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

Abstract Background 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. Purpose 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. Methods 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. Results 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 ) with larger search windows, cross‐similarity maintained minimal bias (below ). 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 , 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.