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Isodose feature-preserving voxelization (IFPV) for radiation therapy treatment planning.

Authors
  • Liu, Hongcheng1
  • Xing, Lei2
  • 1 Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32611-6595, USA.
  • 2 Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, 94305-5847, USA.
Type
Published Article
Journal
Medical physics
Publication Date
Jul 01, 2018
Volume
45
Issue
7
Pages
3321–3329
Identifiers
DOI: 10.1002/mp.12977
PMID: 29772065
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Inverse planning involves iterative optimization of a large number of parameters and is known to be a labor-intensive procedure. To reduce the scale of computation and improve characterization of isodose plan, this paper presents an isodose feature-preserving voxelization (IFPV) framework for radiation therapy applications and demonstrates an implementation of inverse planning in the IFPV domain. A dose distribution in IFPV scheme is characterized by partitioning the voxels into subgroups according to their geometric and dosimetric values. Computationally, the isodose feature-preserving (IFP) clustering combines the conventional voxels that are spatially and dosimetrically close into physically meaningful clusters. A K-means algorithm and support vector machine (SVM) runs sequentially to group the voxels into IFP clusters. The former generates initial clusters according to the geometric and dosimetric information of the voxels and SVM is invoked to improve the connectivity of the IFP clusters. To illustrate the utility of the formalism, an inverse planning framework in the IFPV domain is implemented, and the resultant plans of three prostate IMRT and one head-and-neck cases are compared quantitatively with that obtained using conventional inverse planning technique. The IFPV generates models with significant dimensionality reduction without compromising the spatial resolution seen in traditional downsampling schemes. The implementation of inverse planning in IFPV domain is demonstrated. In addition to the improved computational efficiency, it is found that, for the cases studied here, the IFPV-domain inverse planning yields better treatment plans than that of DVH-based planning, primarily because of more effective use of both geometric and dose information of the system during plan optimization. The proposed IFPV provides a low parametric representation of isodose plan without compromising the essential characteristics of the plan, thus providing a practically valuable framework for various applications in radiation therapy. © 2018 American Association of Physicists in Medicine.

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