ELECTRIC: Evidential Learning-Enhanced CT Reconstruction via Iterative Correction

• URL: http://arxiv.org/abs/2608.00060v1
• Date: Mon, 27 Jul 2026 16:47:52 GMT
• Title: ELECTRIC: Evidential Learning-Enhanced CT Reconstruction via Iterative Correction
• Abstract summary: ELECTRIC (Evidential Learning-Enhanced CT Reconstruction via Iterative Correction) is a physics-guided Bayesian formulation.<n>On held-out patients, the learned prior mean reduces reconstruction error by roughly 70 percent relative to filtered back-projection.<n>The physics-guided update restores measurement consistency while the adaptive-precision reconstruction matches or exceeds a validation-tuned fixed prior.
• Score: 7.5571037918649155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Here we introduce ELECTRIC (Evidential Learning-Enhanced CT Reconstruction via Iterative Correction), a physics-guided Bayesian formulation. An evidential neural network provides an image proposal and an error-predictive epistemic-uncertainty surrogate. The latter is converted into an adaptive precision field and inserted into a Poisson-weighted MAP update. The resulting image-evidence-precision-reconstruction loop treats prior confidence as a learned state variable of iterative reconstruction. In addition to the formulation and theoretical analysis, we report two simulation studies on image slices from the AAPM Mayo Clinic Low-Dose CT dataset: a mechanism-validation pilot using transparent surrogate estimators, and a feasibility study in which a trained Normal-Inverse-Gamma evidential network drives the full closed loop. On held-out patients, the learned prior mean reduces reconstruction error by roughly 70 percent relative to filtered back-projection, the learned epistemic uncertainty is error-predictive and supports selective trust, and the physics-guided update restores measurement consistency while the adaptive-precision reconstruction matches or exceeds a validation-tuned fixed prior and remains markedly more robust to prior-strength misspecification. Together these results demonstrate the complete ELECTRIC closed-loop pipeline, while identifying formal uncertainty calibration and joint training as the principal directions for future work.

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