Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
Department of Radiation Oncology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
Brachytherapy. 2024 Jan-Feb;23(1):96-105. doi: 10.1016/j.brachy.2023.09.009. Epub 2023 Nov 25.
The current standard imaging-technique for creating postplans in seed prostate brachytherapy is computed tomography (CT), that is associated with additional radiation exposure and poor soft tissue contrast. To establish a magnetic resonance imaging (MRI) only workflow combining improved tissue contrast and high seed detectability, a deep learning-approach for automatic seed segmentation on MRI-scans was developed.
Patients treated with I-125 seed brachytherapy received a postplan-CT and a 1.5 T MRI-scan on nominal day 30 after implantation. For MRI-based seed visualization, DIXON-sequences were acquired and deep learning-based quantitative susceptibility maps (QSM) were generated from 3D-gradient-echo-sequences from 20 patients. Seed segmentations created on CT served as ground truth. For automatic seed segmentation on MRI, a 3D nnU-net model was trained using QSM and DIXON, both solely and combined.
Of the implanted seeds 94.8 ± 2.4% were detected with deep learning automatic segmentation entrained on both QSM and DIXON data. Models trained on the individual sequence data-sets performed worse with detection rates of 87.5 ± 2.6% or 88.6 ± 7.5% for QSM and DIXON respectively. The seed centers identified on CT versus QSM and DIXON were on average 1.8 ± 1.3 mm apart. Postimplant dosimetry for evaluation of positioning inaccuracies revealed only small variations of up to 0.4 ± 4.26 Gy in D90 (dose 90% of the prostate receives) between the standard CT-approach and our MRI-only workflow.
The proposed deep learning-based MRI-only workflow provided a promisingly accurate and robust seed localization and thus has the potential to compete with current state-of-the-art CT-based postimplant dosimetry in the future.
目前，前列腺近距离放射治疗中种子后置计划的标准成像技术是计算机断层扫描（CT），但它会带来额外的辐射暴露和较差的软组织对比度。为了建立一种仅基于磁共振成像（MRI）的工作流程，结合改善的组织对比度和高种子可检测性，我们开发了一种基于深度学习的 MRI 扫描上自动种子分割方法。
接受 I-125 种子近距离放射治疗的患者在植入后第 30 天接受后置计划 CT 和 1.5T MRI 扫描。为了进行基于 MRI 的种子可视化，采集了 DIXON 序列，并从 20 名患者的 3D 梯度回波序列生成了基于深度学习的定量磁化率图（QSM）。CT 上创建的种子分割被用作地面实况。对于 MRI 上的自动种子分割，使用 QSM 和 DIXON 单独和组合来训练 3D nnU-net 模型。
通过深度学习自动分割，94.8%±2.4%的植入种子被检测到，该分割是基于 QSM 和 DIXON 数据进行的。基于单个序列数据集训练的模型性能较差，QSM 和 DIXON 的检测率分别为 87.5%±2.6%和 88.6%±7.5%。在 CT 上识别的种子中心与 QSM 和 DIXON 上的种子中心平均相差 1.8±1.3mm。为了评估定位不准确的情况，对植入后剂量进行了评估，在 D90（前列腺接受的剂量为 90%）方面，标准 CT 方法和我们的 MRI 仅工作流程之间只有很小的变化，最大变化为 0.4±4.26Gy。
提出的基于深度学习的 MRI 仅工作流程提供了一种有前景的准确且稳健的种子定位方法，因此有可能在未来与当前最先进的基于 CT 的植入后剂量学相竞争。
