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Advances in Radiotherapy Planning

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

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Advances in Radiotherapy Planning

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

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

  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)

  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

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