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Physical and visual evaluations of CT image quality of large low-contrast objects with visual model-based iterative reconstruction technique: a phantom study

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Abstract

We aimed to verify whether the image quality of large low-contrast objects can be improved using visual model-based iterative reconstruction (VMR) while maintaining the visibility of conventional filtered back projection (FBP) and reducing radiation dose through physical and visual evaluation. A 64-row multi-slice CT system with SCENARIA View (FUJIFILM healthcare Corp. Tokyo, Japan) was used. The noise power spectrum (NPS), task-based transfer function (TTF), and signal-to-noise ratio (SNR) were physically evaluated. A low contrast object as a substitute for a liver mass was visually evaluated. In the noise measurement, STD1 showed an 18% lower noise compared to FBP. STR4 was able to reduce noise by 58% compared to FBP. The NPS of VMR was similar to those of FBP from low to high spatial frequency. The NPS of VMR reconstructions showed a similar variation with frequency as FBP reconstructions. STD1 showed the highest 10% TTF, and higher 10% TTF was observed with lower VMR level. The SNR of VMR was close to that of FBP, and higher SNR was observed with higher VMR level. In the results of the visual evaluation, there was no significant difference in visual evaluation between STD1 and FBP (p = 0.99) and between STD2 and FBP (p = 0.56). We found that the NPS of VMR images was similar to that of FBP images, and it can reduce noise and radiation dose by 25% and 50%, respectively, without decreasing the visual image quality compared to FBP.

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There are no grant support with the CT scanner manufacturer in this study.

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Correspondence to Hideki Shibata.

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There are no conflicts of interest with the CT scanner manufacturer in this study.

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Shibata, H., Matsubara, K., Asada, Y. et al. Physical and visual evaluations of CT image quality of large low-contrast objects with visual model-based iterative reconstruction technique: a phantom study. Phys Eng Sci Med 46, 141–150 (2023). https://doi.org/10.1007/s13246-022-01205-4

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