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