S Mirsadraee1, N W Weir2, S Connolly3, J T Murchison2, J H Reid4, N Hirani5, M Connell4, E J van Beek4. 1. Clinical Research Imaging Centre, Queen's Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK. Electronic address: saeed.mirsadraee@ed.ac.uk. 2. Department of Radiology, Royal Infirmary of Edinburgh, 51 Little France Crescent, Edinburgh EH16 4SA, UK. 3. University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK. 4. Clinical Research Imaging Centre, Queen's Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK. 5. Department of Respiratory Medicine, Queen's Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK.
Abstract
AIM: To assess the feasibility of radiation dose reduction with adaptive iterative dose reduction (AIDR-6 3D) reconstruction in dynamic pulmonary CT perfusion. MATERIALS AND METHODS: CTP examinations of 10 patients acquired at 100 kVp/50 mAs were reconstructed with filtered back projection (FBP) and AIDR-3D. Artificial noise was added to raw data (pre-reconstruction projection data) to simulate lower tube current scanning. Radiodensity (in Hounsfield units), noise, and perfusion values were compared. RESULTS: There was no significant difference in noise between the full and simulated reduced tube current with AIDR-3D reconstruction (p = 1). There was significantly lower noise in lung tissue with AIDR-3D images when compared to reconstructions without AIDR-3D (p = 0.005) and no significant change in the radiodensity (p = 1; mean difference <6 HU). Mean perfusion values increased significantly at lower tube currents (25 and 12.5 mAs), compared to 50 mAs (p = 0.005). This effect was significantly greater in larger patients compared to thin patients. CONCLUSION: AIDR-3D produced significantly lower noise images than FBP-based algorithms and maintained consistent noise levels in lung at 12.5 mAs, indicating this algorithm is suitable for reduced dose lung perfusion imaging. Iterative reconstruction allows significant radiation dose reduction of up to fourfold in smaller patients, and up to twofold in the medium/large size patients. The increase in perfusion values at 25% simulated tube currents is attributed to attenuation bias.
AIM: To assess the feasibility of radiation dose reduction with adaptive iterative dose reduction (AIDR-6 3D) reconstruction in dynamic pulmonary CT perfusion. MATERIALS AND METHODS:CTP examinations of 10 patients acquired at 100 kVp/50 mAs were reconstructed with filtered back projection (FBP) and AIDR-3D. Artificial noise was added to raw data (pre-reconstruction projection data) to simulate lower tube current scanning. Radiodensity (in Hounsfield units), noise, and perfusion values were compared. RESULTS: There was no significant difference in noise between the full and simulated reduced tube current with AIDR-3D reconstruction (p = 1). There was significantly lower noise in lung tissue with AIDR-3D images when compared to reconstructions without AIDR-3D (p = 0.005) and no significant change in the radiodensity (p = 1; mean difference <6 HU). Mean perfusion values increased significantly at lower tube currents (25 and 12.5 mAs), compared to 50 mAs (p = 0.005). This effect was significantly greater in larger patients compared to thin patients. CONCLUSION: AIDR-3D produced significantly lower noise images than FBP-based algorithms and maintained consistent noise levels in lung at 12.5 mAs, indicating this algorithm is suitable for reduced dose lung perfusion imaging. Iterative reconstruction allows significant radiation dose reduction of up to fourfold in smaller patients, and up to twofold in the medium/large size patients. The increase in perfusion values at 25% simulated tube currents is attributed to attenuation bias.
Authors: Fabian Henry Jürgen Elsholtz; Lars-Arne Schaafs; Christoph Erxleben; Bernd Hamm; Stefan Markus Niehues Journal: Radiol Med Date: 2018-06-19 Impact factor: 3.469
Authors: Mischa Woisetschläger; Lilian Henriksson; Wolf Bartholomae; Thomas Gasslander; Bergthor Björnsson; Per Sandström Journal: Eur J Radiol Open Date: 2020-07-03