A Molecular Dynamics Approach for Optimizing Beam Intensities in IMPT Treatment Planning

Gino J. Lim,Li Liao,Xiaodong Zhang

doi:10.4236/jamp.2019.79146

Abstract

This paper uses a molecular dynamics (MD) method for intensity-modulated proton therapy treatment planning optimization to overcome the problem of gradient-based optimization methods such as quasi-Newton, which is sensitive to starting conditions and is easily trapped in local minima. We implemented a molecular dynamics (MD) method and a quasi-Newton method for plan optimization. Three types of cancer cases, prostate, head-and-neck and lung cancer, were tested with three starting different initial conditions. Overall, the MD method consistently resulted in solutions with lower objective function values (OFVs) compared to those from the quasi-Newton algorithm. Furthermore, the MD method converged on the same OFV regardless of its initial starting points used for the prostate cancer case.

Highlights

Radiation therapy uses ionizing radiation to treat malignant tumors by damaging the DNA of cancer cells to block their ability to proliferate
This paper uses a molecular dynamics (MD) method for intensity-modulated proton therapy treatment planning optimization to overcome the problem of gradient-based optimization methods such as quasi-Newton, which is sensitive to starting conditions and is trapped in local minima
Our MD method was developed to overcome the local entrapment issue observed in many gradient-based algorithms that are extensively used in radiotherapy planning systems in clinics

Summary

Introduction

Radiation therapy uses ionizing radiation to treat malignant tumors by damaging the DNA of cancer cells to block their ability to proliferate. The purpose is to deliver a prescribed dosage of radiation to the targeted tumor while minimizing the dose deposition in healthy tissues [1]. As a new and advanced radiation treatment modality, proton therapy has rapidly gained interest [2] [3]. Once the dose deposition reaches a few millimeters beyond this peak, it falls sharply to zero (Figure 1).

Objectives

Methods

Results

Conclusion