Background and purpose

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.

Material and methods

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.

Results

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.

Conclusion

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.

中文翻译：

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针对前列腺癌 I-125 粒子近距离放射治疗中植入后剂量测定的基于深度学习的磁共振成像工作流程

背景和目的

当前用于在种子前列腺近距离放射治疗中创建术后计划的标准成像技术是计算机断层扫描 (CT)，该技术与额外的辐射暴露和较差的软组织对比度有关。为了建立结合改进的组织对比度和高种子可检测性的仅磁共振成像 (MRI) 工作流程，开发了一种用于 MRI 扫描自动种子分割的深度学习方法。

材料与方法

接受 I-125 粒子近距离放射治疗的患者在植入后第 30 天接受了术后 CT 和 1.5 T MRI 扫描。对于基于 MRI 的种子可视化，获取了 DIXON 序列，并根据 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)。在 CT 上创建的种子分割可作为基本事实。对于 MRI 上的自动种子分割，单独或组合使用 QSM 和 DIXON 训练 3D nnU-net 模型。

结果

通过 QSM 和 DIXON 数据中的深度学习自动分割，检测到 94.8 ± 2.4% 的植入种子。在单个序列数据集上训练的模型表现较差，QSM 和 DIXON 的检测率分别为 87.5 ± 2.6% 或 88.6 ± 7.5%。通过 CT 与 QSM 和 DIXON 识别的种子中心平均相距 1.8 ± 1.3 毫米。用于评估定位误差的植入后剂量测定显示，在标准 CT 方法和我们的纯 MRI 工作流程之间，D90（前列腺接受的剂量 90%）仅存在高达 0.4 ± 4.26 Gy 的微小变化。

结论

所提出的基于深度学习的纯 MRI 工作流程提供了有望实现准确且稳健的种子定位，因此有可能在未来与当前最先进的基于 CT 的植入后剂量测定相竞争。