ArticleEuropean radiology2021
CT iterative vs deep learning reconstruction: comparison of noise and sharpness.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 66 papers, 1 of them a synthesis that pooled it.
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Who cites it
66 citing papers in PubMed, 1 synthesis or guideline pooled it, 150 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
- Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study.European radiology · 2023Trial
- Evaluation of deep-learning iterative reconstruction combined with high-frequency kernels in CT: a task-based image quality study.European radiology experimental · 2026Article
- Deep Learning Reconstruction Versus Hybrid Iterative Reconstruction for Acute Cerebral Infarction Detection on 135 kVp Non-Contrast Brain CT.Clinical neuroradiology · 2026Article
- Reconstruction algorithms and arm positioning effects on abdominal CT image quality and radiation dose: a phantom study.European radiology experimental · 2026Article
- Evaluation of optimal ClearInfinity artificial intelligence deep learning reconstruction algorithm weights with a 70-kVp tube voltage combined with "dual-low" abdominal computed tomography angiography.Quantitative imaging in medicine and surgery · 2026Article
- Validation and feasibility of fast knee MRI using a deep learning-assisted 3D iterative image enhancement system.Quantitative imaging in medicine and surgery · 2026Article
- Comparison of deep learning reconstruction algorithms to improve image quality of dual-energy carotid CT angiography under dual-low scan.Neuroradiology · 2026Article
- Effect of Deep Learning-Based Image Reconstruction on Lesion Conspicuity of Liver Metastases in Pre- and Post-contrast Enhanced Computed Tomography.Journal of imaging informatics in medicine · 2026Article
- Improved Image Quality of Virtual Monochromatic Images with Deep Learning Image Reconstruction Algorithm on Dual-Energy CT in Patients with Pancreatic Ductal Adenocarcinoma.Journal of imaging informatics in medicine · 2026Article
- Comparison of DLIR and ASIR-V algorithms for virtual monoenergetic imaging in carotid CTA under a triple-low protocol.Japanese journal of radiology · 2026Article
- Comparison of image quality in 40 keV virtual monoenergetic images of dual-energy CT pulmonary angiography using deep learning and iterative reconstruction algorithms under optimized low dose scanning protocols.Quantitative imaging in medicine and surgery · 2025Article
- Detection of intracranial hemorrhage using ultralow-dose brain computed tomography with deep learning reconstruction versus conventional-dose computed tomography.BMC medical imaging · 2025Article
- Performance of a Deep Learning Reconstruction Method on Clinical Chest-Abdomen-Pelvis Scans from a Dual-Layer Detector CT System.Tomography (Ann Arbor, Mich.) · 2025Article
- Image quality assessment of artificial intelligence iterative reconstruction for low dose unenhanced abdomen: comparison with hybrid iterative reconstruction.Abdominal radiology (New York) · 2025Observational
- Ultra-High-Resolution Photon-Counting Detector CT Benefits Visualization of Abdominal Arteries: A Comparison to Standard-Reconstruction.Journal of imaging informatics in medicine · 2025Article
- Comparison of radiation exposure from dual- and single-energy CT imaging protocols resulting in equivalent contrast-to-noise ratio of lesions for adults and children: a phantom study.European radiology · 2025Article
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Impact of Deep Learning-Based Image Reconstruction on Tumor Visibility and Diagnostic Confidence in Computed Tomography.Bioengineering (Basel, Switzerland) · 2024Article
- Coronary CT angiography: First comparison of model-based and hybrid iterative reconstruction with the reference standard invasive catheter angiography for CAD-RADS reporting.European journal of radiology open · 2024Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo compare image noise and sharpness of vessels, liver, and muscle in lower extremity CT angiography between "adaptive statistical iterative reconstruction-V" (ASIR-V) and deep learning reconstruction "TrueFidelity" (TFI).
methodsThirty-seven patients (mean age, 65.2 years; 32 men) with lower extremity CT angiography were enrolled between November and December 2019. Images were reconstructed with two ASIR-V (blending factor of 80% and 100% (AV-100)) and three TFI (low-, medium-, and high-strength-level (TF-H) settings). Two radiologists evaluated these images for vessels (aorta, femoral artery, and popliteal artery), liver, and psoas muscle. For quantitative analyses, conventional indicators (CT number, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) and blur metric values (indicating the degree of image sharpness) of selected regions of interest were determined. For qualitative analyses, the degrees of quantum mottle and blurring were assessed.
resultsThe higher the blending factor in ASIR-V or the strength in TFI, the lower the noise, the higher the SNR and CNR values, and the higher the blur metric values in all structures. The SNR and CNR values of TF-H images were significantly higher than those of AV-80 images and similar to those of AV-100 images. The blur metric values in TFI images were significantly lower than those in ASIR-V images (p < 0.001), indicating increased sharpness. Among all the investigated image procedures, the overall qualitative image quality was best in TF-H images.
conclusionTF-H was the most balanced image in terms of image noise and sharpness among the examined image combinations. KEY POINTS: • Deep learning image reconstruction "TrueFidelity" is superior to iterative reconstruction "ASIR-V" regarding image noise and sharpness. • The high-strength "TrueFidelity" approach generated the best image quality among the examined image reconstruction procedures. • In iterative and deep learning CT image reconstruction, the higher the blending and strength factors, the lower the image noise and the poorer the image sharpness.
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