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Updated: May 28, 2026
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In vivo Imaging of Deep Cortical Layers using a Microprism
Published on: August 27, 2009
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.
Medical Physics
|May 27, 2026
Summary
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.
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.
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