ArticleInsights into imaging2022
Application of deep learning reconstruction of ultra-low-dose abdominal CT in the diagnosis of renal calculi.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis.Abdominal radiology (New York) · 2023Pooled it
- Evaluation of image quality between hybrid iterative reconstruction and deep learning reconstruction in low dose abdominopelvic CT in low body mass index individuals.Scientific reports · 2026Article
- Evaluation of a super-resolution deep learning reconstruction algorithm in abdominal CT imaging-A qualitative and quantitative performance analysis.Journal of applied clinical medical physics · 2026Article
- Article
- Quantitative and qualitative assessment of ultra-low-dose paranasal sinus CT using deep learning image reconstruction: a comparison with hybrid iterative reconstruction.European radiology · 2025Article
- Can intraoperative endoscopic evaluation replace postoperative low-dose CT for assessing the stone-free status in vacuum-assisted dedusting lithotripsy.World journal of urology · 2025Article
- Agreement between Routine-Dose and Lower-Dose CT with and without Deep Learning-based Denoising for Active Surveillance of Solid Small Renal Masses: A Multiobserver Study.Radiology. Imaging cancer · 2025Article
- Photon-counting versus energy-integrating CT of abdomen-pelvis: a phantom study on the potential for reducing iodine contrast media.European radiology experimental · 2025Article
- Detectability of Hypoattenuating Liver Lesions with Deep Learning CT Reconstruction: A Phantom and Patient Study.Radiology · 2024Article
- Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI.European radiology · 2024Article
- Application of deep learning image reconstruction-high algorithm in one-stop coronary and carotid-cerebrovascular CT angiography with low radiation and contrast doses.Quantitative imaging in medicine and surgery · 2024Article
- Dose Optimization Using a Deep Learning Tool in Various CT Protocols for Urolithiasis: A Physical Human Phantom Study.Medicina (Kaunas, Lithuania) · 2023Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 2 countries.
Funding
Abstract
backgroundRenal calculi are a common and recurrent urological disease and are usually detected by CT. In this study, we evaluated the diagnostic capability, image quality, and radiation dose of abdominal ultra-low-dose CT (ULDCT) with deep learning reconstruction (DLR) for detecting renal calculi.
methodsSixty patients with suspected renal calculi were prospectively enrolled. Low-dose CT (LDCT) images were reconstructed with hybrid iterative reconstruction (LD-HIR) and was regarded as the standard for stone and lesion detection. ULDCT images were reconstructed with HIR (ULD-HIR) and DLR (ULD-DLR). We then compared stone detection rate, abdominal lesion detection rate, image quality and radiation dose between LDCT and ULDCT.
resultsA total of 130 calculi were observed on LD-HIR images. Stone detection rates of ULD-HIR and ULD-DLR images were 93.1% (121/130) and 95.4% (124/130). A total of 129 lesions were detected on the LD-HIR images. The lesion detection rate on ULD-DLR images was 92.2%, with 10 cysts < 5 mm in diameter missed. The CT values of organs on ULD-DLR were similar to those on LD-HIR and lower than those on ULD-HIR. Signal-to-noise ratio was highest and noise lowest on ULD-DLR. The subjective image quality of ULD-DLR was similar to that of LD-HIR and better than that of ULD-HIR. The effective radiation dose of ULDCT (0.64 ± 0.17 mSv) was 77% lower than that of LDCT (2.75 ± 0.50 mSv).
conclusionULDCT combined with DLR could significantly reduce radiation dose while maintaining suitable image quality and stone detection rate in the diagnosis of renal calculi.
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