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Session: Imaging General ePoster Viewing [Return to Session]

CT-Dose: Software Interface to Calculate Dose to Organ in Medical Imaging

S. Morato1,2,3*, R. Miro3, B. Juste3, S. Oliver3, J. Vijande1,2,4, F. Ballester1,4, G. Verdu3, A. Santos5, Matthew M. Mille6, (1) Departamento de Fisica Atomica, Molecular y Nuclear, Universitat de Valencia (UV), Burjassot, ES, (2) Instituto de Fisica Corpuscular, IFIC (UV/CSIC), Burjassot, ES, (3) Institute for Industrial, Radiophysical and Environmental Safety (ISIRYM), Universitat Politecnica de Valencia, Valencia, ES, (4) Unidad Mixta de Investigacion en Radiofisica e Instrumentacion Nuclear en Medicina (IRIMED), Instituto de Investigacion Sanitaria La Fe (IIS/La Fe)/Universitat de Valencia (UV), Valencia, ES, (5) Servicio De Radiofisica. Consorci Hospitalari Provincial De Castello, Castellon, ES. (6) Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, United States.


PO-GePV-I-71 (Sunday, 7/10/2022)   [Eastern Time (GMT-4)]

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Purpose: To quantify the radiation dose by organ received by the patient during the acquisition of CT images using a Monte Carlo method and GPU parallelization with the purpose of including dose data in the patient medical history.

Methods: The Rando phantom was prepared locating inside MOSFET dosemeters to obtain 3D absolute dose points, which are then compared with the values obtained by the MC-GPU simulation.The authors have developed a MATLAB interface, which performs the required simulations automatically from the CT images acquired, including the positions where the experimental values have been obtained. The MATLAB code developed, called CT-dose, automatically creates a MC-GPU voxelized model from the CT images taking into account the Hounsfield numbers. After that, considering the DICOM data the program creates also automatically the rest of the input needed in the MC-GPU simulation and it calls MC-GPU to simulate. Finally, CT-dose delivers a table with the dose data by organ and a 3D visualization of the dose distribution, which can be represented in the program interface or using the PARAVIEW software.

Results: The simulation results in the points where the MOSFET dosimeters were located inside the phantom shows good agreement with respect to experimental values obtained. Due to the punctual characterization of the dose, one could extrapolate that the dose distribution calculation should be accurate. However, more experimental validation with depth dose curves would be necessary for the final validation of the calculations.

Conclusion: This work presents an interface that could be useful to calculate the organ dose data of the patients after CT scans with an accurate Monte Carlo methodology. The parallelization of the MC-GPU code in graphical processors allows the possibility of using this methodology in real cases, which is interesting considering for example the EURATOM 2013/59 Directive.


CT, Dose, Monte Carlo


IM- CT: Monte Carlo, modeling

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