Objectives: The purpose of this study was to investigate the effects of knowledge-based iterative model reconstruction (IMR) on image quality in cardiac CT performed for the planning of redo cardiac surgery by comparing IMR images with images reconstructed with filtered back-projection (FBP) and hybrid iterative reconstruction (HIR).
Methods: We studied 31 patients (23 men, 8 women; mean age 65.1 ± 16.5 years) referred for redo cardiac surgery who underwent cardiac CT. Paired image sets were created using three types of reconstruction: FBP, HIR, and IMR. Quantitative parameters including CT attenuation, image noise, and contrast-to-noise ratio (CNR) of each cardiovascular structure were calculated. The visual image quality--graininess, streak artefact, margin sharpness of each cardiovascular structure, and overall image quality--was scored on a five-point scale.
Results: The mean image noise of FBP, HIR, and IMR images was 58.3 ± 26.7, 36.0 ± 12.5, and 14.2 ± 5.5 HU, respectively; there were significant differences in all comparison combinations among the three methods. The CNR of IMR images was better than that of FBP and HIR images in all evaluated structures. The visual scores were significantly higher for IMR than for the other images in all evaluated parameters.
Conclusions: IMR can provide significantly improved qualitative and quantitative image quality at in cardiac CT for planning of reoperative cardiac surgery.
Key Points: • Cardiac CT before redo surgery may mitigate increased risk • Iterative model reconstruction is the next generation in iterative reconstruction • Iterative model reconstruction improves the image quality in cardiac CT.
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