Abstract

Patient-specific dosimetry and quantitative external counting for in vivo monitoring of radiopharmaceutical therapies require detector calibration coefficients (k) that are currently obtained through computationally intensive Monte Carlo (MC) simulations. We present a digital-twin framework that learns the mapping from organ-level Specific Absorbed Fractions (SAFs) to clinical k values for X-ray and gamma-ray detectors, trained on 4.47 million SAF entries from the OpenDose collaboration covering the ICRP-110 adult male (AM) and adult female (AF) reference phantoms, 91 photon energies (5–10,000 keV), and 336 source organs. An uncertainty-aware ensemble combining Histogram Gradient Boosting, ExtraTrees, Random Forest, Quantile-HGBT, and a Feature-Tokenizer Transformer (414 K parameters) was stacked via ridge regression and calibrated using Conformalized Quantile Regression. The ensemble achieved a test mean absolute error of 0.220 in log10-SAF space (R2 = 0.921) with an empirical 95% prediction interval coverage of 95.0%. Validation against 10,487 independent published S-values (EMDOSE) yielded a Pearson correlation of 0.990 (log space). The framework reduces k-coefficient computation time from hours of MC simulation to milliseconds, supporting real-time detector calibration for theranostic workflows.

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