Monaco VMAT. The Next Generation in IMRT/VMAT Planning. Paulo Mathias Customer Support TPS Application

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Monaco VMAT The Next Generation in IMRT/VMAT Planning Paulo Mathias Customer Support TPS Application 11.05.2011

Background What is Monaco? Advanced IMRT/VMAT treatment planning system from Elekta Software Collaboration with University of Tübingen (UKT) Based on the Hyperion project started in 2000 (M. Alber, et al) Monaco 1.0 released July 2007

Unique Features Biological Modeling Constrained Optimization Voxel-based Structure Control Sensitivity Analysis Smart Sequencing TM Monte Carlo Dose Algorithm VMAT SRS Planning/Dynamic Conformal Arc

Unique Features Biological Modeling Constrained Optimization Voxel-based Structure Control Sensitivity Analysis Smart Sequencing TM Monte Carlo Dose Algorithm VMAT SRS Planning/Dynamic Conformal Arc

Biological Modeling Biological cost functions allow us to model tissue-specific dose responses, that is the volume effect. Serial (small volume effect) Parallel (large volume effect) Tumor (sensitive to cold spots) But, for treatment planning, these cost functions really allow us to control the shape of the DVH

Volume Controlling the DVH (Serial Tissues) Biological Modeling: Controlling the DVH Serial Tissues Maximum Dose Cost Function (Controls only a single point on DVH) Spinal Cord < 45 Gy 45 Gy

Volume Biological Modeling: Controlling the DVH Controlling the DVH (Serial Tissues) Serial Tissues EUD-based Serial Cost Function (Controls many points on DVH, emphasis on high doses) Spinal Cord EUD < 35 Gy dose

Volume Biological Modeling: Controlling the DVH Controlling the DVH (Serial Tissues) Parallel Tissues DVH Cost Function (Controls only a single point on DVH) 50 % of parotid < 30 Gy 50 % 30 Gy dose

Volume Biological Modeling: Controlling the DVH Controlling the DVH (Serial Tissues) Parallel Tissues Parallel Model Cost Function (Controls many points on DVH, emphasis on mean dose) 50 % Mean dose of 30 Gy is expected to damage 50 % of parotid 30 Gy dose

Unique Features Biological Modeling Constrained Optimization Voxel-based Structure Control Sensitivity Analysis Smart Sequencing TM Monte Carlo Dose Algorithm VMAT SRS Planning/Dynamic Conformal Arc

Constrained Optimization Unconstrained Optimization (Most current IMRT systems) PTV1 OAR Constrained Optimization (Monaco) PTV2 OAR PTV1 OAR! Trade off between all objectives PTV2 OAR Constraints may limit target objectives But conflicts become apparent!

Unique Features Biological Modeling Constrained Optimization Voxel-based Structure Control Sensitivity Analysis Smart Sequencing TM Monte Carlo Dose Algorithm VMAT SRS Planning/Dynamic Conformal Arc

Sensitivity Analysis Changing the isoconstraint of PAROTID_RT by 1% improves the dose to PTV4 by 52 cgy

Unique Features Biological Modeling Constrained Optimization Voxel-based Structure Control Sensitivity Analysis Smart Sequencing TM Monte Carlo Dose Algorithm VMAT SRS Planning/Dynamic Conformal Arc

Voxel-based Structure Control: Volume ToolBar: Dose Option The user can switch view from images only and dose overlay with different intensities

Voxel-based Structure Control: Volume ToolBar: Dose Raw and Electron Density The user can switch view to un-interpolated dose and full electron density according to pixel and user defined voxel

Voxel-based Structure Control: Volume ToolBar: Cost Function Occupancy Displays which voxels are being used for a particular structure s cost function. Takes into account layering order, shrink margin, optimize over all voxels, clear etc

Volume Voxel-based Structure Control: Volume ToolBar: Cost Function Variation Serial CF: Maximum dose: 65 Gy High penalty area No penalty area Dose 65 Slide courtesy of Erasmus MC University Medical Center Rotterdam

Voxel-based Structure Control: Volume ToolBar: Monte Carlo Dose Uncertainty Displays the Dose uncertainty throughout the patient. The Dose uncertainty of the target region will also be displayed in the console window during stage 2 optimization

Voxel-based Structure Control: Volume ToolBar: MC Dose Uncertainty The Dose Uncertainty, specified by the user, is per segment. Therefore the uncertainty over the target will always be less The peripheral uncertainty will increase as the doses become lower and more insignificant.

Voxel-based Structure Controls: Minimum electron density fill Automatic clearing of air voxels Auto-flash margins Surface Margin Beamlet width Target Margin Avoidance Margin Clear and Fill Voxelized Structure Application of Clear Application of Fill

Unique Features 1. Biological Modeling 2. Constrained Optimization 3. Voxel-based Structure Control 4. Multicriterial Goals 5. Sensitivity Analysis 6. Smart Sequencing TM 7. Monte Carlo Dose Algorithm 8. VMAT 9. SRS Planning/Dynamic Conformal Arc

Smart Sequencing Segmentation is integrated in the final optimization loop using XVMC Monte Carlo Dose calculations for each segment Fluence smoothing Shape optimization Weight optimization Minimum MU/segment Minimum segment area (cm 2 ) Max. # Control Points/Arc

Unique Features 1. Biological Modeling 2. Constrained Optimization 3. Voxel-based Structure Control 4. Multicriterial Goals 5. Sensitivity Analysis 6. Smart Sequencing TM 7. Monte Carlo Dose Algorithm 8. VMAT 9. SRS Planning/Dynamic Conformal Arc

Advantages of Monte Carlo in VMAT No need to discretize arc into sub beams for dose calculation Discretized Pencil Beam Arc Stage 2 Comparison Continuous Monte Carlo Arc

Advantages of Monte Carlo in VMAT Pencil Beam rays are calculated at at each segment (CP) within the arc Star pattern effect Looks like a static gantry calculation, not reality As overlap between PB rays decreases, information becomes less accurate This type of calculation is similar to what other systems offer

Advantages of Monte Carlo in VMAT Monte Carlo calculations are done to mimic the gantry rotation around the patient Every photon particle is simulated at a random gantry angle somewhere along the arc Produces a smooth distribution of photon particles completely around the arc Artifacts from discretized control points are smoothed out MC is the only algorithm that performs the calculation in this manner

Unique Features 1. Biological Modeling 2. Constrained Optimization 3. Voxel-based Structure Control 4. Multicriterial Goals 5. Sensitivity Analysis 6. Smart Sequencing TM 7. Monte Carlo Dose Algorithm 8. VMAT 9. SRS Planning/Dynamic Conformal Arc

VMAT Planning Single or Multiple Arc plans Specify: Collimator Angles, Gantry Start Angles, Couch Kicks, Arc Increments Ability to optimize multiple arcs simultaneously Creation of a multiple arc plan that can be treated with a single button push at the linac console MC dose engine allows for continuous arc calculation instead of being limited to dose approximations with discrete gantry positions

VMAT Planning: Example 2: Partial Arc 320 0

VMAT Planning: Example 1: Single Arc Setup 360 0

VMAT Planning: Example 3: Two Arcs with different Isocenters

VMAT: Sequencing

Monaco 3.0 Varian VMAT Varian VMAT Arc Info User can decrease the total number of arcs by increasing the Target Dose Rate (up to maximum) prior to stage 2 optimization. At the beginning of stage 2, the console will indicate the number of arcs used. Varian arcs are automatically sorted and split upon DICOM export. User should reduce the parameter Max # Control Points / Arc accordingly for Varian VMAT plans.

Unique Features 1. Biological Modeling 2. Constrained Optimization 3. Voxel-based Structure Control 4. Multicriterial Goals 5. Sensitivity Analysis 6. Smart Sequencing TM 7. Monte Carlo Dose Algorithm 8. VMAT 9. SRS Planning/Dynamic Conformal Arc

SRS Planning/Dynamic Conformal Arc Stereotactic planning with dynamic conformal arcs and Apex mmlc Support for multiple isocenters Frame based stereotactic localization using Ergo++ Localizer module

Monaco: The Next Generation in IMRT Planning Monaco as dedicated IMRT/VMAT system Meaningful way of prescribing using Biological cost functions Optimization Changes applicable during optimization Constrained optimization Evaluation tools (impact factor, sensitivity analysis) No final Recalculation optimization of segment shape and weight Monte Carlo Simulations during optimization

Muchas gracias!