Consensus guide on CT-based prediction of stopping-power ratio using a Hounsfield look-up table for proton therapy - Peters, Nils; Trier Taasti, Vicki; Ackermann, Benjamin; Bolsi, Alessandra; Vallhagen Dahlgren, Christina; Ellerbrock, Malte; Fracchiolla, Francesco; Gomà, Carles; Góra, Joanna; Cambraia Lopes, Patricia; Rinaldi, Ilaria; Salvo, Koen; et al. (Radiotherapy and oncology, 2023)
Background and purpose - Studies have shown large variations in stopping-power ratio (SPR) prediction from computed tomography (CT) across European proton centres. To standardise this process, a step-by-step guide on specifying a Hounsfield look-up table (HLUT) is presented here. - Materials and methods - The HLUT specification process is divided into six steps: Phantom setup, CT acquisition, CT number extraction, SPR determination, HLUT specification, and HLUT validation. Appropriate CT phantoms have a head- and body-sized part, with tissue-equivalent inserts in regard to X-ray and proton interactions. CT numbers are extracted from a region-of-interest covering the inner 70% of each insert in-plane and several axial CT slices in scan direction. For optimal HLUT specification, the SPR of phantom inserts is measured in a proton beam and the SPR of tabulated human tissues is computed stoichiometrically at 100 MeV. Including both phantom inserts and tabulated human tissues increases HLUT stability. Piecewise linear regressions are performed between CT numbers and SPRs for four tissue groups (lung, adipose, soft tissue, and bone) and then connected with straight lines. Finally, a thorough but simple validation is performed. - Results - The best practices and individual challenges are explained comprehensively for each step. A well-defined strategy for specifying the connection points between the individual line segments of the HLUT is presented. The guide was tested exemplarily on three CT scanners from different vendors, proving its feasibility. - Conclusion - The presented step-by-step guide for CT-based HLUT specification with recommendations and examples can contribute to reduce inter-centre variations in SPR prediction.