MENU

Click here to

×

Are you sure ?

Yes, do it No, cancel

Spectral CT Based Elemental Composition Assignment to Increase the Accuracy of Monte Carlo Simulation in Proton Beam Therapy

V Moskvin*, J Uh, C Hua, St. Jude Childrens Research Hospital, Memphis, TN

Presentations

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

Room: AAPM ePoster Library

Purpose: Monte Carlo (MC) simulation of radiation transport in patient geometry currently utilizes the Schneider’s algorithm (SA) (Schneider et al. PMB 2000) for converting CT number to mass density and elemental weights. However, tissues with different radiobiological properties are often represented with the same elemental composition. The purpose of this work is to improve elemental composition assignment with better tissue segmentation in Monte Carlo transport schemes for proton therapy by utilizing the pixel-by-pixel electron density (ED) and effective atomic number (Zeff) generated by spectral CT.

Methods: Three-dimensional ED and Zeff maps were retrospectively reconstructed from patient brain scans acquired on a clinical detector-based spectral CT system. An algorithm for segmenting white matter (WM) and gray matter (GM) in the brain were formulated based on Zeff-ED pairs. The SA represents WM and GM with the same elemental composition because both fall in the same HU interval of 18-80. The beam axis was defined in the treatment plans, and the proton beam passage for both segmentation algorithms was simulated with FLUKA MC code.

Results: Scatter plots of Zeff vs. ED and Zeff vs. HU show different distributions of WM and GM with well-separated cluster centers. Simulations of a spot scanning beam for SA and Zeff-ED based segmentation shows a difference in the depth dose distributions and range. The Schneider algorithm underestimated the energy loss that may result in a larger proton range and lower dose at the spot position.

Conclusion: The Zeff-ED tissue segmentation for elemental composition assignment may increase the accuracy of the MC simulation in proton therapy. The impact of reducing the uncertainty will be important for treating tumors close to critical organs. The accurate tissue segmentation for GM and WM will benefit the MC based radiobiological response modeling.

Download ePoster [PDF]

Funding Support, Disclosures, and Conflict of Interest: Philips research grant support.

Keywords

Tissue Composition, Dual-energy Imaging, Monte Carlo

Taxonomy

TH- External Beam- Particle/high LET therapy: Proton therapy – computational dosimetry-Monte Carlo

Contact Email