Comparison of quantitative image quality of cardiac computed tomography between raw-data-based and model-based iterative reconstruction algorithms with an emphasis on image sharpness.

Background: Image sharpness is commonly degraded on cardiac CT images reconstructed using iterative reconstruction algorithms.

Objective: To compare the image quality of cardiac CT between raw-data-based and model-based iterative reconstruction algorithms developed by the same CT vendor in children and young adults with congenital heart disease.

Materials And Methods: In 29 patients with congenital heart disease, we reconstructed 39 cardiac CT datasets using raw-data-based and model-based iterative reconstruction algorithms. We performed quantitative analysis of image sharpness using distance and angle on a line density profile across an edge of the descending thoracic aorta in addition to CT attenuation, image noise, signal-to-noise ratio and contrast-to-noise ratio. We compared these quantitative image-quality measures between the two algorithms.

Results: CT attenuation did not show significant differences between the algorithms (P>0.05) except in the aorta. Image noise was small but significantly higher in the model-based algorithm than in the raw-data-based algorithm (4.8±2.3 Hounsfield units [HU] vs. 4.7±2.1 HU, P<0.014). Signal-to-noise ratio (110.2±50.9 vs. 108.4±50.4, P=0.050) and contrast-to-noise ratio (91.0±45.7 vs. 89.6±45.1, P=0.063) showed marginal significance between the two algorithms. The model-based algorithm showed a significantly smaller distance (1.4±0.4 mm vs. 1.6±0.3 mm, P<0.001) and a significantly higher angle (77.0±4.3° vs. 74.1±5.7°, P<0.001) than the raw-data-based algorithm.

Conclusion: Compared with the raw-data-based algorithm, the model-based iterative reconstruction algorithm demonstrated better image sharpness and higher image noise on cardiac CT in patients with congenital heart disease.