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Advances in Radiotherapy Planning Core problems:  shape modeling  image segmentation  organ tracking  radiation planning  dose optimization  Visualization.

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Presentation on theme: "Advances in Radiotherapy Planning Core problems:  shape modeling  image segmentation  organ tracking  radiation planning  dose optimization  Visualization."— Presentation transcript:

1 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 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

2 CT image acquisition Optimization via inverse treatment planning Treatment using linear accelerator Intensity-Modulated Radiotherapy

3 Time-Consuming Steps  Manual segmentation (“contouring”) of every radiation-sensitive structure in each slice 45 min  Expert-guided optimization of radiation intensity profiles to achieve clinical acceptability 8+ hrs

4 Major 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)

5 Manual vs. Automatic Plans and Doses manual 5-field plan (8 hours) automatic 5- field plan (30 minutes)

6 4D CT Visualization all working within SCIRun 4-D movies with full volume rendering physical measurements in 3-D any clipping or filtering requested

7 Strategic 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


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