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Modeling of Human Body Tissue Compositions for Monte Carlo Algorithm of Proton Therapy Dose Computation with the Single Energy Computed Tomography Calibration Curve

M Ghasemi Ghonchehnazi1*, G Evans2, C Shang3, (1) Florida Atlantic University, Boca Raton, FL, (2) South Florida Proton Therapy Institute, Delray Beach, FL, (3) South Florida Proton Therapy Institute, Delray Beach, FL

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: dose computation with most planning systems rely on single energy computed tomography (CT) images in which the relative proton stopping-power ratio, mass density and relative electron density are derived from CT Hounsfield units (HU). Using a proton Monte Carlo dose calculation algorithm in treatment planning system, a CT number of each pixel in patient CT image is assigned to mass density, in order to compute the proton stopping power ratio. The accuracy of proton dose computation in Monte Carlo algorithm relies on conversion from HU to mass density. We explore the potential improvement in determining mass density to reduce the uncertainty in predicting the proton range in patients.


Methods: Stoichiometric method is used to calculate the CT scanner specific parameters related to the photoelectric effect, coherent scattering, and Compton interactions, in order to model the CT number of human body tissue compositions. Also, the mass densities of human body tissues were derived from the conversion curve of the measured HU to the mass density of tissue substitutes (a direct calibration approach). Since low-density (lung) tissues, soft tissues and high-density (bone) tissues have showed different uncertainties through the calibration procedure, we estimated their uncertainties individually.


Results: inherent uncertainty (rms error) in soft tissues using the direct calibration method was estimated 1.31%, while the inherent uncertainty using the stoichiometric method was estimated 48%. Therefore, in this work the inherent uncertainty of the mass densities prediction in soft tissues was reduced by a factor of ~2.7. The total inherent uncertainty for lung, soft and bone tissues was reduced by a factor of ~1.5.


Conclusion: results demonstrate that a more accurate prediction of HU to mass density can be achieved by the stoichiometric calibration curve for the Monte Carlo dose calculation algorithm in treatment planning system.

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