Enhancing Ray Casting with a Modified Adaptive Sampling Technique Based on Feature Importance
DOI:
https://doi.org/10.24191/jcrinn.v11i2.596Keywords:
Radiation field visualization, Ray casting, Adaptive sampling, Feature importance, Computational efficiencyAbstract
Radiation simulation is an essential tool for predicting radiation field distributions and supporting safety assessment, emergency response, and training applications, where accurate visualization plays a critical role in understanding spatial dose variations. However, conventional ray casting methods commonly employ an equidistant sampling strategy, which fails to account for the heterogeneous characteristics of radiation data. This leads to redundant computations in homogeneous regions and insufficient representation of critical areas with sharp gradients or complex structures, thereby reducing both computational efficiency and visualization quality. To address this limitation, this paper proposes a Feature Importance-Based Adaptive Ray Casting Algorithm, which modifies the sampling technique by introducing a feature importance mechanism. Specifically, distance weighting, gradient magnitude, and radiation dose values are integrated to adaptively adjust sampling density and shading intensity along each ray. By allocating more computational resources to physically and visually significant regions, the proposed method enhances boundary representation, preserves critical structural details, and reduces unnecessary sampling. Experimental results demonstrate that the proposed approach improves the visual representation of radiation dose distributions by producing clearer boundaries in high-dose regions, smoother colour transitions, and fewer visual artefacts in low-gradient areas. These results indicate that the proposed method enhances the visual fidelity and interpretability of radiation field visualisation. Additionally, it produces clearer boundaries in high-dose regions, smoother colour transitions, and fewer visual artefacts in low-gradient areas. These results indicate that the method achieves an effective balance between sampling resource utilisation and visualisation quality. Furthermore, the proposed method reduced the number of sampling points by 60% while maintaining superior visualization quality, demonstrating improved computational efficiency.
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Copyright (c) 2026 Qian Chao Huo, Lee Chang Kerk, Shamala Palaniappan, Yuchen Li (Author)
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