ArticleEuropean radiology2021
Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 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
- Detection of hypodense hepatic and renal lesions on abdominal CT reconstructed with deep learning image reconstruction technique in patients with large body habitus: A multi-reader study.Abdominal radiology (New York) · 2026Article
- Effect of deep learning reconstruction on arm-induced artifacts compared with hybrid iterative reconstruction and filtered-backprojection in abdominal CT.Radiological physics and technology · 2026Article
- Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond.Abdominal radiology (New York) · 2026Review
- Image Quality Optimization in 60 kVp Head-Neck CTA: A Comparative Study of FBP, ClearView, and ClearInfinity Reconstruction Algorithms.Current medical science · 2025Article
- Article
- State-of-the-Art Deep Learning CT Reconstruction Algorithms in Abdominal Imaging.Radiographics : a review publication of the Radiological Society of North America, Inc · 2024Review
- Detectability of Hypoattenuating Liver Lesions with Deep Learning CT Reconstruction: A Phantom and Patient Study.Radiology · 2024Article
- Systematic Review on Learning-based Spectral CT.ArXiv · 2024Article
- Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction.Abdominal radiology (New York) · 2024Article
- Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms.Tomography (Ann Arbor, Mich.) · 2024Article
- Insights about cervical lymph nodes: Evaluating deep learning-based reconstruction for head and neck computed tomography scan.European journal of radiology open · 2024Article
- Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT.Radiology. Artificial intelligence · 2024Article
- Systematic Review on Learning-based Spectral CT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?Abdominal radiology (New York) · 2023Article
- Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction.Cell reports. Medicine · 2023Article
- Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography.La Radiologia medica · 2023Article
- CT image quality evaluation in the age of deep learning: trade-off between functionality and fidelity.European radiology · 2023Article
- Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT.European radiology · 2023Article
- The value of using a deep learning image reconstruction algorithm of thinner slice thickness to balance the image noise and spatial resolution in low-dose abdominal CT.Quantitative imaging in medicine and surgery · 2023Article
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6 authors.
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Abstract
objectivesTo investigate the image quality and perception of a sinogram-based deep learning image reconstruction (DLIR) algorithm for single-energy abdominal CT compared to standard-of-care strength of ASIR-V.
methodsIn this retrospective study, 50 patients (62% F; 56.74 ± 17.05 years) underwent portal venous phase. Four reconstructions (ASIR-V at 40%, and DLIR at three strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H)) were generated. Qualitative and quantitative image quality analysis was performed on the 200 image datasets. Qualitative scores were obtained for image noise, contrast, small structure visibility, sharpness, and artifact by three blinded radiologists on a 5-point scale (1, excellent; 5, very poor). Radiologists also indicated image preference on a 3-point scale (1, most preferred; 3, least preferred). Quantitative assessment was performed by measuring image noise and contrast-to-noise ratio (CNR).
resultsDLIR had better image quality scores compared to ASIR-V. Scores on DLIR-H for noise (1.40 ± 0.53), contrast (1.41 ± 0.55), small structure visibility (1.51 ± 0.61), and sharpness (1.60 ± 0.54) were the best (p < 0.05) followed by DLIR-M (1.85 ± 0.52, 1.66 ± 0.57, 1.69 ± 0.59, 1.68 ± 0.46), DLIR-L (2.29 ± 0.58, 1.96 ± 0.61, 1.90 ± 0.65, 1.86 ± 0.46), and ASIR-V (2.86 ± 0.67, 2.55 ± 0.58, 2.34 ± 0.66, 2.01 ± 0.36). Ratings for artifacts were similar for all reconstructions (p > 0.05). DLIRs did not influence subjective textural perceptions and were preferred over ASIR-V from the beginning. All DLIRs had a higher CNR (26.38-102.30%) and lower noise (20.64-48.77%) than ASIR-V. DLIR-H had the best objective scores.
conclusionSinogram-based deep learning image reconstructions were preferred over iterative reconstruction subjectively and objectively due to improved image quality and lower noise, even in large patients. Use in clinical routine may allow for radiation dose reduction. KEY POINTS: • Deep learning image reconstructions (DLIRs) have a higher contrast-to-noise ratio compared to medium-strength hybrid iterative reconstruction techniques. • DLIR may be advantageous in patients with large body habitus due to a lower image noise. • DLIR can enable further optimization of radiation doses used in abdominal CT.
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