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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2210.05804 |
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| _version_ | 1866912438716203008 |
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| author | Gao, Yuan Chang, Chih-Wei Mandava, Sagar Marants, Raanan Scholey, Jessica E. Goette, Matthew Lei, Yang Mao, Hui Bradley, Jeffrey D. Liu, Tian Zhou, Jun Sudhyadhom, Atchar Yang, Xiaofeng |
| author_facet | Gao, Yuan Chang, Chih-Wei Mandava, Sagar Marants, Raanan Scholey, Jessica E. Goette, Matthew Lei, Yang Mao, Hui Bradley, Jeffrey D. Liu, Tian Zhou, Jun Sudhyadhom, Atchar Yang, Xiaofeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_05804 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | MRI-based Material Mass Density and Relative Stopping Power Estimation via Deep Learning for Proton Therapy Gao, Yuan Chang, Chih-Wei Mandava, Sagar Marants, Raanan Scholey, Jessica E. Goette, Matthew Lei, Yang Mao, Hui Bradley, Jeffrey D. Liu, Tian Zhou, Jun Sudhyadhom, Atchar Yang, Xiaofeng Medical Physics 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. |
| title | MRI-based Material Mass Density and Relative Stopping Power Estimation via Deep Learning for Proton Therapy |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2210.05804 |