Why the study?
The study sought to investigate the impact of combining high-resolution scan mode with deep learning image reconstruction algorithms in CT.
Does combining high-resolution scan mode with deep learning image reconstruction improve spatial resolution, noise, and contrast-to-noise ratio in cardiac CT phantom models?
Does combining high-resolution scan mode with deep learning image reconstruction improve spatial resolution, noise, and contrast-to-noise ratio in cardiac CT phantom models?
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Combining high-resolution scan mode with deep learning image reconstruction in cardiac CT improves spatial resolution while effectively mitigating the associated increase in image noise.
To investigate the impact of combining the high-resolution (Hi-res) scan mode with deep learning image reconstruction (DLIR) algorithm in CT. Two phantoms (Catphan600® and Lungman, small, medium, large size) were CT scanned using combinations of Hi-res/standard mode and high-definition (HD)/standard kernels. Images were reconstructed with ASiR-V and three levels of DLIR. Spatial resolution, noise and contrast-to-noise ratio (CNR) were assessed. The radiation dose was recorded. The spatial resolution increased using Hi-res & HD. Image noise in the Catphan600® (69%) and the Lungman (10-70%) significantly increased when Hi-res & HD was applied. DLIR reduced the mean noise (54%). The CNR was reduced (64%) for Hi-res & HD. The radiation dose increased for both small (+70%) and medium (+43%) Lungman phantoms but decreased slightly for the large ones (-3%) when Hi-res was applied. In conclusion, the Hi-res scan mode improved the spatial resolution. The HD kernel significantly increased the image noise. DLIR improved the image noise and CNR and did not affect the spatial resolution.
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Mørup et al. (2022) studied this question.
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