Ultra-high resolution computed tomography of joints: practical recommendations for acquisition protocol optimization

摘要

Background: To assess the influence on the spatial resolution of various Ultra-high-resolution computed tomography (CT) parameters and provide practical recommendations for acquisition protocol optimization in musculoskeletal imaging. Methods: All acquisitions were performed with an Ultra-high resolution scanner, and variations of the following parameters were evaluated: field-of-view (150–300 mm), potential (80–140 KVp), current (25–250 mAs), focal spot size (0.4×0.5 to 0.8×1.3 mm 2 ), slice thickness (0.25–0.5 mm), reconstruction matrix (512×512 to 2048×2048), and iso-centering (up to 85 mm off-center). Two different image reconstruction algorithms were evaluated: hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR). CATPHAN 600 phantom images were analyzed to calculate the number of visible line pairs per centimeter (lp/cm). Task transfer function (TTF) curves were calculated to quantitatively evaluate spatial resolution. Cadaveric knee acquisitions were also performed. Results: Under the conditions studied, the factor that most intensely influenced spatial resolution was the matrix size (additional visualization of up to 8 lp/cm). Increasing the matrix from 512×512 to 2048×2048 led to a 28.2% increase in TTF10% values with a high-dose protocol and a 5.6% increase with a low-dose protocol with no change in the number of visually distinguishable line pairs. The second most important factor affecting spatial resolution was the tube output (29.6% TTF10% gain and 5 additional lp/cm visualized), followed by the reconstruction algorithm choice and lateral displacement (both with a 4 lp/cm gain). Decreasing the slice thickness from 0.5 to 0.25 mm, led to an increase of 3 lp/cm (from 17 to 20 lp/cm) and a 17.3% increase in TTF10% values with no change in the “in-plane” spatial resolution. Conclusions: This study provides practical recommendations for spatial resolution optimization using Ultra-high-resolution CT.