ArticleEuropean radiology2024
Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Pooled it
- Image-Domain GAN Denoising for Sn100 kVp Ultra-Low-Dose Chest CT: A Retrospective Paired Image-Quality Study.Diagnostics (Basel, Switzerland) · 2026Article
- Comparative assessment of image quality and radiation dose: spectral purification computed tomography (Sn100kV) with adaptive iterative reconstruction for tuberculosis follow-up.Journal of thoracic disease · 2026Article
- Prospective real-world implementation of deep learning systems in healthcare: a systematic review guided by implementation science.NPJ digital medicine · 2026Article
- Two Deep Image Reconstructions for a 320-Row CT: Review of Clinical ApplicationsCurrent medical imaging · 2026Review
- CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction.European journal of radiology open · 2025Article
- Automatic segmentation and reconstruction of lower-extremity arteries from computed tomography angiography images via a deep learning framework.Quantitative imaging in medicine and surgery · 2025Article
- Comprehensive linkage between molecular biology and imaging radiomics for thyroid nodules.World journal of radiology · 2025Article
- Impact of a deep-learning image reconstruction algorithm on image quality and detection of solid lung lesions.Research in diagnostic and interventional imaging · 2025Article
- Deep learning reconstruction improves computer-aided pulmonary nodule detection and measurement accuracy for ultra-low-dose chest CT.BMC medical imaging · 2025Article
- Comparison of chest computed tomography in Turbo FLASH mode with conventional mode for coronary artery disease screening: radiation dose, image quality, and calcium scoring performance.Quantitative imaging in medicine and surgery · 2025Article
- Ultra-low dose dual-layer detector spectral CT for pulmonary nodule screening: image quality and diagnostic performance.Insights into imaging · 2025Article
- Deep learning model for automated diagnosis of moyamoya disease based on magnetic resonance angiography.EClinicalMedicine · 2024Article
- Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness.Korean journal of radiology · 2024Article
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
Abstract
objectivesTo explore the performance of low-dose computed tomography (LDCT) with deep learning reconstruction (DLR) for the improvement of image quality and assessment of lung parenchyma.
methodsSixty patients underwent chest regular-dose CT (RDCT) followed by LDCT during the same examination. RDCT images were reconstructed with hybrid iterative reconstruction (HIR) and LDCT images were reconstructed with HIR and DLR, both using lung algorithm. Radiation exposure was recorded. Image noise, signal-to-noise ratio, and subjective image quality of normal and abnormal CT features were evaluated and compared using the Kruskal-Wallis test with Bonferroni correction.
resultsThe effective radiation dose of LDCT was significantly lower than that of RDCT (0.29 ± 0.03 vs 2.05 ± 0.65 mSv, p < 0.001). The mean image noise ± standard deviation was 33.9 ± 4.7, 39.6 ± 4.3, and 31.1 ± 3.2 HU in RDCT, LDCT HIR-Strong, and LDCT DLR-Strong, respectively (p < 0.001). The overall image quality of LDCT DLR-Strong was significantly better than that of LDCT HIR-Strong (p < 0.001) and comparable to that of RDCT (p > 0.05). LDCT DLR-Strong was comparable to RDCT in evaluating solid nodules, increased attenuation, linear opacity, and airway lesions (all p > 0.05). The visualization of subsolid nodules and decreased attenuation was better with DLR than with HIR in LDCT but inferior to RDCT (all p < 0.05).
conclusionLDCT DLR can effectively reduce image noise and improve image quality. LDCT DLR provides good performance for evaluating pulmonary lesions, except for subsolid nodules and decreased lung attenuation, compared to RDCT-HIR. CLINICAL RELEVANCE STATEMENT: The study prospectively evaluated the contribution of DLR applied to chest low-dose CT for image quality improvement and lung parenchyma assessment. DLR can be used to reduce radiation dose and keep image quality for several indications. KEY POINTS: • DLR enables LDCT maintaining image quality even with very low radiation doses. • Chest LDCT with DLR can be used to evaluate lung parenchymal lesions except for subsolid nodules and decreased lung attenuation. • Diagnosis of pulmonary emphysema or subsolid nodules may require higher radiation doses.
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