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Original paper| Volume 70, P196-205, February 2020

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Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma

Published:February 08, 2020DOI:https://doi.org/10.1016/j.ejmp.2020.02.001

      Highlights

      • We have developed a new method to track tumor position using fluoroscopic images.
      • Tracking accuracy averaged over seven liver cases was 1.30 ± 0.54 mm.
      • Computation time was < 33 ms for a pair of fluoroscopic images.

      Abstract

      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.

      Keywords

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