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Article

Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms

by
Emilio Quaia
*,
Elena Kiyomi Lanza de Cristoforis
,
Elena Agostini
and
Chiara Zanon
Department of Radiology, University of Padova, Via Giustiniani 2, 35128 Padova, Italy
*
Author to whom correspondence should be addressed.
Tomography 2024, 10(6), 912-921; https://doi.org/10.3390/tomography10060069
Submission received: 6 May 2024 / Revised: 5 June 2024 / Accepted: 6 June 2024 / Published: 7 June 2024

Abstract

Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and improves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently the FBP or IR algorithm (Advanced Modeled Iterative Reconstruction [ADMIRE] model-based algorithm or Adaptive Iterative Dose Reduction 3D [AIDR 3D] hybrid algorithm) for CT image reconstruction. The radiation ED, noise level, and signal-to-noise ratio (SNR) were compared between the different CT scanners. The non-parametric Wilcoxon test was used for statistical comparison. Statistical significance was set at p < 0.05. A total of 83 patients (mean age, 59 ± 15 years [standard deviation]; 56 men) were included. DLIR vs. FBP reduced the ED (18.45 ± 13.16 mSv vs. 22.06 ± 9.55 mSv, p < 0.05), while DLIR vs. FBP and vs. ADMIRE and AIDR 3D IR algorithms reduced image noise (8.45 ± 3.24 vs. 14.85 ± 2.73 vs. 14.77 ± 32.77 and 11.17 ± 32.77, p < 0.05) and increased the SNR (11.53 ± 9.28 vs. 3.99 ± 1.23 vs. 5.84 ± 2.74 and 3.58 ± 2.74, p < 0.05). CT scanners employing DLIR improved the SNR compared to CT scanners using FBP or IR algorithms in ICU patients despite maintaining a reduced ED.
Keywords: CT; intensive care; reconstruction; algorithms; deep learning CT; intensive care; reconstruction; algorithms; deep learning

Share and Cite

MDPI and ACS Style

Quaia, E.; Kiyomi Lanza de Cristoforis, E.; Agostini, E.; Zanon, C. Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms. Tomography 2024, 10, 912-921. https://doi.org/10.3390/tomography10060069

AMA Style

Quaia E, Kiyomi Lanza de Cristoforis E, Agostini E, Zanon C. Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms. Tomography. 2024; 10(6):912-921. https://doi.org/10.3390/tomography10060069

Chicago/Turabian Style

Quaia, Emilio, Elena Kiyomi Lanza de Cristoforis, Elena Agostini, and Chiara Zanon. 2024. "Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms" Tomography 10, no. 6: 912-921. https://doi.org/10.3390/tomography10060069

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