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Various studies have shown that multi\u2010energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age\u2010related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection\u2010based MECT systems for SPR prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To present and evaluate a novel proton SPR prediction method based on MD images from dual\u2010energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non\u2010tissue materials, by minimizing the root\u2010mean\u2010square relative attenuation error in the energy interval from 40 to 140\u00a0keV. A model (here called MD\u2010SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data\u2010fitting. The goodness of fit of the MD\u2010SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head\u2010 and an abdomen\u2010sized phantom setup, were acquired with a clinical projection\u2010based fast\u2010kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non\u2010tissue materials in the phantoms, as well as used for input to the MD\u2010SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image\u2010based consecutive scan DECT for the same phantom setups.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The predicted SPRs of the tissue surrogates were similar for MD\u2010SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non\u2010adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between \u22120.7% and \u22121.8%. The SPRs of the non\u2010tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD\u2010SPR images, compared to 6.8% for DirectSPR.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The MD\u2010SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non\u2010tissue materials.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16929","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T20:04:02Z","timestamp":1704830642000},"page":"881-897","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of proton stopping power ratios using dual\u2010energy CT basis material decomposition"],"prefix":"10.1002","volume":"51","author":[{"given":"Erik","family":"Pettersson","sequence":"first","affiliation":[{"name":"Department of Medical Radiation Sciences Institute of Clinical Sciences Sahlgrenska Academy University of Gothenburg  Gothenburg Sweden"},{"name":"Therapeutic Radiation Physics Department of Medical Physics and Biomedical Engineering Sahlgrenska University Hospital  Gothenburg 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