Dose the deep learning-based iterative reconstruction affect the measuring accuracy of bone mineral density in low dose chest CT?

Abstract

Objectives: To investigate the impacts of a deep learning-based iterative reconstruction algorithm on image quality and measuring accuracy of bone mineral density (BMD) in low dose chest CT.

Methods: Phantom and patient studies were separately conducted in this study. The same low dose protocol was used for phantoms and patients. All images were reconstructed with filter back projection, hybrid iterative reconstruction (KARL, level of 3,5,7), and deep learning-based iterative reconstruction (AIIR, low, medium and high-strength). The noise power spectrum (NPS) and the task-based transfer function (TTF) were evaluated using phantom. The accuracy and the relative error (RE) of BMD were evaluated using a European spine phantom. The subjective evaluation was performed by two experienced radiologists. BMD was measured using QCT. Image noise, signal-to-noise ratio, contrast-to-noise ratio, BMD values and subjective scores were compared with Wilcoxon signed-rank test. The Cohen's kappa test was used to evaluate the inter-reader and inter-group agreement.

Results: AIIR reduced noise and improved resolution in phantom images significantly. There were no significant differences among BMD values in all groups of images (all p > 0.05). RE of BMD measured with AIIR images were smaller. In objective evaluation, all strengths of AIIR achieved less image noise, higher SNR and CNR (all p < 0.05). AIIR-H showed the lowest noise, highest SNR and CNR (p < 0.05). The increase of AIIR algorithm strengths did not affect BMD values significantly (all p > 0.05).

Conclusion: The deep learning-based iterative reconstruction did not affect the accuracy of BMD measurement with Low-dose chest CT, while reducing image noise and improving spatial resolution.

Advances in knowledge: The BMD values could be measured accurately in low-dose chest CT with deep learning-based iterative reconstruction, while reducing image noise and improving spatial resolution.