SynthesisEuropean radiology2022
The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis.
Synthesis in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Photon-counting CT versus energy-integrating detectors for cardiac imaging: a systematic review of evidence from in vivo human studies on image quality and radiation dose.BMC medical imaging · 2025Pooled it
- Pooled it
- Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis.Abdominal radiology (New York) · 2023Pooled it
- Deep Learning Reconstruction Versus Hybrid Iterative Reconstruction for Acute Cerebral Infarction Detection on 135 kVp Non-Contrast Brain CT.Clinical neuroradiology · 2026Article
- Improved delineation of the cystic artery using super-resolution deep learning reconstruction in contrast-enhanced abdominal computed tomography.Radiological physics and technology · 2026Article
- Evaluation of gastric cancer using artificial intelligence iterative reconstruction on abdominal CT: image quality and diagnostic accuracy.Abdominal radiology (New York) · 2026Article
- Impact of Super-Resolution Deep Learning Reconstruction on Low-Dose CT in Patients with Central Venous Catheter or Central Venous Port.Journal of imaging informatics in medicine · 2026Article
- Obese patient imaging: Potential dose reduction with photon-counting CT.Journal of applied clinical medical physics · 2026Article
- The impact of a novel deep learning reconstruction algorithm on image quality in ultralow-dose CT: a quantitative phantom study.European radiology experimental · 2026Article
- Diagnostic value of a second-generation super-resolution deep learning-based reconstruction combined with a metal artifact reduction algorithm for pelvic CT.Skeletal radiology · 2026Article
- Deep learning 3D super-resolution radiomics model based on Gd-enhanced MRI for improving preoperative prediction of HCC pathological grading.Abdominal radiology (New York) · 2026Article
- Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond.Abdominal radiology (New York) · 2026Review
- Dual-low spectral CT pulmonary angiography: a comparative study of image quality, radiation dose, and iodine intake with evaluation of pulmonary embolism detection.Frontiers in medicine · 2026Article
- Feasibility of deep learning-based image reconstruction using TrueFidelity for coronary calcium scoring.PloS one · 2026Article
- Obese patient imaging: improved dose efficiency with photon-counting CT.medRxiv : the preprint server for health sciences · 2025Article
- Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction algorithm.Japanese journal of radiology · 2025Article
- Detection of intracranial hemorrhage using ultralow-dose brain computed tomography with deep learning reconstruction versus conventional-dose computed tomography.BMC medical imaging · 2025Article
- The application of deep learning-based artificial intelligence algorithms combined with low-dose scanning protocols in chest CT.Quantitative imaging in medicine and surgery · 2025Article
- Advantages of deep learning reconstruction algorithm in ultra-high-resolution CT for the diagnosis of pancreatic cystic neoplasm.Japanese journal of radiology · 2025Article
- Deep learning reconstruction enhances image quality in contrast-enhanced CT venography for deep vein thrombosis.Emergency radiology · 2025Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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 POINTS: CT 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.