BackgroundDue to the nonlinear nature of the logarithmic operation and the stochastic nature of photon counts (N), sinogram data of photon counting detector CT (PCD‐CT) are intrinsically biased, which leads to statistical CT number biases. When raw counts are available, nearly unbiased statistical estimators for projection data were developed recently to address the CT number bias issue. However, for most clinical PCD‐CT systems, users' access to raw detector counts is limited. Therefore, it remains a challenge for end users to address the CT number bias issue in clinical applications.PurposeTo develop methods to correct statistical biases in PCD‐CT without requiring access to raw PCD counts.Methods(1) The sample variance of air‐only post‐log sinograms was used to estimate air‐only detector counts, . (2) If the post‐log sinogram data, y, is available, then N of each detector pixel was estimated using . Once N was estimated, a closed‐form analytical bias correction was applied to the sinogram. (3) If a patient's post‐log sinogram data are not archived, a forward projection of the bias‐contaminated CT image was used to perform a first‐order bias correction. Both the proposed sinogram domain‐ and image domain‐based bias correction methods were validated using experimental PCD‐CT data.ResultsExperimental results demonstrated that both sinogram domain‐ and image domain‐based bias correction methods enabled reduced‐dose PCD‐CT images to match the CT numbers of reference‐standard images within [‐5, 5] HU. In contrast, uncorrected reduced‐dose PCD‐CT images demonstrated biases ranging from ‐25 to 55 HU, depending on the material. No increase in image noise or spatial resolution degradation was observed using the proposed methods.ConclusionsCT number bias issues can be effectively addressed using the proposed sinogram or image domain method in PCD‐CT, allowing PCD‐CT acquired at different radiation dose levels to have consistent CT numbers desired for quantitative imaging.

In recent years there has been increased focus on further reducing radiation dose in CT with photon counting CT using solid-state direct-conversion photon counting detectors (PCDs) to reduce the effective dose from routine CT exams to less than 1 mSv. However, despite its noise-reducing capabilities, PCD-CT faces challenges of inaccurate CT numbers at low-dose levels: with smaller pixel areas and multiple energy channels, the number of digital counts recorded in each bin of each PCD pixel can be as low as single-digit integers leading to statistical biases in CT sinograms due to the nonlinear log transformation operation. After tomographic reconstruction, those biases lead to inaccurate CT numbers in PCD-CT images. Previous correction methods require access to the original raw PCD counts. However, in almost all commercial CT systems, raw detector counts are hidden from the end users. Additionally, some CT systems perform the logarithmic transformation of raw counts as a part of the analog-to-digital conversion process for data compression reasons. For those systems, access to the PCD counts is irretrievably lost. Even for the post-log sinogram data, they are usually not archived for each patient. These practical considerations present challenges to the offline application of CT number bias corrections. The purpose of this work was to develop a method to address the statistical bias problem in low-dose PCD-CT without requiring any access to the raw detector counts. Innovations were made in this work to enable bias correction using the post-log sinogram data or using the reconstructed, bias-contaminated PCD-CT images.