thoroughly investigate dosimetric differences between MC and CS in IMRT planning. Supported by NIH P01‐CA59827

No abstract

Purpose: To investigate the use of mathematical basis functions instead of discrete or smoothed beamlets to represent and optimize IMRT beams in inverse planning in order to reduce the degrees of freedom required for producing high quality IMRT plans. Method and Materials: Our in‐house beamlet‐based optimization system was extended to support the optimization of basis function coefficients in order to represent IMRT beams by mathematical surfaces. Comparison studies were performed using a phantom and a prostate case. Four methods were compared; (1) beamlet optimization, (2) beamlet optimization incorporating smoothing in the cost function, (3) Basis function optimization (BFO) using a radial basis function grid (optimization variables are individual function weights), and (4) BFO using polynomials (optimization variables are term coefficients). Results were compared using dose and dose‐volume metrics, beam modulation, MU, and robustness to geometric deviations. Results: In the phantom, BFO plans were comparable to beamlet plans in terms of dose and dose‐volume metrics and superior in terms of using 75–90% fewer optimization variables, requiring 26% fewer MU, containing 38% less plan modulation, and demonstrating improved target coverage when subjected to geometric shifts. Beamlet‐based plans that incorporated smoothing met the clinical objectives, with a 16% reduction in MU compared to beamlets. In the prostate, method (3) resulted in a 24% MU reduction compared to method (1) and was less sensitive to geometric changes. Method (2) also produced favorable results in the prostate with a 19% reduction in MU. Conclusion: BFO plans were clinically comparable to beamlet plans and superior to beamlet plans with smoothing in terms of MU reduction and lessened geometric sensitivity. The use of basis function sets to represent and optimize IMRT intensity patterns is a promising method to reduce IMRT beam complexity and its implications. Supported in part by NIH P01‐CA59827.