Related Experiment Videos
Impact of Deep Learning-Based Denoising on Image Quality and Diagnostic Confidence in Neurovascular
Adrienn Tóth1, Muhammad Taha Hagar1, Matthew Silbergleit1
1From the Department of Radiology and Radiological Science (A.T., M.T.H., M.S., M. V.-N., E.B., J.J., A.V.-S., M.V.S.), Medical University of South Carolina, Charleston, 29425, SC, US; Department of Radiology, Medical Imaging Centre (A.T., A.V.-S.), Heart and Vascular Centre (M.V.-N.), Semmelweis University, Budapest, 1082, Hungary and Department of Diagnostic and Interventional Radiology (M.T.H.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Background And Purpose:
Ultrahigh-resolution (UHR) photon-counting detector (PCD)-CT angiography enables detailed visualization of the neurovasculature. However, image noise remains a limiting factor, particularly in the lower neck and artifact-prone regions. We evaluated the impact of a post hoc artificial intelligence-based denoising (AID) algorithm on image quality and diagnostic confidence in neurovascular UHR PCD-CTA.
Material And Methods:
This retrospective, single-center study included 42 patients who underwent clinically indicated head and neck UHR PCD-CTA. Images were reconstructed using high-level vendor-specific iterative reconstruction (level 4) with and without additional post hoc AID. Quantitative analyses included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and vessel sharpness measurements at the lower neck, mid neck, and intracranial vasculature. Subjective image quality and diagnostic confidence were independently assessed by two neuroradiologists.
Results:
AID significantly reduced image noise across all vascular levels (p < 0.001), with the largest absolute reduction in the lower neck. Vessel sharpness demonstrated statistically significant but small absolute differences, with preserved spatial resolution as reflected by stable FWHM measurements. No significant differences in subjective image quality and diagnostic confidence were observed at any vascular level.
Conclusions:
Post hoc AID substantially improves objective image quality in neurovascular UHR PCD-CTA by improving objective image quality metrics. The lack of improvement in subjective assessments suggests a ceiling effect in regions with already high image quality and highlights persistent challenges in the lower neck and artifact prone regions.
Related Concept Videos
01:19Imaging Studies VII: Vascular Imaging
01:28Imaging Studies for Cardiovascular System V: CT