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Magnetic Resonance Imaging (MRI) is increasingly incorporated into treatmentplanning, because of its superior soft tissue contrast used for tumor and softtissue delineation versus computed tomography (CT). However, MRI cannotdirectly provide mass density or relative stopping power (RSP) maps requiredfor proton radiotherapy dose calculation. To demonstrate the feasibility ofMRI-only based mass density and RSP estimation using deep learning (DL) forproton radiotherapy. A DL-based framework was developed to discover underlyingvoxel-wise correlation between MR images and mass density and RSP. Five tissuesubstitute phantoms including skin, muscle, adipose, 45% hydroxyapatite (HA),and spongiosa bone were customized for MRI scanning based on materialcomposition information from ICRP reports. Two animal tissue phantoms made ofpig brain and liver were prepared for DL training. In the phantom study, two DLmodels were trained: one containing clinical T1 and T2 MRIs and anotherincorporating zero echo time (ZTE) MRIs as input. In the patient applicationstudy, two DL models were trained: one including T1 and T2 MRIs as input, andone incorporating synthetic dual-energy computed tomography (sDECT) images toprovide bone tissue information. In the phantom study, DL model based on T1 andT2 MRI demonstrated higher accuracy mass density and RSP estimation in skin,muscle, adipose, brain, and liver with mean absolute percentage errors (MAPE)of 0.42%, 0.14%, 0.19%, 0.78% and 0.26% for mass density and 0.30%, 0.11%,0.16%, 0.61% and 0.23% for RSP, respectively. DL model incorporating ZTE MRIimproved the accuracy of mass density and RSP estimation in 45% HA andspongiosa bone with MAPE at 0.23% and 0.09% for mass density and 0.19% and0.07% for RSP, respectively. Results show feasibility of MRI-only based massdensity and RSP estimation for proton therapy treatment planning using DLmethod.

MRI-based Material Mass Density and Relative Stopping Power Estimation via Deep Learning for Proton Therapy