Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma
Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma
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DOI：
10.1016/j.ejmp.2020.02.001
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发表时间：
2020-02-01
影响因子：
3.4
通讯作者：
Mori, Shinichiro
中科院分区：
文献类型：
--
作者：
Hirai, Ryusuke;Sakata, Yukinobu;Mori, Shinichiro
目的：我们开发了一种使用荧光透视图像跟踪肿瘤位置的新方法，并使用肝细胞癌病例数据对其进行评估。方法：我们的方法由训练阶段和跟踪阶段组成。在训练阶段，使用四维计算机断层扫描（4DCT）数据计算膈肌和肿瘤之间位置关系的模型数据。沿直线检测隔膜，选择该直线是为了避免 4DCT 伪影。在跟踪阶段，通过将模型应用于隔膜来计算透视图像上的肿瘤位置。使用七个肝脏病例的数据，我们评估了四个指标：隔膜边缘检测误差、建模误差、患者设置误差和肿瘤跟踪误差。我们测量了病例中 15 个透视序列的肿瘤跟踪误差并记录了计算时间。结果：膈肌跟踪的平均位置误差为 0.57 +/- 0.62 mm。三维 (3D) 空间中肿瘤跟踪的平均位置误差为 0.63 +/- 0.30 毫米（建模误差），0.81-2.37 毫米（1-2 毫米设置误差）。透视序列中肿瘤跟踪的平均位置误差为 1.30 +/- 0.54 mm，训练和跟踪阶段的平均计算时间分别为每帧 69.0 +/- 4.6 ms 和 23.2 +/- 1.3 ms。结论：我们的无标记跟踪方法成功估计了肿瘤位置。我们相信我们的结果将有助于提高肝脏病例的治疗准确性。
Purpose: We have developed a new method to track tumor position using fluoroscopic images, and evaluated it using hepatocellular carcinoma case data.Methods: Our method consists of a training stage and a tracking stage. In the training stage, the model data for the positional relationship between the diaphragm and the tumor are calculated using four-dimensional computed tomography (4DCT) data. The diaphragm is detected along a straight line, which was chosen to avoid 4DCT artifact. In the tracking stage, the tumor position on the fluoroscopic images is calculated by applying the model to the diaphragm. Using data from seven liver cases, we evaluated four metrics: diaphragm edge detection error, modeling error, patient setup error, and tumor tracking error. We measured tumor tracking error for the 15 fluoroscopic sequences from the cases and recorded the computation time.Results: The mean positional error in diaphragm tracking was 0.57 +/- 0.62 mm. The mean positional error in tumor tracking in three-dimensional (3D) space was 0.63 +/- 0.30 mm by modeling error, and 0.81-2.37 mm with 1-2 mm setup error. The mean positional error in tumor tracking in the fluoroscopy sequences was 1.30 +/- 0.54 mm and the mean computation time was 69.0 +/- 4.6 ms and 23.2 +/- 1.3 ms per frame for the training and tracking stages, respectively.Conclusions: Our markerless tracking method successfully estimated tumor positions. We believe our results will be useful in increasing treatment accuracy for liver cases.