Mark A Rodrigues1, Michelle C Williams2, Thomas Fitzgerald1, Martin Connell3, Nicholas W Weir3,4, David E Newby2, Edwin J R van Beek3, Saeed Mirsadraee3. 1. 1 Department of Radiology, Royal Infirmary of Edinburgh, Edinburgh, UK. 2. 2 British Heart Foundation/University of Edinburgh Centre for Cardiovascular Science, Queen's Medical Research Institute, Edinburgh, UK. 3. 3 Clinical Research Imaging Centre, University of Edinburgh, Edinburgh, UK. 4. 4 Department of Medical Physics, Royal Infirmary of Edinburgh, Edinburgh, UK.
Abstract
OBJECTIVE: : CT coronary artery calcium scoring (CACS) is additive to traditional risk factors for predicting future cardiac events but is associated with relatively high radiation doses. We assessed the feasibility of CACS radiation dose reduction using a lower tube current and iterative reconstruction (IR). METHODS: : Artificial noise was added to the raw data from 27 CACS studies from patients who were symptomatic to simulate lower tube current scanning (75, 50 and 25% original current). All studies were performed on the same CT scanner at 120 kVp. Data were reconstructed using filtered back projection [Quantum Denoising Software (QDS+)] and IR [adaptive iterative dose reduction three dimensional mild, standard and strong]. Agatston scores were independently measured by two readers. CACS percentile risk scores were calculated. RESULTS: : At 75, 50 and 25% tube currents, all adaptive iterative dose reduction (AIDR) reconstructions decreased image noise relative to QDS+ (p < 0.05). All AIDR reconstructions resulted in small reductions in Agatston score relative to QDS+ at the standard tube current (p < 0.05). Agatston scores increased with QDS+ at 75, 50 and 25% tube current (p < 0.05), whereas no significant change was observed with AIDR mild at any tested tube current. No difference in the percentile risk score with AIDR mild at any tube current occurred compared with QDS+ at standard tube current (p > 0.05). Interobserver agreement for AIDR mild remained excellent even at 25% tube current (intraclass correlation coefficient 0.997). CONCLUSION: : Up to 75% reduction in CACS tube current is feasible using AIDR mild. ADVANCES IN KNOWLEDGE:: AIDR mild IR permits low tube current CACS whilst maintaining excellent intraobserver and interobserver variability and without altering risk classification.
OBJECTIVE: : CT coronary artery calcium scoring (CACS) is additive to traditional risk factors for predicting future cardiac events but is associated with relatively high radiation doses. We assessed the feasibility of CACS radiation dose reduction using a lower tube current and iterative reconstruction (IR). METHODS: : Artificial noise was added to the raw data from 27 CACS studies from patients who were symptomatic to simulate lower tube current scanning (75, 50 and 25% original current). All studies were performed on the same CT scanner at 120 kVp. Data were reconstructed using filtered back projection [Quantum Denoising Software (QDS+)] and IR [adaptive iterative dose reduction three dimensional mild, standard and strong]. Agatston scores were independently measured by two readers. CACS percentile risk scores were calculated. RESULTS: : At 75, 50 and 25% tube currents, all adaptive iterative dose reduction (AIDR) reconstructions decreased image noise relative to QDS+ (p < 0.05). All AIDR reconstructions resulted in small reductions in Agatston score relative to QDS+ at the standard tube current (p < 0.05). Agatston scores increased with QDS+ at 75, 50 and 25% tube current (p < 0.05), whereas no significant change was observed with AIDR mild at any tested tube current. No difference in the percentile risk score with AIDR mild at any tube current occurred compared with QDS+ at standard tube current (p > 0.05). Interobserver agreement for AIDR mild remained excellent even at 25% tube current (intraclass correlation coefficient 0.997). CONCLUSION: : Up to 75% reduction in CACS tube current is feasible using AIDR mild. ADVANCES IN KNOWLEDGE:: AIDR mild IR permits low tube current CACS whilst maintaining excellent intraobserver and interobserver variability and without altering risk classification.
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