Intensity-Modulated Radiation Therapy - CenSSIS

censsis.neu.edu

Intensity-Modulated Radiation Therapy - CenSSIS

Intensity Modulated Radiation

Therapy

Richard J. Radke, Dept. of ECSE, RPI

George T.Y. Chen, Dept. of Rad. Onc.,

MGH


Radiotherapy Pipeline

CT image

acquisition

Optimization

via inverse

treatment

planning

Treatment

using linear

accelerator


Precise Radiation Delivery Advances

Dynamic multileaf collimator

tumor

brain

stem

Multiple nonuniform fields


Correspondence Between Datasets


Inter- and Intra-Patient Shape Models


Automatic Pelvic Organ Segmentation

< 1 mm surface-tosurface

distance

< 1 min

No manual

intervention

black/wireframe = ground truth, yellow/solid = automatic segmentation


3D and 4D Visualization Tools

• Developed new

image motion

estimation

algorithms based on

subsequence

matching, e.g., for

respiratory motion

• Delivered

visualization tools

to allow physicians

to rapidly segment

and identify regions

of interest in 3D or

4D CT imagery


Reduced Order Constrained Optimization

for IMRT Planning

PTV prescription

dose weight

Rectum maximum

dose limit

Bladder maximum

dose weight

(x 20)

IMRT

Objective

Function

Optimization

PTV V95 > 87%

PTV max dose < 111%

Rectal wall max dose < 99%

Rectal wall V87 < 30%

Rectal wall V54 < 53%

Bladder wall V54 < 53%

• Do we really need all 20+ knobs

• Can we find good parameters automatically


Validation: Dose-Volume Histograms

ROCO for prostate (2007) ROCO for lung (2010)


Clinical Validation: Dose Distributions

ROCO for prostate (2007)

ROCO for lung (2010)


Conclusions

• Fast, automatic 3D organ segmentation built

from expert training data; from minutes to

seconds

• Fast, general-purpose IMRT planning; from

hours to minutes

• Fast 3D and 4D visualization tools

• ROCO currently being integrated into MSKCC

treatment planning system, applied to other

sites

• Follow-on R01 from NIH to continue research


George TY Chen, Ph.D.

Department of Radiation Oncology

Professor, Harvard Medical School


Important Connections

• Students

Co-supervised by MGH Radiation Oncology faculty (T.

Bortfeld, SB Jiang, GC Sharp, J Wolfgang, GTY Chen) –

Collaborate d with CenSSIS faculty (D. Kaeli, D. Brooks,

D. Castanon, J. Dy

Segmentation, IMRT, Robust Optimization, Hardware

Acceleration w/undergraduates and graduate students

Guided REU students

• Most valuable connection: interactions –

CenSSIS: a portal into image processing experts

• Stimulated interests in scientific visualization


Scientific Visualization


Fovia Lung

• st1mm_4d_test_21.mov


Conventional Planning Image DRR


BEV Outline MLC


Breast w/ outline


Current Work -at MGH

• Procedural Shader

• Goal: Visualize Uncertainty in Radiotherapy

• Examples of real time rendering of radiologic

pathlength

• To visible surface

• To back edge of tumor (ITV)


Water Equivalent Pathlength

Surface of

Anatomy


Water Equivalent Pathlength

To Tumor

ITV


Shadie: A Programming Language

for Volume Visualization

- Subset of Python

- Built on the concept of "shaders"

- Computes along a ray during visualization

- Multiple transfer functions, colormaps,

structures

- Multiple volumes, 4D support

- Translation to fast GPU code (~100x)

- Milos Hasan / HP Pfister (Harvard SEAS)


Challenges in RO Visualization

•Visualization in 4D or + / interactive

•Visualization of uncertainty and errors

•Quantify effectiveness

•Segmentation aided by visualization

•Domain specific features

Adapted from C. Johnson: Top Scientific Visualization Challenges

0272-1716/04/$20.00 © 2004 IEEE


The Future

4D Treatment Planning Workshop @MGH February 2010

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