A column generation-based algorithm for Volumetric Modulated Arc Therapy (VMAT) treatment plan optimization
| Speaker: | Marina Epelman |
|---|---|
| Affiliation: | University of Michigan |
| Room: | Mathematics & Computer Building (MC) 5158 |
Abstract:
External beam radiation therapy is a common treatment for many types of cancer. During such treatment, radiation is delivered with a gantry, equipped with a radiation source, that is pointed at the patient from various angles. Optimization models are commonly used in individualized treatment planning, and formulation and solution methods for such models have been an area of active research and collaborations. VMAT is a particular technique for delivering radiation, in which the gantry continuously rotates around the patient while the leaves of a multi-leaf collimator (MLC) move in and out of the radiation field to "shape" it. Proposed over a decade ago, this technique has the potential to produce treatments of high quality similar to, e.g., Intensity Modulated Radiation Therapy (IMRT), but requiring less time for treatment delivery. Recently, treatment systems capable of delivering VMAT treatments became commercially available, necessitating the development of relevant treatment planning methods. We propose one such method, which uses optimization models and column generation-based heuristics to produce high-quality VMAT treatment plans that allow for (i) dynamically adjustable gantry speed; (ii) dynamically adjustable dose rate; and (iii) MLC leaf speed constraints.
This talk is based on joint work with Fei Peng and H. Edwin Romeijn at the University of Michigan and Xun Jia, Xuejun Gu and Steve B. Jiang at the University of California San Diego.
Speaker
bio:
Marina
Epelman
received
her
PhD
in
Operations
Research
from
Massachusetts
Institute
of
Technology
(MIT)
in
1999.
The
same
year
she
joined
the
Industrial
and
Operations
Engineering
department
at
the
University
of
Michigan,
where
she
is
now
an
associate
professor.
Her
research
interests
lie
in
developing
optimization
models
and
algorithms
for
problems
arising
in
oncological
treatment
planning,
transportation,
staffing,
manufacturing
and
other
applications,
solved
by
techniques
of
convex
optimization,
sampling-based
algorithms,
dynamic
programming
and
stochastic
optimization.