Response surface methodology for predicting optimal conditions in very low-dose chest CT imaging

Dose reduction techniques, such as new reconstruction algorithms and automated exposure control systems vary with manufacturer and scanner models, complicating the optimization and standardization procedures. We investigated the feasibility of using the design of experiments in CT protocols optimization.

A Doehlert matrix was used to define the experiments to carry out. Measurements were conducted on a 128-slice CT scanner using an anthropomorphic chest phantom with a 5 mm diameter lesion that has a HU of -800. CT images were reconstructed using iterative (ASIR-V) and deep learning-based reconstruction techniques at low (DLIR-L) and high (DLIR-H) strengths. Lesion detectability was assessed using two self-supervised learning-based model observers and six human observers. Second-order polynomial functions have been established to model the combined effect of noise index (NI) and percentage of ASIR-V on dose and model observers' performances. The analysis of agreement between model and human observers was performed using correlation coefficients and Bland-Altman test.

The optimal conditions predicted by this method were NI = 64, % ASIR-V = 60 and DLIR-H reconstruction. They were found in good agreement with the experimental results obtained by the average human observer, as showed by the Bland-Altman plot with a mean absolute difference of -0.01 ± 3.16. Compared to 60 % ASIR-V, these results suggested an approximately 64 % dose reduction potential for DLIR-H without compromising lesion detection.

The proposed method can predict the optimal conditions that ensure diagnostic quality of low-dose chest CT examinations, while minimizing the number of experiments to carry out.