ArticleJapanese journal of radiology2023
Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images.
Article in Japanese journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 20 citations in OpenAlex.
- Deep learning-based motion correction: cardiac motion artifact and image quality improvements on chest CT.Japanese journal of radiology · 2026Article
- High-resolution chest CT using 1024-matrix reconstruction: phantom and clinical evaluation of image quality and post-processing capability.Japanese journal of radiology · 2026Article
- Photon-counting detector computed tomography in thoracic oncology: revolutionizing tumor imaging through precision and detail.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- 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
- Conjugate gradient and deep learning reconstructions: reduced time without affecting image quality and nodule detection.European radiology · 2026Article
- Article
- The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Quantitative Evaluation of Low-Dose CT Image Quality Using Deep Learning Reconstruction: A Comparative Study of Philips Precise Image and GE TrueFidelity.Journal of imaging · 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
- Application of a pulmonary nodule detection program using AI technology to ultra-low-dose CT: differences in detection ability among various image reconstruction methods.Japanese journal of radiology · 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
- Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V.European radiology · 2025Article
- Enhancing Image Quality in Dental-Maxillofacial CBCT: The Impact of Iterative Reconstruction and AI on Noise Reduction-A Systematic Review.Journal of clinical medicine · 2025Review
- Accelerating FLAIR imaging via deep learning reconstruction: potential for evaluating white matter hyperintensities.Japanese journal of radiology · 2025Article
- Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.Japanese journal of radiology · 2025Review
- Comparison of computed tomography and guided bronchoscopy in the diagnosis of pulmonary nodules: A systematic review and meta-analysis.Open medicine (Warsaw, Poland) · 2025Article
- Generative AI and large language models in nuclear medicine: current status and future prospects.Annals of nuclear medicine · 2024Review
- The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI.Japanese journal of radiology · 2024Review
- Quantifying image quality: are we approaching the grail?European radiology · 2024Article
- Artificial Intelligence in Chest Computed Tomography Optimization: A Task-based Framework for Balancing Detectability and Radiation Dose.Journal of medical physicsReview
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Authors and funding
15 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDeep learning reconstruction (DLR) has been introduced by major vendors, tested for CT examinations of a variety of organs, and compared with other reconstruction methods. The purpose of this study was to compare the capabilities of DLR for image quality improvement and lung texture evaluation with those of hybrid-type iterative reconstruction (IR) for standard-, reduced- and ultra-low-dose CTs (SDCT, RDCT and ULDCT) obtained with high-definition CT (HDCT) and reconstructed at 0.25-mm, 0.5-mm and 1-mm section thicknesses with 512 × 512 or 1024 × 1024 matrixes for patients with various pulmonary diseases. MATERIALS AND
methodsForty age-, gender- and body mass index-matched patients with various pulmonary diseases underwent SDCT (CT dose index volume <CTDI
resultsData for each radiation dose showed DLR attained significantly higher SNR than hybrid-type IR for each of the CT data (p < 0.0001). On assessments of all findings except consolidation and nodules or masses, areas under the curve (AUCs) for ULDCT with hybrid-type IR for each section thickness (0.91 ≤ AUC ≤ 0.97) were significantly smaller than those with DLR (0.97 ≤ AUC ≤ 1, p < 0.05) and the standard protocol (0.98 ≤ AUC ≤ 1, p < 0.05).
conclusionDLR is potentially more effective for image quality improvement and lung texture evaluation than hybrid-type IR on all radiation dose CTs obtained at HDCT and reconstructed with each section thickness with both matrixes for patients with a variety of pulmonary diseases.
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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.