Three-dimensional CT imaging in extensor tendons using deep learning reconstruction: optimal reconstruction parameters and the influence of dose

Abstract

The purpose of this study was to evaluate the optimal tube current and reconstruction parameters in extensor tendons 3-dimensional computed tomography (3D CT) using deep learning reconstruction, using iterative reconstruction as a reference. In phantom study, a cylindrical phantom with a 3 mm rod simulated an extensor tendon was used. The phantom images were scanned at tube current of 50, 100, 150, 200, and 250 mA. In the clinical study, nine hands in eight patients underwent CT of hand tendons. All images were reconstructed using advanced intelligent clear-IQ engine (AiCE) parameters (body, body sharp, brain CTA, and brain LCD) and adaptive iterative dose reduction three dimensional (AIDR 3D). Objective image quality for detectability of tendons was evaluated by calculated the low-contrast object speci c contrast-to-noise ratio (CNR LO ) in our phantom study and CNR and coe cient of variation (CV) in the clinical study, respectively. In phantom study, CNR LO (200 mA) of AiCE parameters (body, body sharp, brain CTA, and brain LCD) and AIDR 3D were 5.2, 5.3, 5.3, 5.8, and 5.0, respectively. In the clinical study, AiCE brain CTA was higher CNR and lower CV values than other reconstruction parameters. AiCE without dose reduction may be a useful strategy to further improve image quality of extensor tendons 3D CT. Our study suggests that the AiCE brain CTA is more suitable for extensor tendons 3D CT than other AiCE parameters.