ArticleThe British journal of radiology2021
Low-dose CT urography using deep learning image reconstruction: a prospective study for comparison with conventional CT urography.
Article in The British journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 27 citations in OpenAlex.
- Feasibility of reducing radiation dose and contrast dose while improving image quality in dual-energy CT pulmonary angiography : Use of low-energy virtual monochromatic images with deep learning image reconstruction algorithm.Radiologie (Heidelberg, Germany) · 2026Article
- Low-dose corticomedullary phase CT urography with artificial intelligence iterative reconstruction for bladder cancer evaluation.The British journal of radiology · 2026Article
- Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond.Abdominal radiology (New York) · 2026Review
- Deep learning-based reconstruction improves the image quality of low-dose CT enterography in patients with inflammatory bowel disease.Abdominal radiology (New York) · 2025Article
- Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease.BMC medical imaging · 2025Article
- Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations: a real-world clinical analysis.European radiology · 2025Article
- Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current.BMC medical informatics and decision making · 2024Article
- Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.Journal of applied clinical medical physics · 2024Article
- Evaluation of a Vendor-Agnostic Deep Learning Model for Noise Reduction and Image Quality Improvement in Dental CBCT.Diagnostics (Basel, Switzerland) · 2024Article
- Noise-Optimized CBCT Imaging of Temporomandibular Joints-The Impact of AI on Image Quality.Journal of clinical medicine · 2024Article
- Development and validation of a fully automated system using deep learning for opportunistic osteoporosis screening using low-dose computed tomography scans.Quantitative imaging in medicine and surgery · 2023Article
- Computed Tomography Urography: State of the Art and Beyond.Tomography (Ann Arbor, Mich.) · 2023Review
- Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects.Radiology · 2023Review
- Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease.European radiology · 2022Article
- The Value of Deep Learning Image Reconstruction in Improving the Quality of Low-Dose Chest CT Images.Diagnostics (Basel, Switzerland) · 2022Article
- Radiation and iodine dose reduced thoraco-abdomino-pelvic dual-energy CT at 40 keV reconstructed with deep learning image reconstruction.The British journal of radiology · 2022Article
- Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography.European radiology · 2022Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo compare the image quality of low-dose CT urography (LD-CTU) using deep learning image reconstruction (DLIR) with conventional CTU (C-CTU) using adaptive statistical iterative reconstruction (ASIR-V).
methodsThis was a prospective, single-institutional study using the excretory phase CTU images for analysis. Patients were assigned to the LD-DLIR group (100kV and automatic mA modulation for noise index (NI) of 23) and C-ASIR-V group (100kV and NI of 10) according to the scan protocols in the excretory phase. Two radiologists independently assessed the overall image quality, artifacts, noise and sharpness of urinary tracts. Additionally, the mean CT attenuation, signal-to-noise ratio (SNR) and contrast-to-noise (CNR) in the urinary tracts were evaluated.
results26 patients each were included in the LD-DLIR group (10 males and 16 females; mean age: 57.23 years, range: 33-76 years) and C-ASIR-V group (14 males and 12 females; mean age: 60 years, range: 33-77 years). LD-DLIR group used a significantly lower effective radiation dose compared with the C-ASIR-V group (2.01 ± 0.44 mSv
conclusionIt is feasibility to provide comparable image quality while reducing 71% radiation dose in low-dose CTU with a deep learning image reconstruction algorithm compared to the conventional CTU with ASIR-V. ADVANCES IN KNOWLEDGE: (1) CT urography with deep learning reconstruction algorithm can reduce the radiation dose by 71% while still maintaining image quality.
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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.