Comparative Effects of Model-Based and Adaptive Statistical Iterative Reconstruction Algorithms on Image Quality Metrics in Low-Dose Chest Computed Tomography for Pediatric Pneumonia: A Prospective Study

Background: Pneumonia is a common respiratory disease in children, frequently requiring imaging for diagnosis and treatment evaluation. Optimizing image quality is crucial to ensure diagnostic accuracy while minimizing radiation exposure. Comparing advanced image reconstruction algorithms can help achieve this balance. Objectives: We aimed to compare the effects of the model-based iterative reconstruction (MBIR) algorithm and the adaptive statistical iterative reconstruction (ASIR) algorithm on the noise and overall image quality of low-dose chest computed tomography (CT) for pediatric pneumonia. Patients and Methods: This was a prospective study. A total of 102 children diagnosed with pneumonia between June 2023 and May 2024 were recruited. All of them underwent a low-dose chest CT scan, and then MBIR and ASIR algorithms were employed for image reconstruction using a simple randomization method (n = 51 in each group). The noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the reconstructed images were measured and compared. Subjective evaluation of the image quality was performed by two experienced radiologists. The agreement between them was evaluated based on the Kappa value. Results: The two radiologists provided highly consistent subjective evaluation results for the reconstructed images (Kappa value of 0.85, 95% confidence interval: 0.80 - 0.90). The reconstructed images in the MBIR group were better than those in the ASIR group in terms of image noise, microstructure display, definition of lesion edges, and overall image quality (P < 0.05). The dose-length product and effective dose decreased in the MBIR group compared with those in the ASIR group (P < 0.05). The SNR and CNR of the lung field, main pulmonary artery, and muscle were higher in the MBIR group than in the ASIR group (P < 0.05). Conclusion: The MBIR algorithm shows more promise than the ASIR algorithm in reducing noise, improving SNR, and maintaining good image quality of low-dose chest CT scans for children with pneumonia, without increasing the radiation dose. Further research may be needed to confirm its broader applicability.

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Publication Details

Journal
Iranian Journal of Radiology
Published
2024-10-31
DOI
https://doi.org/10.5812/iranjradiol-158779
Primary Topic
Medical Imaging Techniques and Applications
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article

Comparative Effects of Model-Based and Adaptive Statistical Iterative Reconstruction Algorithms on Image Quality Metrics in Low-Dose Chest Computed Tomography for Pediatric Pneumonia: A Prospective Study

S. Kevin Zhou, Weigen Wang, Jing He, Ruirui Chen et al.
Iranian Journal of Radiology
Medical Imaging Techniques and Applications
article

Comparative Effects of Model-Based and Adaptive Statistical Iterative Reconstruction Algorithms on Image Quality Metrics in Low-Dose Chest Computed Tomography for Pediatric Pneumonia: A Prospective Study

S. Kevin Zhou, Weigen Wang, Jing He, Ruirui Chen, Yizhai Ye, M. Ge
article en

Abstract

Background: Pneumonia is a common respiratory disease in children, frequently requiring imaging for diagnosis and treatment evaluation. Optimizing image quality is crucial to ensure diagnostic accuracy while minimizing radiation exposure. Comparing advanced image reconstruction algorithms can help achieve this balance. Objectives: We aimed to compare the effects of the model-based iterative reconstruction (MBIR) algorithm and the adaptive statistical iterative reconstruction (ASIR) algorithm on the noise and overall image quality of low-dose chest computed tomography (CT) for pediatric pneumonia. Patients and Methods: This was a prospective study. A total of 102 children diagnosed with pneumonia between June 2023 and May 2024 were recruited. All of them underwent a low-dose chest CT scan, and then MBIR and ASIR algorithms were employed for image reconstruction using a simple randomization method (n = 51 in each group). The noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the reconstructed images were measured and compared. Subjective evaluation of the image quality was performed by two experienced radiologists. The agreement between them was evaluated based on the Kappa value. Results: The two radiologists provided highly consistent subjective evaluation results for the reconstructed images (Kappa value of 0.85, 95% confidence interval: 0.80 - 0.90). The reconstructed images in the MBIR group were better than those in the ASIR group in terms of image noise, microstructure display, definition of lesion edges, and overall image quality (P < 0.05). The dose-length product and effective dose decreased in the MBIR group compared with those in the ASIR group (P < 0.05). The SNR and CNR of the lung field, main pulmonary artery, and muscle were higher in the MBIR group than in the ASIR group (P < 0.05). Conclusion: The MBIR algorithm shows more promise than the ASIR algorithm in reducing noise, improving SNR, and maintaining good image quality of low-dose chest CT scans for children with pneumonia, without increasing the radiation dose. Further research may be needed to confirm its broader applicability.

Iranian Journal of RadiologyVol. 21(4)
Openalex Percentile: Top 55%
Medical Imaging Techniques and Applications
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