ArticleEmergency radiology2022
Deep learning versus iterative image reconstruction algorithm for head CT in trauma.
Article in Emergency radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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23 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Pooled it
- Performance of Deep Learning Reconstruction for Detection of Early Ischemic Changes in NCCT: Comparison with ASIR-V in Acute Stroke.Clinical neuroradiology · 2026Article
- Performance of deep-learning reconstruction combined with metal artifact reduction algorithm for dual-energy computed tomography angiography in intracranial aneurysm coil embolization.European journal of radiology open · 2026Article
- Fibre phantom generation using FibreSimulator: an open-source Python tool.Journal of synchrotron radiation · 2026Article
- Comparison of deep learning reconstruction and adaptive statistical iterative reconstruction for head CT in acute stroke.Neuroradiology · 2026Article
- Influence of deep learning image reconstruction and adaptive statistical iterative reconstruction-V on automated Alberta stroke program early CT score- evaluation.Neuroradiology · 2026Article
- Deep-learning reconstruction for noncontrast head computed tomography: Improved image quality and potential diagnostic impact in acute ischemic stroke.Neuroradiology · 2026Article
- Effect of deep learning reconstruction on arm-induced artifacts compared with hybrid iterative reconstruction and filtered-backprojection in abdominal CT.Radiological physics and technology · 2026Article
- Deep learning image reconstruction technique for improving image quality and radiation dose reduction compared to iterative reconstruction technique in non-contrast CT head imaging.Frontiers in radiology · 2026Article
- Article
- Detection of intracranial hemorrhage using ultralow-dose brain computed tomography with deep learning reconstruction versus conventional-dose computed tomography.BMC medical imaging · 2025Article
- Comparing two deep learning spectral reconstruction levels for abdominal evaluation using a rapid-kVp-switching dual-energy CT scanner.Abdominal radiology (New York) · 2025Article
- Deep learning-based reconstruction improves image quality in low-dose head CT angiography.Malawi medical journal : the journal of Medical Association of Malawi · 2025Article
- Noise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple Parameters.Tomography (Ann Arbor, Mich.) · 2024Article
- Insights about cervical lymph nodes: Evaluating deep learning-based reconstruction for head and neck computed tomography scan.European journal of radiology open · 2024Article
- Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.Scientific reports · 2024Article
- Comparison of image quality between Deep learning image reconstruction and Iterative reconstruction technique for CT Brain- a pilot study.F1000Research · 2024Article
- The value of a deep learning image reconstruction algorithm in whole-brain computed tomography perfusion in patients with acute ischemic stroke.Quantitative imaging in medicine and surgery · 2023Article
- Deep Learning-Based Versus Iterative Image Reconstruction for Unenhanced Brain CT: A Quantitative Comparison of Image Quality.Tomography (Ann Arbor, Mich.) · 2023Article
- Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination.Tomography (Ann Arbor, Mich.) · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo compare the image quality between a deep learning-based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT.
methodsHead CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa.
resultsDLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader.
conclusionThe image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.
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