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SU‐F‐J‐94: Development of a Plug‐in Based Image Analysis Tool for Integration Into Treatment Planning
Author(s) -
Owen D,
Anderson C,
Mayo C,
El Naqa I,
Ten Haken R,
Cao Y,
Balter J,
Matuszak M
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4956002
Subject(s) - computer science , context (archaeology) , histogram , image registration , radiation treatment planning , dicom , voxel , artificial intelligence , image processing , dosimetry , computer vision , data mining , image (mathematics) , nuclear medicine , radiation therapy , medicine , paleontology , biology
Purpose: To extend the functionality of a commercial treatment planning system (TPS) to support (i) direct use of quantitative image‐based metrics within treatment plan optimization and (ii) evaluation of dose‐functional volume relationships to assist in functional image adaptive radiotherapy. Methods: A script was written that interfaces with a commercial TPS via an Application Programming Interface (API). The script executes a program that performs dose‐functional volume analyses. Written in C#, the script reads the dose grid and correlates it with image data on a voxel‐by‐voxel basis through API extensions that can access registration transforms. A user interface was designed through WinForms to input parameters and display results. To test the performance of this program, image‐ and dose‐based metrics computed from perfusion SPECT images aligned to the treatment planning CT were generated, validated, and compared. Results: The integration of image analysis information was successfully implemented as a plug‐in to a commercial TPS. Perfusion SPECT images were used to validate the calculation and display of image‐based metrics as well as dose‐intensity metrics and histograms for defined structures on the treatment planning CT. Various biological dose correction models, custom image‐based metrics, dose‐intensity computations, and dose‐intensity histograms were applied to analyze the image‐dose profile. Conclusion: It is possible to add image analysis features to commercial TPSs through custom scripting applications. A tool was developed to enable the evaluation of image‐intensity‐based metrics in the context of functional targeting and avoidance. In addition to providing dose‐intensity metrics and histograms that can be easily extracted from a plan database and correlated with outcomes, the system can also be extended to a plug‐in optimization system, which can directly use the computed metrics for optimization of post‐treatment tumor or normal tissue response models. Supported by NIH ‐ P01 ‐ CA059827