Efficient and Practical Framework for Bias Estimation in Spectral CT

Spectral computed tomography (CT) is increasingly used for quantitative imaging, yet accurate prediction of spectral quantitative bias remains challenging and computationally expensive with conventional approaches. Bias manifests as systematic deviations in reconstructed quantities (e.g., Hounsfield units, iodine density) from their true physical values. It arises from a combination of model mismatch, hardware/processing imperfections, exam-dependent factors, and noise-induced effects amplified by nonlinear operations such as the logarithmic transformation and material decomposition.

We present a practical, projection-based statistical framework to estimate noise-induced spectral bias efficiently, without the runtime burden of Monte Carlo (MC) simulation.

To demonstrate the bias estimator, we modeled the central-ray of a clinical X-ray tube attenuating through a 300 mm patient-equivalent path with a 10 mm insert containing 10 mg/mL iodine. A 120 kVp tube voltage and tube currents from 100-350 mA were used. Ideal and realistic photon-counting detector responses were simulated across 50 bin threshold settings. Quantum Poisson noise was modeled, and Bayesian probabilities of material decomposition outputs centered on ground truth iodine and water bases were computed. Expected material decomposition outputs were derived from a 2D probability map, and bias was measured. A simple Python Monte Carlo (MC) simulation served as a reference.

The proposed bias estimator closely matched MC-derived bias, with an average relative iodine bias percent difference between the estimators of 0.44% across all tube currents and bin thresholds. Average runtime of the bias estimator was only 0.5% of the MC simulation. Optimal thresholds for minimizing iodine noise (via the Cramér-Rao lower bound) differed from those minimizing iodine bias, highlighting key noise-bias tradeoffs.

Efficient spectral bias and noise estimation are essential for quantitative CT system design. This modular framework enables rapid, bias-aware optimization of spectral acquisition parameters and is adaptable to alternative spectral CT technologies beyond photon counting.

Please briefly (150 words or less) describe the novelty and/or significance of your study . Bias estimation is paramount for designing accurate spectral CT systems that deliver improved diagnostic performance. Traditional approaches rely on computationally intensive Monte Carlo simulations. We propose an efficient and practical bias estimator that uses Bayesian statistics and expected material decomposition values derived from a flexible, modular CT forward model. Unlike conventional Monte Carlo approaches, this framework enables rapid exploration of spectral design tradeoffs between bias and noise. We demonstrate both the accuracy and speed of this bias estimator relative to Monte Carlo approaches.