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PRISM: An open-source framework for regularized material decomposition on a novel kV dual-layer imager.

François de Kermenguy1, Matthew W Jacobson1, Gregory C Sharp2

  • 1Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, Massachusetts, USA.

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A new cross-similarity regularization method significantly reduces noise in dual-layer kV X-ray imaging, improving material decomposition for real-time clinical applications like markerless tumor tracking.

Keywords:
dual‐energyimage‐guided radiation therapymaterial decomposition

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Radiology

Background:

  • Dual-layer kV X-ray imaging enables in-treatment material decomposition.
  • Conventional methods amplify noise, limiting clinical use.

Purpose of the Study:

  • Develop a fast, noise-robust material decomposition method for dual-layer kV X-ray imaging.
  • Address noise amplification and instability in dual-energy imaging.

Main Methods:

  • Implemented iterative material decomposition using penalized weighted least squares.
  • Investigated four regularization strategies, including a novel cross-similarity approach.
  • Evaluated performance on phantoms and patient data, comparing CPU and GPU implementations.

Main Results:

  • Cross-similarity regularization reduced noise by fivefold while preserving spatial resolution.
  • Achieved minimal signal bias (<1%) compared to other methods.
  • GPU implementation processed data in under 50 ms, enabling real-time applications.

Conclusions:

  • Developed a robust framework for high-quality dual-energy material decomposition.
  • Novel cross-similarity regularization offers superior noise suppression and resolution preservation.
  • GPU-accelerated method meets latency requirements for real-time clinical applications.