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 mm2), 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. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
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 mm2), 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. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Authors: Kishore Rajendran; Naveen S Murthy; Matthew A Frick; Shengzhen Tao; Mark D Unger; Katherine T LaVallee; Nicholas B Larson; Shuai Leng; Timothy P Maus; Cynthia H McCollough Journal: Invest Radiol Date: 2020-06 Impact factor: 10.065
Authors: Kathryn S Stok; Andrew J Burghardt; Stephanie Boutroy; Michiel P H Peters; Sarah L Manske; Vincent Stadelmann; Nicolas Vilayphiou; Joop P van den Bergh; Piet Geusens; Xiaojuan Li; Hubert Marotte; Bert van Rietbergen; Steven K Boyd; Cheryl Barnabe Journal: Quant Imaging Med Surg Date: 2020-02
Authors: Frederick J A Meijer; Joanne D Schuijf; Joost de Vries; Hieronymus D Boogaarts; Willem-Jan van der Woude; Mathias Prokop Journal: Insights Imaging Date: 2019-01-28