This work provides a quantitative assessment of helium ion CT (HeCT) for particle therapy treatment planning. For the first time, HeCT based range prediction accuracy in a heterogeneous tissue phantom is presented and compared to single-energy x-ray CT (SECT), dual-energy x-ray CT (DECT) and proton CT (pCT). HeCT and pCT scans were acquired using the US pCT collaboration prototype particle CT scanner at the Heidelberg Ion-Beam Therapy Center. SECT and DECT scans were done with a Siemens Somatom Definition Flash and converted to RSP. A Catphan CTP404 module was used to study the RSP accuracy of HeCT. A custom phantom of 20 cm diameter containing several tissue equivalent plastic cubes was used to assess the spatial resolution of HeCT and compare it to DECT. A clinically realistic heterogeneous tissue phantom was constructed using cranial slices from a pig head placed inside a cylindrical phantom (ø150 mm). A proton beam (84.67 mm range) depth-dose measurement was acquired using a stack of GafchromicTM EBT-XD films in a central dosimetry insert in the phantom. CT scans of the phantom were acquired with each modality, and proton depth-dose estimates were simulated based on the reconstructions. The RSP accuracy of HeCT for the plastic phantom was found to be 0.3 ± 0.1%. The spatial resolution for HeCT of the cube phantom was 5.9 ± 0.4 lp cm−1 for central, and 7.6 ± 0.8 lp cm−1 for peripheral cubes, comparable to DECT spatial resolution (7.7 ± 0.3 lp cm−1 and 7.4 ± 0.2 lp cm−1, respectively). For the pig head, HeCT, SECT, DECT and pCT predicted range accuracy was 0.25%, −1.40%, −0.45% and 0.39%, respectively. In this study, HeCT acquired with a prototype system showed potential for particle therapy treatment planning, offering RSP accuracy, spatial resolution, and range prediction accuracy comparable to that achieved with a commercial DECT scanner. Still, technical improvements of HeCT are needed to enable clinical implementation.

Purpose: The relative stopping power (RSP) uncertainty is the largest contributor to the range uncertainty in proton therapy. The purpose of this work is to develop a robust and systematic method that yields accurate patient specific RSPs by combining pre‐treatment X‐ray CT and daily proton radiography. Methods: The method is formulated as a penalized least squares optimization (PLSO) problem min(|Ax‐B|). The matrix A represents the cumulative path‐length crossed in each material computed by calculating proton trajectories through the X‐ray CT. The material RSPs are denoted by x and B is the pRad, expressed as water equivalent thickness. The equation is solved using a convex‐conic optimizer. Geant4 simulations were made to assess the feasibility of the method. RSP extracted from the Geant4 materials were used as a reference and the clinical HU‐RSP curve as a comparison. The PLSO was first tested on a Gammex RMI‐467 phantom. Then, anthropomorphic phantoms of the head, pelvis and lung were studied and resulting RSPs were evaluated. A pencil beam was generated in each phantom to evaluate the proton range accuracy achievable by using the optimized RSPs. Finally, experimental data of a pediatric head phantom (CIRS) were acquired using a recently completed experimental pCT scanner. Results: Numerical simulations showed precise RSP (<0.75%) for Gammex materials except low‐density lung (LN‐300) (1.2%). Accurate RSP have been obtained for the head (µ=−0.10%, 1.5σ=1.12%), lung (µ=−0.33, 1.5σ=1.02%) and pelvis anthropomorphic phantoms (µ=0.12, 1.5σ=0,99%). The range precision has been improved with an average R80 difference to the reference (µ±1.5σ) of −0.20±0.35%, −0.47±0.92% and −0.06±0.17% in the head, lung and pelvis phantoms respectively, compared to the 3.5% clinical margin. Experimental HU‐RSP curve have been produced on the CIRS pediatric head. Conclusion: The proposed PLSO with prior knowledge X‐ray CT shows promising potential (R80 σ<1.0% over all sites) to decrease the range uncertainty.

Purpose: To develop an accurate phenomenological model of the cubic spline trajectory (CST) estimate of the proton path, accounting for the initial proton energy and water equivalent thickness (WET) traversed. Methods: Geant4 Monte Carlo (MC) simulations were used to calculate the path of protons crossing various slabs (5–30 cm WET) of different material (LN300, water and CB2–50% CaCO3) for a range of initial energies (150–330MeV). For each MC trajectory, CST was constructed based on the proton entrance and exit information and compared with the MC using the root mean square (RMS) metric. The CST path is dependent on the direction vector magnitudes (|P0,1|). First, |P0,1| is set to the proton path length. Then, a factor Λ is introduced to modify |P0,1|. The factor is varied to minimize the RMS with MC paths for every configuration. Finally, a set of Λopt factors that minimizes the RMS is presented. These are dependent on the ratio between WET and water equivalent path length (WEPL). The resolution along the path is investigated with a set of slabs. MTF analysis is performed on proton radiographs of a line‐pair phantom reconstructed using the CST trajectories (Λopt and Λ1). Results: Λopt was fitted to the ratio of WET/WEPL using a power function (Y=1‐AXB where A=0.36, B=4.07). The RMS deviation calculated along the path, between the CST and the MC path, increases with the WET. The increase is larger when using Λ1 than Λopt (difference of 5.0% with WET/WEPL=0.86). For 230(330) MeV protons, the MTF10% was found to increase by 40%(6%) respectively for a thick phantom (30cm) and by 25%(1%) for thinner phantom (25cm) when using the Λopt model compared to the Λ1 model. Conclusion: Based on these results, using CST with the Λopt factor reduces the RMS deviation and increases the spatial resolution when reconstructing proton trajectories.