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. 2024 Nov 21;10(12):1867-1880.
doi: 10.3390/tomography10120136.

Visibility of Intracranial Perforating Arteries Using Ultra-High-Resolution Photon-Counting Detector Computed Tomography (CT) Angiography

Takashi Okazaki  1 , Tetsu Niwa  1 , Ryoichi Yoshida  2 , Takatoshi Sorimachi  3 , Jun Hashimoto  1
Affiliations

Visibility of Intracranial Perforating Arteries Using Ultra-High-Resolution Photon-Counting Detector Computed Tomography (CT) Angiography

Takashi Okazaki et al. Tomography. .

Abstract

Background/Objectives: Photon-counting detector computed tomography (PCD-CT) offers energy-resolved CT data with enhanced resolution, reduced electronic noise, and improved tissue contrast. This study aimed to evaluate the visibility of intracranial perforating arteries on ultra-high-resolution (UHR) CT angiography (CTA) on PCD-CT. Methods: A retrospective analysis of intracranial UHR PCD-CTA was performed for 30 patients. The image quality from four UHR PCD-CTA reconstruction methods [kernel Hv40 and Hv72, with and without quantum iterative reconstruction (QIR)] was assessed for the lenticulostriate arteries (LSAs) and pontine arteries (PAs). A subjective evaluation included peripheral visibility, vessel sharpness, and image noise, while objective analysis focused on the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Results: Peripheral LSAs were well visualized across all reconstruction methods, with no significant differences between them. Vessel sharpness and image noise varied significantly (p < 0.0001); sharper LSAs and more noise were seen with kernel Hv72 compared to kernel Hv40 (p < 0.05). A similar pattern was observed for PAs, though peripheral visibility was lower than that for LSAs. The SNR and CNR were the highest in the presence of kernel Hv72 with QIR, and lowest with kernel Hv72 without QIR, compared to kernel Hv40 (p < 0.05). Conclusions: UHR PCD-CTA provided a good visualization of the intracranial perforating arteries, particularly LSAs. The vessel sharpness and image noise varied by reconstruction method, in which kernel Hv72 with QIR offered the optimal visualization.

Keywords: CT angiography; intracranial perforating arteries; photon-counting detector CT; ultra-high-resolution CT.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Process of quantum iterative reconstruction (QIR). QIR divides raw detector data into two energy-level streams, each undergoing its iterative loop for artifact cancelation and noise reduction. Synchronization points ensure precise geometric alignment between these streams, which undergo spectral processing to create spectral maps and monoenergetic images.
Figure 2
Figure 2
Subjective analysis scores for peripheral visibility, vessel sharpness, and image noise of the LSA (A) and PA (B) in UHR PCD-CTA, using kernel Hv40 with and without QIR and kernel Hv72 with and without QIR. The box indicates the median score, and the error bar shows the score range. * indicates significant differences with post hoc pairwise comparison.
Figure 2
Figure 2
Subjective analysis scores for peripheral visibility, vessel sharpness, and image noise of the LSA (A) and PA (B) in UHR PCD-CTA, using kernel Hv40 with and without QIR and kernel Hv72 with and without QIR. The box indicates the median score, and the error bar shows the score range. * indicates significant differences with post hoc pairwise comparison.
Figure 3
Figure 3
Representative UHR PCD-CTA images with coronal partial maximum intensity for a 68-year-old female, showing kernel Hv40 without QIR (A) and with QIR (B), as well as kernel Hv72 without QIR (C) and with QIR (D) in the LSA region. The LSAs are generally well visualized across all reconstruction methods, but vessel sharpness and image noise vary among methods.
Figure 4
Figure 4
Representative UHR PCD-CTA images with sagittal partial maximum intensity projection for a 71-year-old female, showing kernel Hv40 without QIR (A) and with QIR (B), as well as kernel Hv72 without QIR (C) and with QIR (D) in the PA region. The PAs are less visible compared to the LSAs, with variations in vessel sharpness and image noise among the reconstruction methods.
Figure 5
Figure 5
Distribution of the number of right (A) and left (B) LSAs.
Figure 6
Figure 6
SNR and CNR values for the LSA (A) and PA (B) regions on UHR PCD-CTA using kernel Hv40 with and without QIR and kernel Hv72 with and without QIR. The data are shown as box-and-whisker plots, depicting the median, upper and lower quartiles, and the maximum and minimum values. * indicates significant differences with post hoc pairwise comparison. White circles indicate outliers.
Figure 6
Figure 6
SNR and CNR values for the LSA (A) and PA (B) regions on UHR PCD-CTA using kernel Hv40 with and without QIR and kernel Hv72 with and without QIR. The data are shown as box-and-whisker plots, depicting the median, upper and lower quartiles, and the maximum and minimum values. * indicates significant differences with post hoc pairwise comparison. White circles indicate outliers.
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