Abstract An important source of uncertainty in proton therapy treatment planning is the assignment of stopping‐power ratio (SPR) from CT data. A commercial product is now available that creates an… Click to show full abstract
Abstract An important source of uncertainty in proton therapy treatment planning is the assignment of stopping‐power ratio (SPR) from CT data. A commercial product is now available that creates an SPR map directly from dual‐energy CT (DECT). This paper investigates the use of this new product in proton treatment planning and compares the results to the current method of assigning SPR based on a single‐energy CT (SECT). Two tissue surrogate phantoms were CT scanned using both techniques. The SPRs derived from single‐energy CT and by DirectSPR™ were compared to measured values. SECT‐based values agreed with measurements within 4% except for low density lung and high density bone, which differed by 13% and 8%, respectively. DirectSPR™ values were within 2% of measured values for all tissues studied. Both methods were also applied to scanned containers of three types of animal tissue, and the expected range of protons of two different energies was calculated in the treatment planning system and compared to the range measured using a multi‐layer ion chamber. The average difference between range measurements and calculations based on SPR maps from dual‐ and single‐energy CT, respectively, was 0.1 mm (0.07%) versus 2.2 mm (1.5%). Finally, a phantom was created using a layer of various tissue surrogate plugs on top of a 2D ion chamber array. Dose measurements on this array were compared to predictions using both single‐ and dual‐energy CTs and SPR maps. While standard gamma pass rates for predictions based on DECT‐derived SPR maps were slightly higher than those based on single‐energy CT, the differences were generally modest for this measurement setup. This study showed that SPR maps created by the commercial product from dual‐energy CT can successfully be used in RayStation to generate proton dose distributions and that these predictions agree well with measurements.
               
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