ArticleClinical neuroradiology2024
Improvement of Neurovascular Imaging Using Ultra-High-Resolution Computed Tomography Angiography.
Article in Clinical neuroradiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 10 citations in OpenAlex.
- Effective Dose Estimations for the Swiss Survey on Medical Radiation Exposure of the Population in 2023.Health physics · 2026Article
- Deep Learning-Based Image Reconstruction in Ultra-High-Resolution CT Venography for Improved Visualization of Cerebral Venous Drainage.Clinical neuroradiology · 2026Article
- Photon-counting detector of the ophthalmic artery: impact of virtual monoenergetic levels, reconstruction kernel, and quantum iterative reconstruction (QIR) on image quality.Quantitative imaging in medicine and surgery · 2026Article
- Radiation Dose Reduction and Image Quality Improvement of UHR CT of the Neck by Novel Deep-learning Image Reconstruction.Clinical neuroradiology · 2025Article
- High-resolution computed tomography with 1,024-matrix for artificial intelligence-based computer-aided diagnosis in the evaluation of pulmonary nodules.Journal of thoracic disease · 2025Article
- Visibility of Intracranial Perforating Arteries Using Ultra-High-Resolution Photon-Counting Detector Computed Tomography (CT) Angiography.Tomography (Ann Arbor, Mich.) · 2024Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate diagnostic image quality of ultra-high-resolution computed tomography angiography (UHR-CTA) in neurovascular imaging as compared to normal resolution CT-angiography (NR-CTA). MATERIAL AND
methodsIn this retrospective single-center study brain and neck CT-angiography was performed using an ultra-high-resolution computed tomography scanner (n = 82) or a normal resolution CT scanner (NR-CTA; n = 73). Ultra-high-resolution images were reconstructed with a 1024 × 1024 matrix and a slice thickness of 0.25 mm, whereas NR-CT images were reconstructed with a 512 × 512 matrix and a slice thickness of 0.5 mm. Three blinded neuroradiologists assessed overall image quality, artifacts, image noise, overall contrast and diagnostic confidence using a 4-point Likert scale. Furthermore, the visualization and delineation of supra-aortic arteries with an emphasis on the visualization of small intracerebral vessels was assessed using a cerebral vascular score, also utilizing a 4-point Likert scale. Quantitative analyses included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), noise and the steepness of gray value transition. Radiation exposure was determined by comparison of computed tomography dose index (CTDIvol), dose length product (DLP) and mean effective dose. Interrater agreement was evaluated via determining Fleiss-Kappa.
resultsUltra-high-resolution CT-angiography (UHR-CTA) yielded excellent image quality with superior quantitative (SNR: p < 0.001, CNR: p < 0.001, steepness of gray value transition: p < 0.001) and qualitative results (overall image quality: 4 (Inter quartile range (IQR) = 4-4); p < 0.001, diagnostic confidence: 4 (IQR = 4-4); p < 0.001) compared to NR-CT (overall image quality: 3 (IQR = 3-3), diagnostic confidence: 3 (IQR = 3-4)). Furthermore, UHR-CT enabled significantly superior delineation and visualization of all vascular segments, from proximal extracranial vessels to the smallest peripheral cerebral branches (e.g. , UHR-CTA PICA: 4 (3-4) vs. NR-CTA PICA: 3 (2-3); UHR-CTA P4: 4 (IQR = 3-4) vs. NR-CTA P4: 2 (IQR = 2-3); UHR-CTA M4: 4 (IQR = 4-4) vs. NR-CTA M4: 3 (IQR = 2-3); UHR-CTA A4: 4 (IQR = 3-4) vs. NR-CTA A4: 2 (IQR = 2-3); all p < 0.001). Noteworthy, a reduced mean effective dose was observed when applying UHR-CT (NR-CTA: 1.8 ± 0.3 mSv; UHR-CTA: 1.5 ± 0.5 mSv; p < 0.001).
conclusionUltra-high-resolution CT-angiography improves image quality in neurovascular imaging allowing the depiction and evaluation of small peripheral cerebral arteries. It may thus improve the detection of pathologies in small cerebrovascular lesions and the resulting diagnosis.
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