A Knowledge-based Iterative Model Reconstruction Algorithm: Can Super-Low-Dose Cardiac CT Be Applicable in Clinical Settings?

Abstract

To investigate whether "full" iterative reconstruction, a knowledge-based iterative model reconstruction (IMR),enables radiation dose reduction by 80% at cardiac computed tomography (CT). A total of 23 patients (15 men, eight women; mean age 64.3 ± 13.4 years) who underwent retrospectively electrocardiography-gated cardiac CT with dose modulation were evaluated. We compared full-dose (FD; 730 mAs) images reconstructed with filtered back projection (FBP) technique and the low-dose (LD; 146 mAs) images reconstructed with FBP and IMR techniques. Objective and subjective image quality parameters were compared among the three different CT images. There was no significant difference in the CT attenuation among the three reconstructions. The mean image noise of LD-IMR (18.3± 10.6 Hounsfield units [HU]) was significantly lowest among the three reconstructions (41.9 ± 15.3 HU for FD-FBP and 109.9 ± 42.6 HU for LD-FBP; P < .01). The contrast-to-noise ratio of LD-IMR was better than that of FD-FBP and LD-FBP (P < .01). Visual evaluation score was also highest for LD-IMR. The IMR can provide improved image quality at super-low-dose cardiac CT with 20% of the standard tube current.