LAUSR

BACKGROUND In-situ range verification of particle therapy based on the detection of secondary emitted radiation requires highly accurate assignment of elemental concentrations (particularly carbon and oxygen) in the human body. PURPOSE A method for quantitatively predicting carbon and oxygen concentrations in human soft tissues is proposed. This method relies on an empirical one-to-one correspondence between the mass fraction and water content (WC), which is a measurable tissue quantity based on magnetic resonance (MR) imaging (referred to as "MRWC-based method"). METHODS A numerical analysis of the MRWC-based method was performed for 47 standard human soft tissues tabulated in the literature as objects of interest with unknown mass fractions of the four main elements-C, O, H, and N. Thereafter, the method was evaluated in terms of the mass-fraction quantification accuracy by comparing it with the gold-standard CT-based method developed by Schneider et al. The MRWC-based method was also applied to the MR imaging data of a virtual head phantom obtained from a three-dimensional MRI simulated brain database. RESULTS The predicted mass fractions in a range of human soft tissues were in better agreement with the reference values than those predicted by the CT-based method. The mean absolute errors of the predicted mass% values for the overall standard soft tissues could be reduced from 4.8 percentage points (pp) (CT-based) to 0.5 pp (MRWC-based) for carbon and from 5.2 pp (CT-based) to 0.4 pp (MRWC-based) for oxygen. The application to the simulated MRI data confirmed the capability of the sufficient recognition of the boundaries between the white matter and gray matter in the brain that could not be realized by the CT-based method. Thus, the MRWC-based method exhibits superior performance in the prediction of carbon and oxygen concentrations in soft tissues. CONCLUSIONS This study is limited to a proof-of-concept scope but demonstrates the feasibility of the MRWC-based method for the generation of elemental images of human soft tissues from MRI-derived water-content images. This article is protected by copyright. All rights reserved.