Purpose. To investigate the feasibility of using a single MRI acquisition for fiducial marker identification and synthetic CT (sCT) generation towards MRI-only treatment planning for prostate external beam radiation therapy (EBRT). Methods. Seven prostate cancer patients undergoing EBRT, each with three implanted gold fiducial markers, participated in this study. In addition to the planning CT scan, all patients were scanned on a 3 T MR scanner with a 3D double-echo gradient echo (GRE) sequence. Quantitative susceptibility mapping (QSM) was performed for marker localization. QSM-derived marker positions were compared to those from CT. The bulk density assignment technique for sCT generation was adopted. The magnitude GRE images were segmented into muscle, bone, fat, and air using a combination of unsupervised intensity-based classification of soft tissue and convolutional neural networks (CNN) for bone segmentation. Results. All implanted markers were visualized and accurately identified (average error: 0.70.5 mm). QSM generated distinctive contrast for hemorrhage, calcifications, and gold fiducial markers. The estimated susceptibility/HU values on QSM/CT for gold and calcifications were 31.52.9 ppm/1220100 HU and 14.60.9 ppm/440100 HU, respectively. The intensity-based soft tissue classification resulted in an average Dice score of 0.970.02; bone segmentation using CNN resulted in an average Dice score of 0.930.03. Conclusion. This work indicates the feasibility of simultaneous fiducial marker identification and sCT generation using a single MRI acquisition. Future works includes evaluation of the proposed method in a large cohort of patients with optimized acquisition parameters as well as dosimetric evaluations.

目的。研究使用单个 MRI 采集进行基准标记识别和合成 CT (sCT) 生成对前列腺外束放射治疗 (EBRT) 的仅 MRI 治疗计划的可行性。方法。七名接受 EBRT 的前列腺癌患者参与了这项研究，每名患者都植入了三个金基准标记。除了计划的 CT 扫描外，所有患者都在 3 T MR 扫描仪上进行了扫描，该扫描仪具有 3D 双回波梯度回波 (GRE) 序列。对标记定位进行了定量敏感性作图 (QSM)。将 QSM 衍生的标记位置与来自 CT 的标记位置进行比较。采用了用于 sCT 生成的体积密度分配技术。使用基于无监督强度的软组织分类和用于骨骼分割的卷积神经网络 (CNN) 的组合，将幅度 GRE 图像分割为肌肉、骨骼、脂肪和空气。结果。所有植入的标记都可视化并准确识别（平均误差：0.70.5 mm）。QSM 对出血、钙化和金基准标记产生了独特的对比。QSM/CT 上金和钙化的估计敏感性/HU 值分别为 31.52.9 ppm/1220100 HU 和 14.60.9 ppm/440100 HU。基于强度的软组织分类的平均 Dice 得分为 0.970.02；使用 CNN 进行骨骼分割的平均 Dice 得分为 0.930.03。结论。这项工作表明使用单个 MRI 采集同时进行基准标记识别和 sCT 生成的可行性。未来的工作包括在具有优化的采集参数以及剂量评估的大量患者中评估所提出的方法。