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A conditional point cloud diffusion model for deformable liver motion tracking via a single arbitrarily-angled x-ray

Jiacheng Xie1,2,3, Hua-Chieh Shao1,2,3, Yunxiang Li1,2,3

  • 1Department of Radiation Oncology, The Advanced Imaging and Informatics for Radiation Therapy (AIRT) Laboratory, Dallas, TX 75390, United States of America.

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Summary

This study introduces a novel framework for tracking liver motion using a single X-ray projection, significantly improving accuracy for image-guided radiotherapy. The method enhances liver tumor localization by estimating deformable motion from X-ray images.

Keywords:
biomechanical modelingdiffusion modellivermotion estimationpoint cloudx-ray projection

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Radiotherapy

Background:

  • Accurate tracking of deformable liver motion is crucial for real-time monitoring and intervention during radiotherapy.
  • Existing methods face challenges with single X-ray projections and arbitrary angles.

Purpose of the Study:

  • To introduce a conditional point cloud diffusion (PCD) model-based framework (PCD-Liver) for accurate and robust liver motion tracking from single X-ray projections.
  • To enable precise liver tumor localization by inferring internal motion from surface motion estimation.

Main Methods:

  • Developed a patient-specific model combining rigid alignment and a conditional PCD model to estimate deformable vector fields (DVFs) of the liver surface.
  • Utilized a geometry-informed feature pooling layer to extract motion-encoded features from single X-ray images, enabling projection angle-agnostic DVF estimation.
  • Integrated the estimated liver surface motion into a U-Net-based biomechanical model to infer internal liver motion and localize tumors.

Main Results:

  • PCD-Liver significantly reduced motion estimation errors, with mean RMSE decreasing from 8.82 mm to 3.63 mm and HD95 from 10.84 mm to 4.29 mm.
  • Tumor localization error (COME) was reduced from a mean of 9.72 mm to 3.46 mm.
  • The framework demonstrated stable performance even under highly noisy conditions.

Conclusions:

  • The proposed PCD-Liver framework offers an accurate and robust solution for estimating deformable liver motion from single X-ray projections.
  • This approach enhances liver tumor localization accuracy, paving the way for improved image-guided radiotherapy.
  • The projection angle-agnostic nature of the model increases its applicability in diverse clinical scenarios.