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SU‐E‐T‐284: Conversion of Computational Phantom to DICOM CT Images to Be Used in a Treatment Planning System for Epidemiologic Dose Reconstruction Studies
Author(s) -
Lamart S,
Lee C,
Lee C
Publication year - 2013
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 - 10.1118/1.4814718
Subject(s) - dicom , imaging phantom , hounsfield scale , radiation treatment planning , computer science , nuclear medicine , eclipse , segmentation , scanner , medical imaging , computer vision , artificial intelligence , medicine , radiation therapy , computed tomography , physics , radiology , astronomy
Purpose: To develop and verify a novel method of converting surface‐based deformable hybrid computational phantom into DICOM CT images, which can be readily imported into a treatment planning system (TPS). We intended to use the technique to reconstruct doses for organs located within or near to the treatment field to support epidemiologic study of late effect in patients who received radiotherapy many years ago without volumetric imaging. Methods: We generated 352 axial images from an adult hybrid computational phantom with the in‐plane resolution of 1 mm2 and 5 mm slice thickness. All of the 78 different tissue densities of the phantom were converted into Hounsfield Units (HU) using the CT‐to‐density conversion table. In‐house MATLAB code was written to generate DICOM format images with header from the matrix of HU. We imported the generated images into Eclipse TPS (Varian, Palo Alto, CA), verified the conversion process by comparing organ volumes and densities between the original phantom and the imported images, and illustrated a simulation of a seven‐field 3D conformal prostate treatment. Results: Between the original phantom and its CT images imported in Eclipse, there were less than 2% difference in volumes and densities after automatic segmentation of the selected organs. Illustrative three‐dimensional dose distribution for prostate tumor volume and normal tissues were calculated from the TPS using the converted CT numbers. Conclusion: To reconstruct normal tissue doses for late effects studies, we established a technique to convert hybrid computational phantoms to DICOM CT images compatible with a commercial TPS. With the flexibility of the hybrid phantom that can be deformed to match patient size, our new dose reconstruction method will enable us to estimate dose distribution for the patients whose volumetric images are not available.