ArticleEuropean radiology2023
Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis.Abdominal radiology (New York) · 2023Pooled it
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- Deep Learning Algorithm-Reconstructed Triple-Rule-Out CT: Image Quality and Performance in Evaluating Coronary Lumen Stenosis.Journal of imaging informatics in medicine · 2026Article
- Three-Dimensional Printing in Pediatric Orthopaedics: Cost Analysis and Emerging Applications.Journal of the Pediatric Orthopaedic Society of North America · 2026Review
- Performance of deep-learning reconstruction combined with metal artifact reduction algorithm for dual-energy computed tomography angiography in intracranial aneurysm coil embolization.European journal of radiology open · 2026Article
- Improving image quality and diagnostic confidence for PRETEXT staging in pediatric hepatoblastoma using thin-slice and low-energy virtual monochromatic images in dual-energy CT with deep learning image reconstruction algorithm.BMC medical imaging · 2026Article
- Deep learning-based denoising in cardiac CT: effects on image quality, calcium scoring interchangeability, and reporting workflow.Frontiers in radiology · 2026Article
- Application of 80 kVp combined with deep learning reconstruction algorithm in overweight patients coronary CT angiography: reduced radiation dose and contrast agent dose.Frontiers in radiology · 2026Article
- Impact of Combined Deep Learning Image Reconstruction and Metal Artifact Reduction Algorithm on CT Image Quality in Different Scanning Conditions for Maxillofacial Region with Metal Implants: A Phantom Study.Journal of imaging informatics in medicine · 2025Article
- The application value of deep learning-based reconstruction combined with segmented contrast injection in "one-stop" cardio-cerebrovascular computed tomography angiography.Quantitative imaging in medicine and surgery · 2025Article
- Objective assessment of diagnostic image quality in CT scans: what radiologists and researchers need to know.Insights into imaging · 2025Review
- Image quality improvement in head and neck angiography based on dual-energy CT and deep learning.BMC medical imaging · 2025Article
- Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction.Abdominal radiology (New York) · 2024Article
- Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT.BMC medical imaging · 2024Article
- The application value of a vendor-specific deep learning image reconstruction algorithm in "triple low" head and neck computed tomography angiography.Quantitative imaging in medicine and surgery · 2024Article
- AI-based approaches in the daily practice of abdominal imaging.European radiology · 2024Article
- Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers.European radiology · 2023Article
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10 authors.
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Abstract
objectiveTo evaluate image quality and diagnostic confidence improvement using a thin slice and a deep learning image reconstruction (DLIR) in contrast-enhanced abdominal CT.
methodsForty patients with hepatic lesions in enhanced abdominal CT were retrospectively analyzed. Images in the portal phase were reconstructed at 5 mm and 1.25 mm slice thickness using the 50% adaptive statistical iterative reconstruction (ASIR-V) (ASIR-V50%) and at 1.25 mm using DLIR at medium (DLIR-M) and high (DLIR-H) settings. CT number and standard deviation of the hepatic parenchyma, spleen, portal vein, and subcutaneous fat were measured, and contrast-to-noise ratio (CNR) was calculated. Edge-rise-slope (ERS) was measured on the portal vein to reflect spatial resolution and the CT number skewness on liver parenchyma was calculated to reflect image texture. Two radiologists blindly assessed the overall image quality including subjective noise, image contrast, visibility of small structures using a 5-point scale, and object sharpness and lesion contour using a 4-point scale.
resultsFor the 1.25-mm images, DLIR significantly reduced image noise, improved CNR and overall subjective image quality compared to ASIR-V50%. Compared to the 5-mm ASIR-V50% images, DLIR images had significantly higher scores in the visibility and contour for small structures and lesions; as well as significantly higher ERS and lower CT number skewness. At a quarter of the signal strength, the 1.25-mm DLIR-H images had a similar subjective noise score as the 5-mm ASIR-V50% images.
conclusionDLIR significantly reduces image noise and maintains a more natural image texture; image spatial resolution and diagnostic confidence can be improved using thin slice images and DLIR in abdominal CT. KEY POINTS: • DLIR further reduces image noise compared with ASIR-V while maintaining favorable image texture. • In abdominal CT, thinner slice images improve image spatial resolution and small object visualization but suffer from higher image noise. • Thinner slice images combined with DLIR in abdominal CT significantly suppress image noise for detecting low-density lesions while significantly improving image spatial resolution and overall image quality.
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