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New Optimization Technique for Radiation Therapy Planning by Daniel Riofrio (Dissertation Defense)

April 8, 2015

Dissertation Defense Announcement
Daniel Riofrio

Date: April 8, 2015
Place: ECE 118
Time:  11:00 a.m. 

Committee Members: 
Shuang Luan - Chair - Department of Computer Science, UNM 
Trilce Estrada - Department of Computer Science, UNM 
Michael Holzscheiter - Department of Physics and Astronomy, UNM 
Adam Hecht - Department of Nuclear Engineering, UNM 

New Optimization Technique for Radiation Therapy Planning


Daniel Andrés Riofrío Almeida

M.S., Computer Science, University of New Mexico, Albuquerque, NM., 2012.

B.S., Software and Systems Engineering, Escuela Politécnica Nacional, Quito, Ecuador, 2006.


The main goal of radiation therapy is to deliver a lethal dose radiation to the targeted tumor while minimizing the radiation dose to the surrounding normal tissues and critical organs. Modern cancer therapy have benefited enormously from computer controlled treatment devices with increased precision and capability. However, this increased sophistication also creates new challenges for treatment planning. As the number of parameters in a treatment plan increases, the traditional computational approaches are no longer adequate to fully exploit the potential of the latest treatment devices. This is because at the heart of treatment planning is often a set of substantially non-trivial constrained geometric optimization problems.

In this dissertation, we present a new optimization framework combining Particle Swarm Optimization (PSO) with numerical optimization (e.g., least distance programming, non-negative least-square optimization, etc.). For our new PSO framework, we moved away from the classical view of a particle representing a potential solution of the optimization function; instead, we use the whole particle distribution as the solution. We modeled tumors, critical organs and other tissues as geometric volumes, whose surfaces have an associated potential function. The radiation source is modeled as kinetic particles subject to the forces from the potential functions from both the particles and the various geometric objects. The final configuration of the swarm of particles including their trajectories is the treatment plan.

To demonstrate the potential of our new optimization paradigm, we have applied it to Gamma Knife ® radiosurgery and High Dose Rate Brachytherapy (HDR) for prostate cancer. Mathematically, Gamma Knife radiosurgery is a ball-packing process whose goal is to “pack” some spherical high dose volumes into a tumor volume, while brachytherapy is to find the trajectories of some spherical high dose volumes. Both problems are computationally intractable. There is no known algorithm for brachytherapy that can generate the trajectories, which is currently done manually by a physician. The new framework models the spherical high dose volume as kinetic particles and simulates the “swarm” of these particles through a potential field created based on medical constraints and prescriptions. The resulting stable swarm, further refined by least distance programming, is the final treatment plan. Our experiments with real and simulated clinical data have shown that the new framework significantly outperforms current clinical systems.