advances in radiotherapy planning
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Advances in Radiotherapy Planning. Core problems: shape modeling image segmentation organ tracking radiation planning dose optimization Visualization Challenge: accelerate labor-intensive tasks without loss of performance. RPI: R. Radke, Y. Jeong, R. Lu, S. Chen - PowerPoint PPT PresentationTRANSCRIPT
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Advances in Radiotherapy PlanningAdvances in Radiotherapy Planning
Core problems: shape modeling image segmentation organ tracking radiation planning dose optimization Visualization
Challenge: accelerate labor-intensive tasks without loss of performance
Core problems: shape modeling image segmentation organ tracking radiation planning dose optimization Visualization
Challenge: accelerate labor-intensive tasks without loss of performance
RPI: R. Radke, Y. Jeong, R. Lu, S. ChenBU: D. Castañón, B. MartinNU: D. Kaeli, H. WuMGH: G. Chen, G. Sharp, T. Bortfeld, S. JiangMSKCC: A. Jackson, E. Yorke, C-S. Chui, L. Hong, M. Lovelock
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CT image acquisition
Optimization via inversetreatment planning
Treatment using linear accelerator
Intensity-Modulated RadiotherapyIntensity-Modulated Radiotherapy
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Time-Consuming StepsTime-Consuming Steps
Manual segmentation (“contouring”) of every radiation-sensitive structure in each slice
Manual segmentation (“contouring”) of every radiation-sensitive structure in each slice
45 min
Expert-guided optimization of radiation intensity profiles to achieve clinical acceptability
Expert-guided optimization of radiation intensity profiles to achieve clinical acceptability
8+ hrs
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Major Censsis ResultsMajor Censsis Results
Fast, accurate segmentation of 3D CT in low contrast areas using clinically useful organ shape models
Breast IMRT planning using machine learning minutes ! seconds
Prostate IMRT planning (Posters R2D p5,6) Parameter-based sensitivity analysis,
optimization, and machine learning hours ! minutes
IMRT planning under location/shape uncertainty (Poster R2D p9) New algorithms that speed up plans by
20X State-of-the-art 4D visualization (Poster
R3B p3)
Fast, accurate segmentation of 3D CT in low contrast areas using clinically useful organ shape models
Breast IMRT planning using machine learning minutes ! seconds
Prostate IMRT planning (Posters R2D p5,6) Parameter-based sensitivity analysis,
optimization, and machine learning hours ! minutes
IMRT planning under location/shape uncertainty (Poster R2D p9) New algorithms that speed up plans by
20X State-of-the-art 4D visualization (Poster
R3B p3)
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Manual vs. Automatic Plans and DosesManual vs. Automatic Plans and Doses
manual5-field plan(8 hours)
automatic 5-field plan
(30 minutes)
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4D CT Visualization4D CT Visualization
• all working within SCIRun• 4-D movies with full volume rendering• physical measurements in 3-D• any clipping or filtering requested
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Strategic Goals and SustainabilityStrategic Goals and Sustainability
IMRT availability has exploded since 2001 Human involvement is a speed bottleneck at
several critical points (contouring, planning) CenSSIS algorithms can assist by improving
speed without sacrificing quality
Goal: Incorporation of CenSSIS results in IMRT Tech transfer through MGH and MSKCC: leaders
in IMRT Integration into treatment planning system
vendors
Sustainability plans $1.4M R01 proposal submitted to NCI R21 proposals in development Extension of results to other treatment areas
IMRT availability has exploded since 2001 Human involvement is a speed bottleneck at
several critical points (contouring, planning) CenSSIS algorithms can assist by improving
speed without sacrificing quality
Goal: Incorporation of CenSSIS results in IMRT Tech transfer through MGH and MSKCC: leaders
in IMRT Integration into treatment planning system
vendors
Sustainability plans $1.4M R01 proposal submitted to NCI R21 proposals in development Extension of results to other treatment areas