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Real-time liver motion estimation via deep learning-based angle-agnostic X-ray imaging.

Hua-Chieh Shao1,2,3, Yunxiang Li1,2,3, Jing Wang1,2,3

  • 1The Advanced Imaging and Informatics for Radiation Therapy (AIRT) Laboratory, Dallas, Texas, USA.

Medical Physics
|November 3, 2023
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Summary

A new deep learning method, X360, enables accurate liver motion estimation from any X-ray angle, improving real-time imaging and tumor localization during radiotherapy.

Keywords:
X-raygraph neural networkliverreal-time imaging

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Area of Science:

  • Medical imaging
  • Radiotherapy
  • Artificial intelligence

Background:

  • Real-time liver imaging is limited by rapid breathing, causing under-sampling in 3D imaging.
  • Current deep learning (DL) methods for motion estimation are often limited to fixed X-ray angles, hindering radiotherapy guidance.

Purpose of the Study:

  • To develop an angle-agnostic DL method for deformable liver motion estimation from individual X-ray projections.
  • To enhance the accuracy of single X-ray-based motion estimation for real-time liver imaging.

Main Methods:

  • Developed X360, a DL method using patient-specific 4D-CT data and a deformation-driven approach to estimate liver boundary motion.
  • Incorporated a geometry-informed X-ray feature pooling layer for angle-agnostic feature extraction.
  • Integrated X360 with optical surface imaging and biomechanical modeling for intra-liver motion and tumor localization.

Main Results:

  • X360 reduced the mean 95-percentile Hausdorff distance of liver boundary motion from 10.9 mm to 5.5 mm.
  • Integrated X360 improved liver tumor localization accuracy, decreasing the center-of-mass error from 9.4 mm to 2.2 mm.
  • Demonstrated robust liver boundary motion estimation from arbitrary-angle X-ray projections.

Conclusions:

  • X360 provides fast and robust liver boundary motion estimation from any X-ray projection angle for real-time imaging guidance.
  • The X360 framework enables accurate, real-time, and marker-less liver tumor localization.