ArticleEuropean radiology2023
CT image quality evaluation in the age of deep learning: trade-off between functionality and fidelity.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 11 citations in OpenAlex.
- Detection of hypodense hepatic and renal lesions on abdominal CT reconstructed with deep learning image reconstruction technique in patients with large body habitus: A multi-reader study.Abdominal radiology (New York) · 2026Article
- Transferability of radiomics models between deep learning and conventional CT reconstruction algorithms: A task-based assessment for stratifying acute pancreatitis severity.Journal of applied clinical medical physics · 2026Article
- Improving visibility of small anatomical details on low and ultra-low dose computed tomography with artificial intelligence-based image reconstructions.Radiation protection dosimetry · 2026Article
- High performance of low/ultralow-dose photon-counting CT for pulmonary metastasis in young musculoskeletal malignancy patients.European journal of radiology open · 2025Article
- Impact of deep learning based reconstruction algorithms on CT radiomic features of carotid plaques.Journal of applied clinical medical physics · 2025Article
- Deep learning in CT image reconstruction and processing: techniques, performance evaluation, radiation dose, and future perspective.The British journal of radiology · 2025Review
- Effects of different combinations of noise index and preset adaptive statistical iterative reconstruction Veo on the accuracy and image quality of bone mineral density measurements using fast kilovolt-switching dual-energy computed tomography: a phantom study.Quantitative imaging in medicine and surgery · 2025Article
- Detectability of Hypoattenuating Liver Lesions with Deep Learning CT Reconstruction: A Phantom and Patient Study.Radiology · 2024Article
- Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution.Medical physics · 2024Article
- Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms.European radiology experimental · 2024Article
- Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo quantitatively compare DLIR and ASiR-V with realistic anatomical images.
methodsCT scans of an anthropomorphic phantom were acquired using three routine protocols (brain, chest, and abdomen) at four dose levels, with images reconstructed at five levels of ASiR-V and three levels of DLIR. Noise power spectrum (NPS) was estimated using a difference image by subtracting two matching images from repeated scans. Using the max-dose FBP reconstruction as the ground truth, the structure similarity index (SSIM) and gradient magnitude (GM) of difference images were evaluated. Image noise magnitude (σ), frequency location of the NPS peak (f
resultsImage noise had a strong (R
conclusionCompared to ASiR-V, DLIR presents trade-offs between functionality and fidelity: it has a noise texture closer to FBP and more edge enhancement, but reduced structure similarity. These trade-offs and unique protocol-dependent behaviors of DLIR should be considered during clinical implementation and deployment. KEY POINTS: • DLIR reconstructed images demonstrate closer noise texture and lower structure similarity to FBP while producing equivalent noise levels comparable to ASiR-V. • DLIR has additional edge enhancement as compared to ASiR-V. • DLIR has unique protocol-dependent behaviors that should be considered for clinical implementation.
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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.