Sparse deconvolution of proton radiography data to estimate water equivalent thickness maps.

Purpose: In proton therapy, the conversion of the planning computed tomography (CT) into proton stopping powers is tainted by uncertainties which may jeopardize dose conformity. Proton radiography provides a direct information on the energy reduction of protons in the patient. However, it is currently limited by the degradation ("blurring") of the one-dimensional depth-dose deposition profiles which constitute the pixels.

Methods: An iterative algorithm is implemented to extract high-resolution water equivalent thickness (WET) maps from the measurements of depth-dose profiles acquired with a multilayer ionization chamber. The method relies on the assumption that those curves are a function of the WET, which can benefit from a sparse representation.

Results: When used without relying on any prior knowledge derived from the planning CT, the method already outperforms the published one in terms of accuracy. We also propose a variant which integrates the planning CT in a robust fashion to further improve the deconvolution result and reach an accuracy of 1.5 mm on the estimated WET. The methods are applied to both synthetic data and actual proton radiography acquisitions on phantoms.

Conclusions: Besides the increase in accuracy achieved in the estimation of WET maps from proton radiography data, we demonstrate that the proposed deconvolution algorithm is also more robust with respect to confounding factors such as residual setup errors or changes in the anatomy. Therefore, proton radiography using a range probe provides both the required accuracy to assess and reduce range uncertainty in proton therapy and the simplicity of integrated-mode proton radiography.