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Characteristics of the deep learning-based virtual monochromatic image with fast kilovolt-switching CT: a phantom study

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Abstract

Purpose

We assessed the physical properties of virtual monochromatic images (VMIs) obtained with different energy levels in various contrast settings and radiation doses using deep learning-based spectral computed tomography (DL-Spectral CT) and compared the results with those from single-energy CT (SECT) imaging.

Materials and methods

A Catphan® 600 phantom was scanned by DL-Spectral CT at various radiation doses. We reconstructed the VMIs obtained at 50, 70, and 100 keV. SECT (120 kVp) images were acquired at the same radiation doses. The standard deviations of the CT number and noise power spectrum (NPS) were calculated for noise characterization. We evaluated the spatial resolution by determining the 10% task-based transfer function (TTF) level, and we assessed the task-based detectability index (d’).

Results

Regardless of the radiation dose, the noise was the lowest at 70 keV VMI. The NPS showed that the noise amplitude at all spatial frequencies was the lowest among other VMI and 120 kVp images. The spatial resolution was higher for 70 keV VMI compared to the other VMIs, except for high-contrast objects. The d’ of 70 keV VMI was the highest among the VMI and 120 kVp images at all radiation doses and contrast settings. The d’ of the 70 keV VMIs at the minimum dose was higher than that at the maximum dose in any other image.

Conclusion

The physical properties of the DL-Spectral CT VMIs varied with the energy level. The 70 keV VMI had the highest detectability by far among the VMI and 120-kVp images. DL-Spectral CT may be useful to reduce radiation doses.

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Authors and Affiliations

  1. Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan

    Yuna Katsuyama, Tsukasa Kojima, Takashi Shirasaka, Masatoshi Kondo & Toyoyuki Kato

  2. Department of Health Sciences, Graduate school of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, Japan

    Tsukasa Kojima

Authors
  1. Yuna Katsuyama
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  2. Tsukasa Kojima
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  3. Takashi Shirasaka
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  4. Masatoshi Kondo
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  5. Toyoyuki Kato
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Corresponding author

Correspondence to Yuna Katsuyama.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Katsuyama, Y., Kojima, T., Shirasaka, T. et al. Characteristics of the deep learning-based virtual monochromatic image with fast kilovolt-switching CT: a phantom study. Radiol Phys Technol 16, 77–84 (2023). https://doi.org/10.1007/s12194-022-00695-x

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  • DOI: https://doi.org/10.1007/s12194-022-00695-x

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