ArticleJapanese journal of radiology2021
Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions.
Article in Japanese journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 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
- Deep learning-based motion correction: cardiac motion artifact and image quality improvements on chest CT.Japanese journal of radiology · 2026Article
- Deep Learning Reconstruction Specialized for Inner Ear: Improving Image Quality and Anatomical Structure Visualization as Compared with Conventional Hybrid-Type Iterative Reconstruction on High-Definition CT.Diagnostics (Basel, Switzerland) · 2026Article
- 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 for improved image quality of ultra-high-resolution brain CT angiography: application in moyamoya disease.Japanese journal of radiology · 2025Article
- High-resolution deep learning reconstruction for coronary CTA: compared efficacy of stenosis evaluation with other methods at in vitro and in vivo studies.European radiology · 2025Article
- Improved vascular depiction and image quality through deep learning reconstruction of CT hepatic arteriography during transcatheter arterial chemoembolization.Japanese journal of radiology · 2024Article
- Deep Learning Reconstruction for DWIs by EPI and FASE Sequences for Head and Neck Tumors.Cancers · 2024Article
- Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy.European radiology · 2024Article
- Fairness of artificial intelligence in healthcare: review and recommendations.Japanese journal of radiology · 2024Review
- Measurement of solid size in early-stage lung adenocarcinoma by virtual 3D thin-section CT applied artificial intelligence.Scientific reports · 2023Article
- Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images.Japanese journal of radiology · 2023Article
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation inDiagnostic and interventional radiology (Ankara, Turkey) · 2023Article
- Area-Detector Computed Tomography for Pulmonary Functional Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Clinical acceptance of deep learning reconstruction for abdominal CT imaging: objective and subjective image quality and low-contrast detectability assessment.European radiology · 2022Article
- Novel Intraoperative Navigation Using Ultra-High-Resolution CT in Robot-Assisted Partial Nephrectomy.Cancers · 2022Article
- [Feasibility Study of Ultra-High-Resolution Low-Dose Temporal Bone CT with 1 024×1 024 Reconstruction Matrix Size].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2021Article
- Ultra-high resolution computed tomography of joints: practical recommendations for acquisition protocol optimization.Quantitative imaging in medicine and surgery · 2021Article
- Advanced CT techniques for assessing hepatocellular carcinoma.La Radiologia medica · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo determine the image quality improvement including vascular structures using deep learning reconstruction (DLR) for ultra-high-resolution CT (UHR-CT) and area-detector CT (ADCT) compared to a commercially available hybrid-iterative reconstruction (IR) method. MATERIALS AND
methodThirty-two patients suspected of renal cell carcinoma underwent dynamic contrast-enhanced (CE) CT using UHR-CT or ADCT systems. CT value and contrast-to-noise ratio (CNR) on each CT dataset were assessed with region of interest (ROI) measurements. For qualitative assessment of improvement for vascular structure visualization, each artery was assessed using a 5-point scale. To determine the utility of DLR, CT values and CNRs were compared among all UHR-CT data by means of ANOVA followed by Bonferroni post hoc test, and same values on ADCT data were also compared between hybrid IR and DLR methods by paired t test.
resultsFor all arteries except the aorta, the CT value and CNR of the DLR method were significantly higher compared to those of the hybrid-type IR method in both CT systems reconstructed as 512 or 1024 matrixes (p < 0.05).
conclusionDLR has a higher potential to improve the image quality resulting in a more accurate evaluation for vascular structures than hybrid IR for both UHR-CT and ADCT.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.