ArticleTomography (Ann Arbor, Mich.)2026
Standardized Images and Evaluation Metrics for Tomography.
Article in Tomography (Ann Arbor, Mich.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
BACKGROUND/
objectivesModern tomographic reconstruction methods-including physics-informed and AI-based approaches-can produce very high fidelity images. In this regime, widely used global image quality metrics often approach saturation, making it harder to distinguish residual differences between methods and identify remaining performance gaps. This study introduces a physically grounded and standardized evaluation framework designed to retain sensitivity beyond conventional global metrics and support both comparison and systematic improvement in tomographic reconstruction methods.
methodsThe proposed framework defines standardized reference images-"Source", "Detector", "Ideal", and "Realistic"-using Monte Carlo simulations, with the Ideal Image serving as a physically grounded benchmark. Reconstruction performance is evaluated using pixel-wise difference and χ2 maps, Region-of-Interest analysis, intensity (gray-value) histogram comparisons, and the Structure and Contrast Index (SCI), computed on difference maps. Demonstrations use simulated SPECT data reconstructed with ART, MLEM, and RISE-1.
resultsAcross case studies, SCI and χ2-based diagnostics reveal structured residuals and localized deficiencies not evident from global similarity metrics such as SSIM or NMSE. Comparative analyses show that methods with similar global scores can exhibit distinct residual structures and region-specific performance variations, while improved agreement in the sinogram domain does not necessarily translate into improved image fidelity. Histogram-based diagnostics provide complementary information on intensity redistribution not captured by pixel-domain summaries.
conclusionsThe framework provides a reproducible, physically meaningful, and sensitive approach for evaluating tomographic reconstruction performance in the high-fidelity regime. By combining standardized reference images with multi-domain and multi-metric analysis, it enables robust benchmarking and supports physically consistent interpretation of reconstruction quality.
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