Image Quality Assessment of the External Carotid Artery and Its Branches on Ultra-High-Resolution Head and Neck Computed Tomography Angiography Using a High-Resolution 0.25-mm Detector and Deep Learning Reconstruction

Motoma Kanaya; Noriyuki Fujima; Koji Yamasaki; Yuki Takano; Yohei Ikebe; Taisuke Harada; Yukie Shimizu; Hiroyuki Kameda; Yuya Hirano; Aogu Yamaguchi; Satoshi Kano; Akihiro Homma; Kohsuke Kudo

doi:10.3174/ajnr.A9375

Image Quality Assessment of the External Carotid Artery and Its Branches on Ultra-High-Resolution Head and Neck Computed Tomography Angiography Using a High-Resolution 0.25-mm Detector and Deep Learning Reconstruction

AJNR Am J Neuroradiol. 2026 Apr 25:ajnr.A9375. doi: 10.3174/ajnr.A9375. Online ahead of print.

Authors

Affiliations

¹ From the Department of Diagnostic Imaging (M.K., N.F., K.Y., Y.T., Y.I., T.H., Y.S., K.K.), Center for Cause of Death Investigation (Y.I., T.H.), Faculty of Medicine, Otolaryngology-Head and Neck Surgery (S.K., A.H.), Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan; Department of Diagnostic and Interventional Radiology (M.K., N.F., K.Y., Y.T., Y.I., T.H., Y.S., H.K., K.K.), Radiology (H.K.), Faculty of Dental Medicine, Radiological Technology (Y.H., A.Y.), Medical AI Human Research and Development Center (K.K.) and Global Center for Biomedical Science and Engineering (K.K.), Faculty of Medicine, Hokkaido University, Sapporo, Japan.
² From the Department of Diagnostic Imaging (M.K., N.F., K.Y., Y.T., Y.I., T.H., Y.S., K.K.), Center for Cause of Death Investigation (Y.I., T.H.), Faculty of Medicine, Otolaryngology-Head and Neck Surgery (S.K., A.H.), Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan; Department of Diagnostic and Interventional Radiology (M.K., N.F., K.Y., Y.T., Y.I., T.H., Y.S., H.K., K.K.), Radiology (H.K.), Faculty of Dental Medicine, Radiological Technology (Y.H., A.Y.), Medical AI Human Research and Development Center (K.K.) and Global Center for Biomedical Science and Engineering (K.K.), Faculty of Medicine, Hokkaido University, Sapporo, Japan. fujima@med.hokudai.ac.jp.

PMID: 42034565
DOI: 10.3174/ajnr.A9375

Abstract

Background and purpose: Deep learning reconstruction can improve image quality of CTA, but its benefit for visualizing small-caliber external carotid artery branches on ultra-high-resolution CTA remains unclear. We evaluated the image quality of ultra-high-resolution CTA of the external carotid artery using filtered back projection, hybrid iterative reconstruction, and deep learning reconstruction, and assessed its added value for visualizing small-caliber external carotid artery branches and tumor-feeding vessels.

Materials and methods: In this single-center study, we retrospectively analyzed 24 patients who underwent ultra-high-resolution CTA for evaluation of head/neck tumors or an elongated styloid process. Axial images (0.25-mm slice thickness) were reconstructed with each method. The quantitative analyses included attenuation, image noise, SNR, contrast-to-noise ratio, edge rise distance, and edge rise slope for the external carotid artery trunk and 11 branches. Two radiologists (each with 8 years of experience) independently assessed overall image quality, peripheral vessel sharpness, artifact severity, and clarity of the tumor-margin and intratumoral vessels, using a four-point Likert scale. Objective and subjective metrics were compared using nonparametric repeated-measures tests. Interobserver agreement was assessed using quadratic-weighted κ-value.

Results: Deep learning reconstruction provided the lowest image noise, highest SNR, and highest contrast-to-noise ratio for the external carotid artery trunk (all P <.001). For the 11 branches, deep learning reconstruction demonstrated the shortest edge rise distance (0.795 [0.751-0.824] mm vs. 0.982 [0.942-1.017] for hybrid iterative reconstruction and 1.044 [1.002-1.157] for filtered back projection), the highest edge rise slope(613.4 [586.1-686.5] vs. 393.1 [366.7-442.6] and 477.4 [446.9-524.7]), and the highest contrast-to-noise ratio (40.6 [36.0-43.2] vs. 16.7 [14.9-20.2] and 9.8 [8.3-11.0]) (all P <.001). Subjective scores were highest for deep learning reconstruction across all categories (median overall image quality: 4 for deep learning reconstruction, 3 for hybrid iterative reconstruction, and 2 for filtered back projection). Deep learning reconstruction also provided significantly superior visualization of tumor-margin and intratumoral vessels (P <.001). Interobserver agreement was substantial for all qualitative metrics (κ = 0.67-0.76).

Conclusions: Deep learning reconstruction markedly improves the quality of ultra-high-resolution CTA images of the external carotid artery system by enhancing vessel sharpness, contrast resolution, and tumor-feeding vessel conspicuity, although further validation in larger cohorts is needed to confirm these findings.