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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 BU: D. Castañón, B. Martin NU: D. Kaeli, H. Wu MGH: G. Chen, G. Sharp, T. Bortfeld, S. Jiang MSKCC: A. Jackson, E. Yorke, C-S. Chui, L. Hong, M. Lovelock

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 Presentation

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Page 1: Advances in Radiotherapy Planning

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

Page 2: Advances in Radiotherapy Planning

CT image acquisition

Optimization via inversetreatment planning

Treatment using linear accelerator

Intensity-Modulated RadiotherapyIntensity-Modulated Radiotherapy

Page 3: Advances in Radiotherapy Planning

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

Page 4: Advances in Radiotherapy Planning

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)

Page 5: Advances in Radiotherapy Planning

Manual vs. Automatic Plans and DosesManual vs. Automatic Plans and Doses

manual5-field plan(8 hours)

automatic 5-field plan

(30 minutes)

Page 6: Advances in Radiotherapy Planning

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

Page 7: Advances in Radiotherapy Planning

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