Kidney stone classification by speckle x-ray imaging

Objective: Urinary stone-related diseases affect approximately 6% of the global population, with nearly half of the patients experiencing recurrence. The diagnosis and management of the disease depend on the stone type and composition. Yet, current clinical imaging modalities (ultrasound, computed tomography, and radiography) lack the sensitivity and specificity required for accurate classification. Speckle-based darkfield X-ray imaging offers a potential non-invasive method for classifying urinary stones with the required hardware simplicity and robustness for potential in vivo, clinical applicability. However, the influence of diffuser masks and state-of-the-art speckle X-ray image retrievals on classification remains underexplored.Approach: This ex vivo study systematically compared the efficacy of custom diffuser masks and state-of-the-art speckle X-ray retrieval algorithms, using both grid and custom speckle masks, for single-shot dark-field imaging in urinary stone classification at high X-ray energy (80 kVp).Main Results: Among the various types of urinary stones examined in this study, canine ammonium urate showed the most distinct visibility contrast difference, deviating by 32% from the z-standardized reference transmission. Overall, the results indicate a potential to differentiate between three main groups of urinary stones based on their attenuation-to-scattering coefficients: ammonium urate, calcium-based stones, and a third group comprising cystine and struvite. Comparisons of Unified Modulated Pattern Analysis, Fokker-Planck, and classical retrieval methods showed that Fokker-Planck was the most noise-sensitive, while Unified Modulated Pattern Analysis was the most robust.Significance: The findings in this study establish a technical foundation for advancing speckle-based dark-field X-ray imaging toward clinical translation for noninvasive urinary stone classification.