Abstract
ObjectiveTo 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).MethodsPubMed 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.ResultsSixteen 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.ConclusionsThere 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.Key PointsCT values of abdominal CT images are similar between deep learning reconstruction (DLR), filtered back-projection (FBP), and hybrid iterative reconstruction (IR).DLR results in improved image quality in terms of SNR and CNR compared to FBP and hybrid IR images.DLR can thus be safely implemented in the clinical setting resulting in improved image quality without affecting CT values.
Highlights
Computed tomography (CT) acquires images of tissues inside the human body
9 studies were excluded due to the lack of Hounsfield unit (HU), signal-to-noise ratio (SNR), or contrast-to-noise ratio (CNR) as an outcome measure, and 3 studies excluded because no deep learning reconstruction (DLR) on abdominal organs was described
The average CT values for liver, spleen, and fat tissue obtained by the three reconstruction techniques (FBP, iterative reconstruction (IR), and DLR) were within ± 0.3 HU and ± 1.3% [24]
Summary
Computed tomography (CT) acquires images of tissues inside the human body. Photons, emitted by an X-ray tube, interact with human tissue and either get absorbed due to the photoelectric effect or get scattered due to Compton scattering. To reconstruct attenuation measurements by the CT scanner into HUs and display these in a comprehensible image suitable for clinical diagnosis, a method for iterative data reconstruction was proposed This technique simulated the CT system, and based upon this model, it iteratively adjusted measured data for various factors influencing the attenuation measurements as e.g., estimated dose or noise effects. A new reconstruction technique got introduced that allowed for low-dose CT measurements while maintaining image quality in terms of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) [2]. This reconstruction technique resembles the initially proposed IR method, but is a combination of FBP and full IR, called hybrid IR. We compared the image quality of these three reconstruction techniques
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