PDF(Pubmed)
Abstract：
Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 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. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.
摘要：
磁共振成像（MRI）由于其优越的软组织对比度而越来越多地用于治疗计划，与计算机断层扫描（CT）相比，这对肿瘤和软组织勾画很有用。然而,MRI不能直接提供质量密度或相对停止力（RSP）图，这是计算质子放射治疗剂量所必需的。因此,将人工智能(AI)集成到基于MRI的治疗计划中以直接从MRI估计质量密度和RSP,这引起了人们的极大兴趣.开发了基于深度学习（DL）的框架，以建立MR图像与质量密度以及RSP之间的体素相关性。为了便于学习，创建了五个组织替代体模，代表不同的组织，如皮肤，肌肉，脂肪组织,45%羟基磷灰石（HA），和海绵状骨。这些幻影的组成基于ICRP报告中的信息。此外,两个动物组织体模，模拟猪的大脑和肝脏，是为DL培训目的而准备的。幻影研究涉及两个DL模型的开发。第一个模型利用临床T1和T2MRI扫描作为输入，而第二个模型包含零回波时间（ZTE）MRI扫描。在患者应用研究中，另外训练了两个DL模型：一个使用T1和T2MRI扫描作为输入，和另一个模型结合合成双能量计算机断层扫描（sDECT）图像，以提供准确的骨组织信息。DECT经验模型被用作评估体模和患者应用研究中提出的模型的参考。选择DECT经验模型作为评估体模和患者应用研究中提出的模型的参考。在幻影研究中，基于T1和T2MRI扫描的DL模型在估计皮肤的质量密度和RSP方面表现出更高的准确性，肌肉，脂肪组织,大脑,还有肝脏.平均绝对百分比误差(MAPE)为0.42%，0.14%，0.19%，0.78%，质量密度为0.26%，和0.30%，0.11%，0.16%，0.61%，RSP为0.23%，分别。结合ZTEMRI的DL模型进一步提高了45％HA和海绵状骨的质量密度和RSP估计的准确性，质量密度的MAPE值为0.23%和0.09%，RSP为0.19%和0.07%，分别。这些结果证明了在质子治疗治疗计划中使用仅MRI方法结合DL方法进行质量密度和RSP估计的可行性。通过采用这种方法，可以直接从MRI扫描中获得质子放射治疗的必要信息，消除了对额外成像模式的需要。
