Purpose: To assess the effects of deep learning image reconstruction (DLIR) and hybrid iterative reconstruction (HIR) on the image quality of virtual monochromatic spectral (VMS) images and to investigate the dose reduction potential of the VMS and conventional 120 kVp images. Methods: A cylindrical phantom simulating an adult abdomen was used. The contrast was set to 60 (medium) and 300 (high) Hounsfield units. CT acquisitions were performed at three dose levels: 12, 9, and 6 mGy. Images were reconstructed via filtered back projection (FBP), DLIR, and HIR. The noise power spectrum (NPS) and task transfer function (TTF) were measured, and the system performance (SP) function was calculated (TTF2/NPS). Results: The noise magnitudes at low spatial frequencies with DLIR and HIR were lower than that with FBP by 45.6% and 24.4%, respectively. Compared to the FBP results, the TTF values at 50% with DLIR at medium and high contrast changed by –13.2% and +25.3% with the VMS images and –2.0% and +9.3% with the 120 kVp images, respectively. In the VMS and 120 kVp images, compared to the SP values of 12 mGy FBP images, SP values of 6 mGy DLIR images decreased at medium contrast and increased at high contrast. Conclusions: DLIR achieved better noise reduction than HIR. The spatial resolution of VMS-DLIR varied significantly depending on the contrast. The image quality of VMS-DLIR and 120 kVp-DLIR potentially decrease in medium contrast tasks and increase in high contrast tasks with 50% dose reduction.
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