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Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction

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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objective

To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images.

Methods

In this Institutional review board approved prospective study, 86 participants who underwent DECTE were enrolled. The early-enteric phase scan was performed using standard-dose (noise index: 8) and images were reconstructed at 5 mm and 1.25 mm slice thickness with ASIR-V at a level of 40% (ASIR-V40%). The late-enteric phase scan used low-dose (noise index: 12) and images were reconstructed at 1.25 mm slice thickness with ASIR-V40%, and DLIR at medium (DLIR-M) and high (DLIR-H). The 70 keV monochromatic images were used for image comparison and analysis. For objective assessment, image noise, artifact index, SNR and CNR were measured. For subjective assessment, subjective noise, image contrast, bowel wall sharpness, mesenteric vessel clarity, and small structure visibility were scored by two radiologists blindly. Radiation dose was compared between the early- and late-enteric phases.

Results

Radiation dose was reduced by 50% in the late-enteric phase [(6.31 ± 1.67) mSv] compared with the early-enteric phase [(3.01 ± 1.09) mSv]. For the 1.25 mm images, DLIR-M and DLIR-H significantly improved both objective and subjective image quality compared to those with ASIR-V40%. The low-dose 1.25 mm DLIR-H images had similar image noise, SNR, CNR values as the standard-dose 5 mm ASIR-V40% images, but significantly higher scores in image contrast [5(5–5), P < 0.05], bowel wall sharpness [5(5–5), P < 0.05], mesenteric vessel clarity [5(5–5), P < 0.05] and small structure visibility [5(5–5), P < 0.05].

Conclusions

DLIR significantly reduces image noise at the same slice thickness, but significantly improves spatial resolution and lesion conspicuity with thinner slice thickness in DECTE, compared to conventional ASIR-V40% 5 mm images, all while providing 50% radiation dose reduction.

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Author notes

  1. Xu Lin and Yankun Gao equally contributed to this work.

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China

    Xu Lin, Yankun Gao, Chao Zhu, Jian Song & Xingwang Wu

  2. CT Research Center, GE Healthcare China, Shanghai, 210000, China

    Ling Liu & Jianying Li

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  1. Xu Lin
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  5. Ling Liu
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Contributions

Xu Lin: the acquisition of data, analysis of data, and drafting the article. Yankun Gao: the acquisition of data, analysis of data. Chao Zhu: the acquisition of data. Jian Song: the acquisition of data. Ling Liu: the analysis and interpretation of data. Jianying Li: the analysis and interpretation of data. Xingwang Wu: final approval of the version to be submitted.

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Correspondence to Xingwang Wu.

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Lin, X., Gao, Y., Zhu, C. et al. Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04221-y

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