  Magnetic Resonance Imaging (MRI) is increasingly incorporated into treatment
planning, because of its superior soft tissue contrast used for tumor and soft
tissue delineation versus computed tomography (CT). However, MRI cannot
directly provide mass density or relative stopping power (RSP) maps required
for proton radiotherapy dose calculation. To demonstrate the feasibility of
MRI-only based mass density and RSP estimation using deep learning (DL) for
proton radiotherapy. A DL-based framework was developed to discover underlying
voxel-wise correlation between MR images and mass density and RSP. Five tissue
substitute phantoms including skin, muscle, adipose, 45% hydroxyapatite (HA),
and spongiosa bone were customized for MRI scanning based on material
composition information from ICRP reports. Two animal tissue phantoms made of
pig brain and liver were prepared for DL training. In the phantom study, two DL
models were trained: one containing clinical T1 and T2 MRIs and another
incorporating zero echo time (ZTE) MRIs as input. In the patient application
study, two DL models were trained: one including T1 and T2 MRIs as input, and
one incorporating synthetic dual-energy computed tomography (sDECT) images to
provide bone tissue information. In the phantom study, DL model based on T1 and
T2 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 MRI
improved the accuracy of mass density and RSP estimation in 45% HA and
spongiosa bone with MAPE at 0.23% and 0.09% for mass density and 0.19% and
0.07% for RSP, respectively. Results show feasibility of MRI-only based mass
density and RSP estimation for proton therapy treatment planning using DL
method.
