Extension of the open-source TIGRE toolbox for proton imaging.

Proton irradiation is a well-established method to treat deep-seated tumors in radio oncology. Usually, an X-ray computed tomography (CT) scan is used for treatment planning. Since proton therapy is based on the precise knowledge of the stopping power describing the energy loss of protons in the patient tissues, the Hounsfield units of the planning CT have to be converted.

Feasibility study of a proton CT system based on 4D-tracking and residual energy determination via time-of-flight.

For dose calculations in ion beam therapy, it is vital to accurately determine the relative stopping power (RSP) distribution within the treatment volume. A suitable imaging modality to achieve the required RSP accuracy is proton computed tomography (pCT), which usually uses a tracking system and a separate residual energy (or range) detector to directly measure the RSP distribution. This work investigates the potential of a novel pCT system based on a single detector technology, namely low gain avalanche detectors (LGADs).

Calculating 1/βp for most likely path estimates for protons and helium ions using an analytical model.

In ion computed tomography, limited spatial resolution can be related to the non-straight path of ions resulting from multiple Coulomb scattering in the object to be imaged. By including sophisticated path estimates such as most likely path (MLP) or optimized cubic spline into the image reconstruction algorithm, the achieved spatial resolution can be substantially improved compared to assuming a simple straight line path only. The typically used implementation of the MLP is a matrix-based approach employing Bayesian statistics and modelling multiple Coulomb scattering as Gaussian distribution.

First application of the GPU-based software framework TIGRE for proton CT image reconstruction.

In proton therapy, the knowledge of the proton stopping power, i.e. the energy deposition per unit length within human tissue, is essential for accurate treatment planning.