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Advancing Neurovascular Imaging: Optimization of Reconstruction Kernel and Quantum Iterative Reconstruction for
Adrienn Tóth1, Jennifer Yongjoo Cho2, John Crow2
1Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston 29425, SC (A.T., J.Y., J.C., E.W., J.J., M.G.M., E.B., M.V.S.); Medical Imaging Centre, Semmelweis University, Budapest, Hungary (A.T.).
Rationale And Objectives:
To find the optimal reconstruction parameters for ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) angiography of the head and neck, with a special focus on imaging intracranial aneurysms.
Material And Methods:
18 patients with intracranial aneurysms were prospectively enrolled in this single-center study. CT angiograms were acquired in UHR mode on a clinical PCD-CT scanner. Images were reconstructed with six strength levels of a dedicated neurovascular kernel (Hv48-89) and with quantum iterative reconstruction (QIR) levels 1-4. Image noise, contrast-to-noise ratio, and vessel sharpness were determined for all reconstructions. Qualitative image quality was assessed by three readers using a 5-point Likert scale, for the best-performing reconstructions. Aneurysm dome and neck sizes were independently measured by two readers to assess inter-reader reliability.
Results:
18 patients (mean age, 64.3 years ± 15 [SD], 5 men,) were evaluated. Three kernels (Hv56, Hv64, Hv72) were identified as best performing in the quantitative analysis. The qualitative analysis demonstrated a consistent preference for QIR level 4 across all kernels in each evaluated category (P<0.001). Hv72 was the most frequently preferred, although it exhibited a higher incidence of image artifacts compared to the other kernels. Inter-reader reliability was high for dome measurements-especially on UHR images-while neck measurements showed greater variability.
Conclusion:
Hv72 kernel with QIR level 4 proved to be the optimal combination of CTA parameters among the configurations evaluated. Our results could provide reference for protocol optimization on PCD-CT for neurovascular imaging.
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