ArticleDiagnostics (Basel, Switzerland)2024
Impact of AI-Based Post-Processing on Image Quality of Non-Contrast Computed Tomography of the Chest and Abdomen.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 4 citations in OpenAlex.
- Additional Dose Reduction Potential of Vendor-Agnostic Deep Learning Model: A Phantom Study.Journal of the Korean Society of Radiology · 2026Article
- Image-domain deep learning denoising for low-dose chest CT on a single 128-slice CT platform: a retrospective image-quality assessment.Frontiers in medicine · 2026Article
- AI in Medical Imaging and Image Processing.Journal of clinical medicine · 2025Article
- A Systematic Review of Medical Image Quality Assessment.Journal of imaging · 2025Review
- Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.Scientific reports · 2024Article
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Authors and funding
9 authors at 2 institutions in 1 country.
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Abstract
Non-contrast computed tomography (CT) is commonly used for the evaluation of various pathologies including pulmonary infections or urolithiasis but, especially in low-dose protocols, image quality is reduced. To improve this, deep learning-based post-processing approaches are being developed. Therefore, we aimed to compare the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast chest and low-dose abdominal CTs. In this retrospective study, non-contrast chest CTs of patients suspected of COVID-19 pneumonia and low-dose abdominal CTs suspected of urolithiasis were analysed. All images were reconstructed using filtered back-projection (FBP) and were post-processed using an artificial intelligence (AI)-based commercial software (PixelShine (PS)). Additional iterative reconstruction (IR) was performed for abdominal CTs. Objective and subjective image quality were evaluated. AI-based post-processing led to an overall significant noise reduction independent of the protocol (chest or abdomen) while maintaining image information (max. difference in SNR 2.59 ± 2.9 and CNR 15.92 ± 8.9,
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