Motion Planning in Stereotaxic Radiosurgery A. Schweikard, J.R. Adler, and J.C. Latombe Presented by...

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Transcript of Motion Planning in Stereotaxic Radiosurgery A. Schweikard, J.R. Adler, and J.C. Latombe Presented by...

Motion Planning in Stereotaxic

RadiosurgeryA. Schweikard, J.R. Adler, and J.C. Latombe

Presented by Vijay Pradeep

Tumor = bad

Brain = good

Critical Section= good & sensitive

Minimally invasive procedure that uses an intense, focused beam of radiation as an ablative surgical instrument to destroy tumors

Radiosurgery Problem

Radiosurgery Methods – Single Beam

Radiation

Single Beam:- High Power along entire cylinder- Damages lots of brain tissue

Dose from multiple beams is additive

Radiosurgery Methods – Multiple Beams

- Intersection of beams is spherical- Energy is highest at tumor

Radiation

LINAC System

• Goal:– Determine a set of beam configurations that will

destroy a tumor by cross firing at it

• Parameters:– Assume Spherical Tumor– LINAC Kinematics (Only Vertical Great-Circle Arcs)– Minimum angle of separation between arcs– Min # Of Arcs

Critical

Tumor

Problem Statement

Obstacle Representation

Similar to Trapezoidal Decomposition

- Represent with half-sphere- Project obstacles onto surface- Find criticality points- Draw arcs

Criteria• ω – Minimum spacing between arcs• N – Number of great circle arcs• K – Minimum free length of each arc

Path Planning

0 2ππGreat Circle Plane Angle

Free

Length

s1

s2

s3

s4

s5

s6

K

Criteria• ω – Minimum spacing between arcs• N – Number of great circle arcs• K – Minimum free length of each arc

Path Planning

0 2ππGreat Circle Plane Angle

s1

s2

s3

s4

s5

s6

K

ω ω ω

p1

p2

p3 p4

p6

Free

Length

Results

Manually Planned Automatically Planned

Non-Spherical Tumors

Approximated by multiple independent spherical targets

Plan for each spherical tumor is computed and executed independently.

• Takes advantage of structure/simplicity– Uses idea of criticality on obstacles vertices– Constrained to Vertical Great-Circle Arcs– Assumes independent spherical tumors– Plans for feasibility, not optimality

• Elegant, but not necessarily easiest– Actually samples 128 points and chooses the

best under constraints

Take Aways