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

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

SUBMITTER: van Stiphout JA 

PROVIDER: S-EPMC9038933 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature