The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis

J.A. van Stiphout*, J. Driessen, L.R. Koetzier, L.B. Ruules, Martin Willemink, Jan W.T. Heemskerk, Aart J. van der Molen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Objective
To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR).

Methods
PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image reconstruction techniques FBP, hybrid IR, and DLR. Mean differences in CT values between reconstruction techniques were analyzed. A comparison between signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of FBP, hybrid IR, and DLR was made. A comparison of diagnostic confidence between hybrid IR and DLR was made.

Results
Sixteen articles were included, six being suitable for meta-analysis. In the liver, the mean difference between hybrid IR and DLR was − 0.633 HU (p = 0.483, SD ± 0.902 HU). In the spleen, the mean difference between hybrid IR and DLR was − 0.099 HU (p = 0.925, SD ± 1.061 HU). In the pancreas, the mean difference between hybrid IR and DLR was − 1.372 HU (p = 0.353, SD ± 1.476 HU). In 14 articles, CNR was described. In all cases, DLR showed a significantly higher CNR. In 9 articles, SNR was described. In all cases but one, DLR showed a significantly higher SNR. In all cases, DLR showed a significantly higher diagnostic confidence.

Conclusions
There were no significant differences in CT values reconstructed by FBP, hybrid IR, and DLR in abdominal organs. This shows that these reconstruction techniques are consistent in reconstructing CT values. DLR images showed a significantly higher SNR and CNR, compared to FBP and hybrid IR.
Original languageEnglish
Number of pages9
JournalEuropean Radiology
DOIs
Publication statusPublished - 2021

Keywords

  • Tomography
  • x-ray computed
  • Abdomen
  • Image processing, computer-assisted
  • Deep learning

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