Search

Prospective head-to-head comparison of coronary calcium scores between standard computed tomography (CT) and photon-counting CT show no significant differences, while photon-counting CT administers substantially lower radiation dose.

BackgroundAdvances in CT and machine learning have enabled on-site non-invasive assessment of fractional flow reserve (FFRCT).PurposeTo assess the interoperator and intraoperator variability of coronary CT angiography-derived FFRCT using a machine learning-based postprocessing prototype.Materials and methodsWe included 60 symptomatic patients who underwent coronary CT angiography. FFRCT was calculated by two independent operators after training using a machine learning-based on-site prototype. FFRCT was measured 1 cm distal to the coronary plaque or in the middle of the segments if no coronary lesions were present. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to evaluate interoperator variability effect in FFRCT estimates. Sensitivity analysis was done by cardiac risk factors, degree of stenosis and image quality.ResultsA total of 535 coronary segments in 60 patients were assessed. The overall ICC was 0.986 per patient (95% CI 0.977 to 0.992) and 0.972 per segment (95% CI 0.967 to 0.977). The absolute mean difference in FFRCT estimates was 0.012 per patient (95% CI for limits of agreement: −0.035 to 0.039) and 0.02 per segment (95% CI for limits of agreement: −0.077 to 0.080). Tight limits of agreement were seen on Bland-Altman analysis. Distal segments had greater variability compared with proximal/mid segments (absolute mean difference 0.011 vs 0.025, pConclusionA high degree of interoperator and intraoperator reproducibility can be achieved by on-site machine learning-based FFRCT assessment. Future research is required to evaluate the physiological relevance and prognostic value of FFRCT.

Background: Advances in computed tomography (CT) and machine learning have enabled on-site non-invasive assessment of fractional flow reserve (FFRCT). Purpose: To assess the inter-operator variability of Coronary CT Angiography–derived FFRCT using a machine learning based post-processing prototype.Materials and Methods: We included 60 symptomatic patients who underwent coronary CT angiography. FFRCT was calculated by 2 independent operators after training using a machine learning based on-site prototype. FFRCT was measured 1 cm distal to the coronary plaque or in the middle of the segments if no coronary lesions were present. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to evaluate inter-operator variability effect in FFRCT estimates. Sensitivity analysis was done by cardiac risk factors, degree of stenosis and image quality. Results: A total of 535 coronary segments in 60 patients were assessed. The overall ICC was 0.986 per patient (95% CI: 0.977 - 0.992) and 0.972 per segment (95% CI: 0.967 - 0.977). The absolute mean difference in FFRCT estimates was 0.012 per patient (95% CI for limits of agreement: -0.035 - 0.039) and 0.02 per segment (95% CI for limits of agreement: -0.077 - 0.080). Tight limits of agreement were seen on Bland-Altman analysis. Distal segments had greater variability compared to proximal/mid segments (absolute mean difference 0.011 vs 0.025, pConclusion: A high degree of inter-operator reproducibility can be achieved by onsite machine learning based FFRCT assessment. Future research is required to evaluate the physiological relevance and prognostic value of FFRCT.

Partial scan reconstruction (PSR) artifacts are present in myocardial perfusion imaging using dynamic multidetector computed tomography (MDCT). PSR artifacts appear as temporal CT number variations due to inconsistencies in the angular data range used to reconstruct images and compromise the quantitative value of myocardial perfusion when using MDCT. The purpose of this work is to present and evaluate a technique termed targeted spatial frequency filtration (TSFF) to reduce CT number variations due to PSR when applied to myocardial perfusion imaging using MDCT.The TSFF algorithm requires acquiring enough X-ray projections to reconstruct both partial (π + fan angle α) and full (2π) scans. Then, using spatial linear filters, the TSFF-corrected image data are created by superimposing the low spatial frequency content of the full scan reconstruction (containing no PSR artifacts, but having low spatial resolution and worse temporal resolution) with the high spatial frequency content of the partial scan reconstruction (containing high spatial frequencies and better temporal resolution). The TSFF method was evaluated both in a static anthropomorphic thoracic phantom and using an in vivo porcine model and compared with a previously validated reference standard technique that avoids PSR artifacts by pacing the animal heart in synchrony with the gantry rotation. CT number variations were quantified by measuring the range and standard deviation of CT numbers in selected regions of interest (ROIs) over time. Myocardial perfusion parameters such as blood volume (BV), mean transit time (MTT), and blood flow (BF) were quantified and compared in the in vivo study.Phantom experiments demonstrated that TSFF reduced PSR artifacts by as much as tenfold, depending on the location of the ROI. For the in vivo experiments, the TSFF-corrected data showed two- to threefold decrease in CT number variations. Also, after TSFF, the perfusion parameters had an average difference of 13.1% (range 4.5%-25.6%) relative to the reference method, in contrast to an average difference of 31.8% (range 0.3%-54.0%) between the non-TSFF processed data with the reference method.TSFF demonstrated consistent reduction in CT number variations due to PSR using controlled phantom and in vivo experiments. TSFF-corrected data provided quantitative measures of perfusion (BV, MTT, and BF) with better agreement to a reference method compared to noncorrected data. Practical implementation of TSFF is expected to incur in an additional radiation exposure of 14%, when tube current is modulated to 20% of its maximum, to complete the needed full scan reconstruction.