ArticleRadiology. Artificial intelligence2019
Deep Learning-based CT Image Reconstruction: Initial Evaluation Targeting Hypovascular Hepatic Metastases.
Article in Radiology. Artificial intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 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
- Utility of intracellular enhancement for improving hepatic lesion visibility at gadoxetic acid-enhanced hepatobiliary-phase MRI.European journal of radiology open · 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
- Phantom-based performance comparison of two commercial deep learning CT reconstruction algorithms with super- and normal-resolution settings.European radiology experimental · 2026Article
- AI-based denoising improves image quality in HCC volume perfusion CT without affecting Milan classification.BMC medical imaging · 2026Article
- Two Deep Image Reconstructions for a 320-Row CT: Review of Clinical ApplicationsCurrent medical imaging · 2026Review
- Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond.Abdominal radiology (New York) · 2026Review
- Deep learning in CT image reconstruction and processing: techniques, performance evaluation, radiation dose, and future perspective.The British journal of radiology · 2025Review
- Deep learning reconstruction for improved image quality of ultra-high-resolution brain CT angiography: application in moyamoya disease.Japanese journal of radiology · 2025Article
- Deep learning reconstruction enhances image quality in contrast-enhanced CT venography for deep vein thrombosis.Emergency radiology · 2025Article
- Ultra-low-dose hepatic computed tomography with a novel real-time deep learning-based noise reduction algorithm: a prospective cross-sectional analysis of image quality and lesion detection.Quantitative imaging in medicine and surgery · 2025Article
- Image quality and radiation dose of reduced-dose abdominopelvic computed tomography (CT) with silver filter and deep learning reconstruction.Scientific reports · 2025Article
- CZT-based photon-counting-detector CT with deep-learning reconstruction: image quality and diagnostic confidence for lung tumor assessment.Japanese journal of radiology · 2025Article
- Contrast-enhanced thin-slice abdominal CT with super-resolution deep learning reconstruction technique: evaluation of image quality and visibility of anatomical structures.Japanese journal of radiology · 2025Article
- Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography.Radiological physics and technology · 2024Article
- A paradigm shift in oncology imaging: a prospective cross-sectional study to assess low-dose deep learning image reconstruction versus standard-dose iterative reconstruction for comprehensive lesion detection in dual-energy computed tomography.Quantitative imaging in medicine and surgery · 2024Article
- Article
- Deep learning CT reconstruction improves liver metastases detection.Insights into imaging · 2024Article
- Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.Physics in medicine and biology · 2024Article
- Deep learning reconstruction vs standard reconstruction for abdominal CT: the influence of BMI.European radiology · 2024Article
Corrections and comments
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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
purposeTo evaluate the effect of a deep learning-based reconstruction (DLR) method on the conspicuity of hypovascular hepatic metastases on abdominal CT images. MATERIALS AND
methodsThis retrospective study with institutional review board approval included 58 patients with hypovascular hepatic metastases. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and the contrast-to-noise ratio (CNR). CNR was calculated as region of interest ([ROI]
resultsThe image noise was significantly lower, and the CNR was significantly higher on DLR images than hybrid IR images (median image noise: 19.2 vs 12.8 HU,
conclusionDLR improved the quality of abdominal CT images for the evaluation of hypovascular hepatic metastases.© RSNA, 2019
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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.