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Exploring the Low-Dose Limit for Focal Hepatic Lesion Detection with a Deep Learning-Based CT Reconstruction Algorithm: A Simulation Study on Patient Images

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Abstract

This study aims to investigate the maximum achievable dose reduction for applying a new deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in computed tomography (CT) for hepatic lesion detection. A total of 40 patients with 98 clinically confirmed hepatic lesions were retrospectively included. The mean volume CT dose index was 13.66 ± 1.73 mGy in routine-dose portal venous CT examinations, where the images were originally obtained with hybrid iterative reconstruction (HIR). Low-dose simulations were performed in projection domain for 40%-, 20%-, and 10%-dose levels, followed by reconstruction using both HIR and AIIR. Two radiologists were asked to detect hepatic lesion on each set of low-dose image in separate sessions. Qualitative metrics including lesion conspicuity, diagnostic confidence, and overall image quality were evaluated using a 5-point scale. The contrast-to-noise ratio (CNR) for lesion was also calculated for quantitative assessment. The lesion CNR on AIIR at reduced doses were significantly higher than that on routine-dose HIR (all p < 0.05). Lower qualitative image quality was observed as the radiation dose reduced, while there were no significant differences between 40%-dose AIIR and routine-dose HIR images. The lesion detection rate was 100%, 98% (96/98), and 73.5% (72/98) on 40%-, 20%-, and 10%-dose AIIR, respectively, whereas it was 98% (96/98), 73.5% (72/98), and 40% (39/98) on the corresponding low-dose HIR, respectively. AIIR outperformed HIR in simulated low-dose CT examinations of the liver. The use of AIIR allows up to 60% dose reduction for lesion detection while maintaining comparable image quality to routine-dose HIR.

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Data Availability

All the data are available upon reasonable request to the corresponding authors.

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Funding

This work was supported by the National Key Research and Development Program of China (2021YFF0501504); and West China Hospital “1·3·5” Discipline of Excellence Project (ZYGD18019).

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Author notes

  1. Wanjiang Li and Zhenlin Li contributed equally as co-corresponding authors to this work.

Authors and Affiliations

  1. Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

    Yongchun You, Yuting Wen, Dian Guo, Wanjiang Li & Zhenlin Li

  2. United Imaging Healthcare, Shanghai, China

    Sihua Zhong & Guozhi Zhang

Authors
  1. Yongchun You
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  2. Sihua Zhong
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  3. Guozhi Zhang
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  4. Yuting Wen
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  7. Zhenlin Li
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Contributions

Conceptualization and study design: Zhenlin Li and Guozhi Zhang; data collection: Yongchun You, Yuting Wen, Dian Guo and Wanjiang Li; statistical analysis and data interpretation: Yongchun You, Sihua Zhong, Wanjiang Li; manuscript preparation: Yongchun You, Sihua Zhong and Guozhi Zhang; all authors read and approved the final manuscript.

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Correspondence to Wanjiang Li or Zhenlin Li.

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You, Y., Zhong, S., Zhang, G. et al. Exploring the Low-Dose Limit for Focal Hepatic Lesion Detection with a Deep Learning-Based CT Reconstruction Algorithm: A Simulation Study on Patient Images. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01080-3

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