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Deep Neural Networks for Image-Based Dietary Assessment
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Real-time tumor tracking using fluoroscopic imaging with deep neural network analysis.

Ryusuke Hirai1, Yukinobu Sakata1, Akiyuki Tanizawa1

  • 1Corporate Research and Development Center, Toshiba Corporation, Kanagawa 212-8582, Japan.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|April 1, 2019
PubMed
Summary

A novel deep neural network (DNN) algorithm enables markerless tumor tracking using fluoroscopy, improving respiratory gating accuracy. This advanced tumor tracking technology achieved high precision with rapid computation times.

Keywords:
Deep neural networkImage guidanceMarkerless tumor trackingParticle beam therapy

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Respiratory gating is crucial for accurate radiotherapy, but traditional methods can be invasive or time-consuming.
  • Improving tumor tracking accuracy and treatment throughput is essential for effective radiation delivery.

Purpose of the Study:

  • To develop and evaluate a fluoroscopic markerless tumor tracking algorithm based on a deep neural network (DNN).
  • The goal was to enhance respiratory gating accuracy and treatment throughput in radiotherapy.

Main Methods:

  • A DNN was trained using digitally reconstructed radiography (DRR) images from 4D CT to generate target probability maps (TPMs).
  • The trained network was applied to fluoroscopic images for real-time tumor position estimation.
  • Lateral correction using linear regression was integrated to refine estimated target positions.

Main Results:

  • The markerless tracking algorithm achieved an average accuracy of 1.64 ± 0.73 mm across lung and liver cancer cases.
  • Tracking accuracy was higher for liver cases (1.37 ± 0.81 mm) compared to lung cases (1.90 ± 0.65 mm).
  • The computation time for processing a pair of fluoroscopic images was less than 40 ms.

Conclusions:

  • The developed markerless tumor tracking algorithm successfully estimated tumor positions with high accuracy.
  • This technology shows promise for advancing tumor tracking in radiotherapy, potentially improving treatment efficacy and efficiency.