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

A new prototype of kV Dual-Layer Imager enables in-treatment material decomposition. However, conventional decomposition methods, such as direct matrix inversion, result in significant noise amplification, limiting its clinical applicability.

To develop and evaluate a fast, noise-robust material decomposition method for dual-layer kV X-ray imaging. This approach addresses the severe noise amplification and instability associated with direct dual-energy inversion through a regularized, projection-domain real-time framework.

Dual energy projections were acquired using a prototype kV Dual-Layer Imager on a clinical linear accelerator. A penalized weighted least squares objective function was implemented to perform iterative material decomposition. Four regularization strategies were investigated: quadratic, edge-weighted quadratic, non-local similarity, and the proposed cross-similarity, which enforces consistency in both spatial and spectral domains. Performance was evaluated using a TOR-18FG phantom to quantify the trade-off between noise and spatial resolution (Line Spread Function FWHM) and signal bias, as well as on patient thoracic projections. Computation times were compared between a CPU sparse-matrix implementation and a custom GPU matrix-free implementation.

The proposed cross-similarity regularization achieved up to a fivefold noise reduction in water-equivalent images while preserving spatial resolution, outperforming local regularization methods. Unlike standard similarity regularization, which induced signal bias (up to $31\%$ ) with larger search windows, cross-similarity maintained minimal bias (below $1\%$ ). In patient studies, cross-similarity enhanced soft-tissue visibility and preserved fine lung structures better than local methods. The GPU matrix-free implementation achieved decomposition times under $50\text{ms}$ , approximately two orders of magnitude faster than the CPU implementation CONCLUSIONS: Our developed method provides a robust framework for high-quality DE material decomposition. The novel cross-similarity regularization offers superior noise suppression and resolution preservation compared to conventional methods. With sub-50 ms processing times, the GPU-accelerated implementation satisfies the latency requirements for real-time clinical applications such as intra-fraction markerless tumor tracking.