Poster or Presentation Title
Location
Math Study Area
Advisor(s)
Steve Laub
Adit Panchal
Start Date
30-6-2018 2:40 PM
End Date
30-6-2018 2:51 PM
Abstract
Abstract
Title: Classification of IMPT Plans
Purpose:
Create a method to aid physicists and dosimetrists in the classification of Intensity Modulated Proton Therapy treatment plans.
Method:
A Python program was developed to read structure sets and dose grids from radiation treatment plans. The program outputs field-specific histograms of the number of voxels within the planning target volume that received dose and normalizes each histogram to the percent volume of the target that received each field’s contribution of the total dose. It then determines the number of Gaussian distributions in each histogram, which serves as an initial classification metric, and calculates additional metrics, including the width and maximum dose in each distribution, and the rate at which the number of voxels decrease per change in dose. 49 patients were analyzed, and compared to data from a standard Single-Field Uniform Dose (SFUD) treatment plan and assessed for similarity.
Results:
The program can classify hybrid treatment plans by assigning them a score based on calculated metrics that represent their correlation with a standard SFUD treatment plan.
Conclusions:
The program and its outputs can aid in improved classification and differentiation of hybrid treatment plans to facilitate the precision of patient and target volume positioning.
Classification of Intensity-Modulated Proton Therapy Plans
Math Study Area
Abstract
Title: Classification of IMPT Plans
Purpose:
Create a method to aid physicists and dosimetrists in the classification of Intensity Modulated Proton Therapy treatment plans.
Method:
A Python program was developed to read structure sets and dose grids from radiation treatment plans. The program outputs field-specific histograms of the number of voxels within the planning target volume that received dose and normalizes each histogram to the percent volume of the target that received each field’s contribution of the total dose. It then determines the number of Gaussian distributions in each histogram, which serves as an initial classification metric, and calculates additional metrics, including the width and maximum dose in each distribution, and the rate at which the number of voxels decrease per change in dose. 49 patients were analyzed, and compared to data from a standard Single-Field Uniform Dose (SFUD) treatment plan and assessed for similarity.
Results:
The program can classify hybrid treatment plans by assigning them a score based on calculated metrics that represent their correlation with a standard SFUD treatment plan.
Conclusions:
The program and its outputs can aid in improved classification and differentiation of hybrid treatment plans to facilitate the precision of patient and target volume positioning.