Institute of Medical Informatics, University of Luebeck, Germany.
Department of Bioengineering, Imperial College London, United Kingdom.
Med Image Anal. 2017 Dec;42:173-188. doi: 10.1016/j.media.2017.06.011. Epub 2017 Jul 18.
The gynecological cancer mortality rate, including cervical, ovarian, vaginal and vulvar cancers, is more than 20,000 annually in the US alone. In many countries, including the US, external-beam radiotherapy followed by high dose rate brachytherapy is the standard-of-care. The superior ability of MR to visualize soft tissue has led to an increase in its usage in planning and delivering brachytherapy treatment. A technical challenge associated with the use of MRI imaging for brachytherapy, in contrast to that of CT imaging, is the visualization of catheters that are used to place radiation sources into cancerous tissue. We describe here a precise, accurate method for achieving catheter segmentation and visualization. The algorithm, with the assistance of manually provided tip locations, performs segmentation using image-features, and is guided by a catheter-specific, estimated mechanical model. A final quality control step removes outliers or conflicting catheter trajectories. The mean Hausdorff error on a 54 patient, 760 catheter reference database was 1.49 mm; 51 of the outliers deviated more than two catheter widths (3.4 mm) from the gold standard, corresponding to catheter identification accuracy of 93% in a Syed-Neblett template. In a multi-user simulation experiment for evaluating RMS precision by simulating varying manually-provided superior tip positions, 3σ maximum errors were 2.44 mm. The average segmentation time for a single catheter was 3 s on a standard PC. The segmentation time, accuracy and precision, are promising indicators of the value of this method for clinical translation of MR-guidance in gynecologic brachytherapy and other catheter-based interventional procedures.
仅在美国，妇科癌症（包括宫颈癌、卵巢癌、阴道癌和外阴癌）的死亡率就超过每年 20000 人。在包括美国在内的许多国家，外照射放疗后行高剂量率近距离放疗是标准治疗方法。磁共振成像（MRI）在软组织成像方面的卓越能力，使其在近距离放疗计划和实施中得到了越来越多的应用。与 CT 成像相比，将 MRI 成像用于近距离放疗有一个技术挑战，即难以可视化用于将放射源放置在癌组织中的导管。我们在此描述了一种精确、准确的导管分割和可视化方法。该算法在手动提供尖端位置的帮助下，使用图像特征进行分割，并由特定于导管的估计机械模型进行指导。最后一步质量控制步骤可剔除异常值或相互冲突的导管轨迹。在一个包含 54 名患者和 760 根导管的参考数据库上，平均 Hausdorff 误差为 1.49 毫米；51 个异常值偏离金标准超过两个导管宽度（3.4 毫米），这对应于 Syed-Neblett 模板中导管识别准确率为 93%。在一个用于模拟不同手动提供的尖端位置以评估均方根精度的多用户模拟实验中，3σ 最大误差为 2.44 毫米。单个导管的平均分割时间为 3 秒，这是在标准 PC 上完成的。分割时间、准确性和精度，是该方法在妇科近距离放疗和其他基于导管的介入性手术中实现 MRI 引导的临床转化的有价值的指标。
