Water Equivalent Thickness Estimation Via Sparse Deconvolution of Proton Radiography Data

Proton radiography using a multilayer ionization chamber can potentially be used for assessing the quality of the stopping power computation in proton therapy. However, the finite proton beam profile leads to a degradation of the depth-dose curves (‘blurring’) measured by the range probe, which makes the estimation of the integrated proton stopping power a complex task. Existing methods aiming at determining a map of the integrated proton stopping power currently involve the use of the planning x-ray computed tomography (CT) as a priori knowledge. Consequently, such methods are very sensitive to small misalignment between the planning CT and the proton radiography acquisitions, to errors in the stopping power computation and to changes in the anatomy of the patient. In this paper, we develop an algorithm based on a sparsity assumption that estimates the integrated proton stopping power map of an anthropomorphic phantom from proton radiography data without using any prior information from the CT