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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Background

Abdominal multi-slice helical computed tomography (CT) and contrast-enhanced scanning have been widely recognized clinically.

Objective

The impact of the deep learning image reconstruction (DLIR) on the quality of dynamic contrast-enhanced CT imaging of primary liver cancer lesions was evaluated through comparison with the filtered back projection (FBP) and the new generation of adaptive statistical iterative reconstruction-V (ASIR-V).

Methods

We evaluated the image noise of the lesion, fine structures inside the lesion, and diagnostic confidence in 48 liver cancer subjects. The CT values of the solid part of the lesion and the adjacent normal liver tissue and the systolic and diastolic blood pressure (SD) values of the right paravertebral muscle were measured. The muscle SD value was considered as the background noise of the image, and the signal noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion and normal liver parenchyma were calculated.

Results

High consistency in the evaluation of image noise (Kappa = 0.717). The Kappa values for margin/pseudocapsule, fine structure within the lesion, and diagnostic confidence were 0.463, 0.527, and 0.625, respectively. Besides, the differences in SD, SNR and CNR data of reconstructed lesion images among the six groups were statistically significant.

Conclusion

The contrast-enhanced CT image noise of DLIR-H in the portal venous phase is much lower than that of ASIR-V and FBP in primary liver cancer patients. In terms of the lesion structure display, the new reconstruction algorithm DLIR is superior.

© 2024 Han et al.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
2025-06-20

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/content/journals/cmir/10.2174/0115734056261849231207055304
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An Evaluation Analysis for Computed Tomography Image Quality of Primary Liver Cancer Lesions Based on Deep Learning Image Reconstruction
Curr. Med. Imaging 20, e15734056261849 (2024); https://doi.org/10.2174/0115734056261849231207055304
/content/journals/cmir/10.2174/0115734056261849231207055304
/content/journals/cmir/10.2174/0115734056261849231207055304
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  • Article Type:     Research Article
Keyword(s): ASIR-V; Contrast-enhanced computed tomography; Deep learning image reconstruction (DLIR); Filtered back projection (FBP); Iterative reconstruction; Liver cancer

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