Estimating the modulating effect of lung tissue in particle therapy using a clinical CT voxel histogram analysis

Abstract

To treat lung tumours with particle therapy, different additional tasks and challenges in treatment planning and application have to be addressed thoroughly. One of these tasks is the quantification and consideration of the Bragg peak (BP) degradation due to lung tissue: as lung is an heterogeneous tissue, the BP is broadened when particles traverse the microscopic alveoli. These are not fully resolved in clinical CT images and thus, the effect is not considered in the dose calculation. In this work, a correlation between the CT histograms of heterogeneous material and the impact on the BP curve is presented. Different inorganic materials were scanned with a conventional CT scanner and additionally, the BP degradation was measured in a proton beam and was then quantified. A model is proposed that allows an estimation of the modulation power by performing a histogram analysis on the CT scan. To validate the model for organic samples, a second measurement series was performed with frozen porcine lunge samples. This allows to investigate the possible limits of the proposed model in a set-up closer to clinical conditions. For lung substitutes, the agreement between model and measurement is within ±0.05 mm and for the organic lung samples, within ±0.15 mm. This work presents a novel, simple and efficient method to estimate if and how much a material or a distinct region (within the lung) is degrading the BP on the basis of a common clinical CT image. Up until now, only a direct in-beam measurement of the region or material of interest could answer this question.

Experimental determination of modulation power of lung tissue for particle therapy

“…In that case, calculating a total modulation power cannot reproduce the doublepeak structure that can clearly be seen in the depth dose distribution, though, however, is clinically spoken more accurate than any other approach presented so far. The most accurate solution might be to use the model presented by Flatten et al (2021) to calculate a locally resolved modulation power. Using that model, the lung can be divided into ROIs, each having a specific modulation power (corresponding to the fit with one normal distribution).…”