The purpose of this study was to compare the effectiveness of a metal artifact reduction algorithm (MAR), model-based iterative reconstruction (MBIR), and virtual monochromatic imaging (VMI) for reducing metal artifacts in CT imaging. A phantom study was performed for quantitatively evaluating the dark bands and fine streak artifacts generated by unilateral hip prostheses. Images were obtained by conventional scanning at 120 kilovolt peak, and reconstructed using filtered back projection, MAR, and MBIR. Furthermore, virtual monochromatic images (VMIs) at 70 kilo-electron volts (keV) and 140 keV with/without use of MAR were obtained by dual-energy CT. The extents and mean CT values of the dark bands and the differences in the standard deviations and location parameters of the fine streak artifacts evaluated by the Gumbel method in the images obtained by each of the methods were statistically compared by analyses of variance. Significant reduction of the extent of the dark bands was observed in the images reconstructed using MAR than in those not reconstructed using MAR (all, P < .01). Images obtained by VMI at 70 keV and 140 keV with use of MAR showed significantly increased mean CT values of the dark bands as compared to those obtained by reconstructions without use of MAR (all, <.01). Significant reduction of the difference in the standard deviations used to evaluate fine streak artifacts was observed in each of the image sets obtained with VMI at 140 keV with/without MAR and conventional CT with MBIR as compared to the images obtained using other methods (all, P < .05), except between VMI at 140 keV without MAR and conventional CT with MAR. The location parameter to evaluate fine streak artifacts was significantly reduced in CT images obtained using MBIR and in images obtained by VMI at 140 keV with/without MAR as compared to those obtained using other reconstruction methods (all, P < .01). In our present study, MAR appeared to be the most effective reconstruction method for reducing dark bands in CT images, and MBIR and VMI at 140 keV appeared to the most effective for reducing streak artifacts.

PurposeThe purpose of the study is to evaluate the effect of energy level on the modulation transfer functions (MTF) and noise power spectra (NPS) of virtual monochromatic images (VMIs) obtained using four types of computed-tomographic (CT) scanners: Revolution, SOMATOM, IQon, and Aquilion. Materials and methods VMIs were obtained at 70, 60, and 50 kiloelectron volts (keV), and also at the lowest keV available in each scanner. We evaluated the MTF and NPS in the VMIs obtained at each keV. Results No significant effect of the energy level on the MTF was observed in IQon, whereas the spatial resolution decreased as the energy level decreased in the other types of scanners. The NPS curves tended to increase as the energy levels decreased with three types of scanners other than Aquilion. ConclusionThe spatial resolution and noise frequency characteristics of VMIs may be affected by the energy level, and the effects of energy level on these characteristics differ depending on the type of CT scanners.

Objective: This study aimed to compare the performance of deep learning image reconstruction (DLIR) with that of standard filtered back projection (FBP) and adaptive statistical iterative reconstruction V (ASiR-V) for measurement of the vascular diameter on computed tomography (CT) angiography model. Methods:We used 6 vascular models of 3 wall thicknesses. We used DLIR, FBP, and ASiR-V for reconstruction, and compared the accuracy and precision of vascular diameter measurement, as well as the image noise, among the 3 reconstruction methods.Results: Image noise was in the order of FBP > ASiR-V > DLIR. The vascular diameters measured using DLIR and ASiR-V were comparable with, or significantly closer to, the actual diameter than those measured using FBP. The precision of the diameter measurement using DLIR was comparable with or significantly superior to that using FBP/ASiR-V.Conclusions: Use of DLIR, as compared with FBP or ASiR-V, for image reconstruction can improve the precision and accuracy of vascular diameter measurement.