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

Abstract

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.

openPR — A computational tool for CT conversion assessment with proton radiography

“…14 This method is, however, not optimal because it does not use the fact that a measured IDD contains information on the WEPL not only at the spot position but also in its vicinity. In a recent publication, 13 we developed a deconvolution algorithm which takes advantage of this phenomenon called range mixing, to estimate the WEPL map with a higher resolution than that of the proton radiograph. In other words, this method estimates not only the WEPL at the center of the beamlets but also between them.…”

“…Although the tools mentioned above have been evaluated in their respective publications, 10,13,16 they have limitations related to the imaged object. All these methods are based on a model according to which the proton beamlets propagate in straight lines, parallel to each other, with negligible scattering.…”

“…12 However, deconvolution techniques can be used to increase the spatial resolution and obtain a satisfactory image to assess the range prediction. 13 In a recent publication, Meijers et al used a proton radiography technique relying on a multilayer ionization chamber (MLIC) to validate their site-specific HU-RSP conversion, with measurements performed on animal samples. 14 Their results showed that the classic margin rule (1 mm + 2.4% when using Monte Carlo.…”

“…Previous work had already warned about the extreme sensitivity of range error estimation with respect to small residual misalignment and approximations in IDD modeling in the presence of lateral heterogeneities. [10][11][12][13] Second, the workflow relies on simulations carried out by the TPS to estimate the range error. This can be useful to take into account in the range uncertainty assessment, the uncertainty coming from the modeling carried out by the TPS.…”

“…Nonetheless, this comes at the cost of signal degradations resulting from the presence of lateral inhomogeneties along the path of the beam and, to a lesser extent, from multiple Coulomb scattering (MCS) 12 . However, deconvolution techniques can be used to increase the spatial resolution and obtain a satisfactory image to assess the range prediction 13 …”