Overcoming the challenges of inaccurate CT numbers in low dose CT imaging

In recent years, much effort has been committed to lowering radiation dose for x-ray CT imaging. However, when the radiation dose in CT imaging is lowered, not only are noise level and photon starvation-induced noise streaks elevated, but the CT number also becomes more inaccurate or biased. Note that CT number bias issues are intrinsically rooted in the statistical nature of photons and the standard image formation process that has been used for the past 50 years in medical CT practices: after CT data are acquired, a log-transform is applied to generate the sinogram projection data, then an image reconstruction algorithm is applied to reconstruct the CT images. However, there is a fundamental flaw in this image formation process: the log-transform itself is a statistically biased estimator since the statistical mean of the log-transformed data is different from the log-transform of the statistical mean of the data. In medical CT applications, we are forced to take the log-transform of a single sample of the measured CT data and then images are reconstructed from the log-transformed data. Consequently, CT images will then have inaccurate CT numbers. In this work, we investigated the imaging physics foundation of the CT number inaccuracy issue in low dose CT and developed a simple, yet extremely effective correction method to address this long-standing issue in CT imaging. This correction scheme was experimentally validated in the context of photon counting detector CT (PCD-CT). Our experimental results demonstrated that the correction scheme addresses the CT number bias problem and improves material quantification accuracy in PCD-CT images.