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Related Experiment Video

Updated: Oct 18, 2025

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

Published on: May 20, 2014

Conic formulation of fluence map optimization problems.

S C M Ten Eikelder¹, A Ajdari², T Bortfeld²

¹Department of Econometrics and Operations Research, Tilburg University, The Netherlands.

Physics in Medicine and Biology

|September 29, 2021

Summary

This summary is machine-generated.

This study introduces conic representations for fluence map optimization (FMO) in radiation therapy, enabling polynomial-time solutions for complex treatment planning problems.

Keywords:

conic optimization convex optimization fluence map optimization intensity-modulated radiation therapy interior-point method

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Area of Science:

Medical Physics
Optimization Theory
Computational Science

Background:

Convexity of objectives and constraints in fluence map optimization (FMO) is well-studied.
Conic representation, a powerful optimization characteristic, has not been explored in FMO.

Purpose of the Study:

To introduce conic representations for FMO objectives and constraints.
To demonstrate that FMO problems with multiple biological criteria can be solved in polynomial time.

Main Methods:

Constructing conic representations for FMO objectives and constraints using quadratic, exponential, and power cones.
Developing conically representable approximations for fractionation-corrected functions.
Applying primal-dual interior-point algorithms for solving conically represented FMO problems.

Main Results:

Most FMO objectives and constraints were successfully represented conically.
The study demonstrated polynomial-time solvability for FMO problems with multiple biological evaluation criteria.
Numerical results on the TROTS dataset showed stable performance for FMO problems in conic form.

Conclusions:

Conic optimization offers a promising approach for solving complex FMO problems.
This work establishes the potential for efficient and optimal solutions in radiation therapy planning.
Future research in optimization algorithms may further enhance the speed and applicability of conic FMO.