S86 I. J. Radiation Oncology d Biology d Physics Volume 72, Number 1, Supplement, 2008

directions. The volume of the PTV based on the non-uniform margin was 65% smaller than for the uniform margin approach (range53-75%). The use of the non-uniform margin reduces the exposure of the bladder by 77% (range 49-92%) and rectum by 63%(range 42-83%), compared to the uniform margin approach (p = 0.001 for all comparisons).

Conclusions: An adaptive patient-specific non-uniform margin can significantly reduce the PTV size (65%), and resultant expo-sure to bladder and rectum (77% and 63%, respectively), compared to the use of uniform margins. This is a potential way to fullyexploit the information afforded by IGRT to further improve the therapeutic ratio.

Author Disclosure: S. Zhou, None; S. Das, None; F. Yin, None; S. Yoo, None; W. Lee, None; H. Yan, None; Q.J. Wu, None; Z.Wang, None; L. Marks, None.

191 Reduced-order Constrained Optimization in IMRT Planning

R. Lu1, R. Radke1, L. Happersett2, J. Yang2, E. Yorke2, A. Jackson2

1Dept of ECSE, Rensselaer Polytechnic Institute, Troy, NY, 2Dept of Medical Physics, Memorial Sloan-Kettering Cancer Center,New York, NY

Purpose/Objective(s): IMRT optimization typically involves minimizing a weighted sum of penalties reflecting violations of clin-ical constraints using fast conjugate gradient algorithms. However, since the constraints can be violated during optimization, ob-taining an acceptable IMRT plan for a difficult site can take several hours, due to the manual process of adjusting the parameters inthe objective function. Formulating the criteria as hard constraints provides better control over the resulting plans, and requires noartificial parameters. However, since IMRT optimization typically involves thousands of intensity variables and constraints, con-strained optimization techniques like linear or mixed-integer programming are far too slow to be used in the clinic, and are difficultto use with dose-volume constraints (DVCs). We describe a new IMRT optimization technique leveraging two advances that avoidthe parameter adjustment problem and preserve fast convergence.

Materials/Methods: First, we perform patient-specific unconstrained optimizations over many different combinations of param-eters in the penalized cost function, and identify a low-dimensional subspace that effectively represents the resulting intensitydistributions using Monte Carlo simulation and Principal Component Analysis. In 36 86.4 Gy prostate patients, less than 10principal components were needed to capture 99% of the fluence matrix variances, a substantially better result than previousmethods using the Hessian of the unconstrained objective function. Second, we use constrained optimization in this reducedspace to quickly refine the intensities so as to meet all the clinical requirements, leveraging a simple but effective iterativemethod for correctly incorporating DVCs. Using doses calculated with a truncated kernel, plans were created for 36 patientsand evaluated using a written clinical protocol. A 10 patient subset was evaluated for clinical acceptability following fulldose calculation.

Results: For prostate cases and a modern PC workstation, after 5 minutes spent in dimensional reduction, the constrained optimi-zation converged in 15 seconds on average. When self consistently evaluated using the kernel-based dose calculation all 36 plansmet the constraints specified by the clinical protocol. Of the 10-plan subset evaluated following full dose calculation, 5 were ac-cepted by an experienced treatment planner. The remaining 5 plans were very close to acceptable, with deficiencies due to errors ofa few percent in the approximate dose calculation used in optimization.

Conclusions: Determining an appropriate reduced-order search space takes only a few minutes, enabling a constrained optimiza-tion algorithm that is both very fast and can achieve clinical constraints. The approach can be easily extended to more complex sites.

Author Disclosure: R. Lu, None; R. Radke, None; L. Happersett, None; J. Yang, None; E. Yorke, None; A. Jackson, None.

192 A Simple, Robust IMRT Optimization Method for Lung Cancer, Accounting for Tissue Heterogenity and

Intra-fraction Lung Tumor Motion

J. Bissonnette1, A. J. Hope1, A. Lundin2, H. Rehbinder3, T. G. Purdie1

1Princess Margaret Hospital, Toronto, ON, Canada, 2RaySearch Laboratories, Stockholm, Sweden, 3Raysearch Laboratories,Stockholm, Sweden

Purpose/Objective(s): The theoretical basis for the optimization of IMRT plans that are robust to positional uncertainties havebeen reported, most notably for lung cancer. Rather than relying on margins to ensure target coverage, robust methods compensatefor a target moving partially outside of the treatment field by increasing the dose delivered on that field, thereby ensuring adequatetarget coverage while sparing healthy tissues better. In the lung, this loss of coverage is further exacerbated by reduced doses at theperiphery of lung tumors due to tissue heterogeneity. We propose a simple planning technique to apply the principles of robustplanning to lung cancer radiation therapy.

Materials/Methods: Four patients with 4DCT planning scans were randomly chosen and re-planned using both a conventionalIMRT and a simple robust approach, using identical beam arrangements. The conventional IMRT plan attempted to cover thePTV accounting for the full extent of tumor motion as defined by the planning 4DCT [PTVconv]. The robust approach consistedof defining a PTV only on the exhale phase of breathing [PTVexh], but compensating for intra-fraction tumor motion and tissueheterogeneity by increasing the dose by 10% to specific parts of the PTVexh: (i) projecting the parts of the PTVconv that fall outsideof the PTVexh onto the PTVexh and (ii) any part of the PTVexh intersecting with normal lung tissue. Both plans were normalized toidentical ITV coverage. Dose accumulation in deformable and movable target and lung voxels was performed, based on multiplephases from the planning 4DCT data set, using research software (Orbit Workstation, Raysearch Laboratories).

Results: Tumor motion ranged from 2 to 10 mm in the cranio-caudal direction. For three patients, the accumulated dose to theipsilateral lung was lower in the robust plan; on average, V20 decreased by 3.0% [2.3% to 4.0%], V10 decreased by 2.9%[1.0% to 5.2%], and V5 decreased by 3.2% [0.9% to 5.6%]. For the fourth patient, the robust plan increased V20 by 5.1%,V10 by 3.4%, and V5 by 0.2%. For all cases, the accumulated dose to CTV was increased, in the robust plan, by 3.3 Gy [0.6Gy to 5.2 Gy]. The accumulated CTV dose in robust plans was always at the prescription dose level or higher; conventional plansonly achieved this in one case.