Objective:To evaluate the performance of deep learning reconstruction (DLR) in visualizing lenticulostriate arteries (LSAs) on cerebral CT angiography (CTA).Methods:This cross-sectional study retrospectively analyzed cerebral CTA from 38 patients who underwent cerebral CTA at the Fourth Affiliated Hospital of Harbin Medical University between January and December 2023. Images were reconstructed using filtered back projection (FBP), three-dimensional adaptive iterative dose reduction (AIDR), and DLR-advanced inteuigent clear-IQ engine(AiCE) algorithms (FBP group, AIDR group, DLR-AiCE group). On axial images, the mean CT values, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured at the origin of LSAs, cerebrospinal fluid in lateral ventricles, temporal muscle, and head of the caudate nucleus. Subjective evaluations were performed for overall vascular visualization and LSAs delineation. Comparisons of subjective and objective evaluation indexes among the 3 groups were performed using the complex measurement ANOVA, Friedman test, or χ2 test. Results:The CT, SD, SNR and CNR values at the origin of LSAs, cerebrospinal fluid in lateral ventricles, temporal muscle, and head of the caudate nucleus demonstrated statistically significance among DLR-AiCE group, AIDR group and FBP group ( P<0.001), in which, except for the difference between the FBP group and the AIDR group in the CT value of the head of the caudate nucleus and the CT value of the cerebrospinal fluid of the lateral ventricle which was not statistically significant ( P>0.05), the remaining pairwise comparisons between the groups for each site measurements were statistically significant ( P<0.001). The difference in the overall comparison of the subjective scores of the overall vessels and LSAs in the images of the DLR-AiCE group, the AIDR group, and the FBP group was statistically significant ( P<0.001), and the two-by-two comparisons showed a statistically significant difference ( P<0.001) except for the difference in the subjective scores of LSAs between the FBP group and the AIDR group. Conclusion:The DLR-AiCE algorithm significantly reduces image noise and improves image quality, enabling superior visualization of LSAs, thereby enhancing diagnostic confidence.

Objective:To evaluate the performance of deep learning reconstruction (DLR) in visualizing lenticulostriate arteries (LSAs) on cerebral CT angiography (CTA).Methods:This cross-sectional study retrospectively analyzed cerebral CTA from 38 patients who underwent cerebral CTA at the Fourth Affiliated Hospital of Harbin Medical University between January and December 2023. Images were reconstructed using filtered back projection (FBP), three-dimensional adaptive iterative dose reduction (AIDR), and DLR-advanced inteuigent clear-IQ engine(AiCE) algorithms (FBP group, AIDR group, DLR-AiCE group). On axial images, the mean CT values, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured at the origin of LSAs, cerebrospinal fluid in lateral ventricles, temporal muscle, and head of the caudate nucleus. Subjective evaluations were performed for overall vascular visualization and LSAs delineation. Comparisons of subjective and objective evaluation indexes among the 3 groups were performed using the complex measurement ANOVA, Friedman test, or χ2 test. Results:The CT, SD, SNR and CNR values at the origin of LSAs, cerebrospinal fluid in lateral ventricles, temporal muscle, and head of the caudate nucleus demonstrated statistically significance among DLR-AiCE group, AIDR group and FBP group ( P<0.001), in which, except for the difference between the FBP group and the AIDR group in the CT value of the head of the caudate nucleus and the CT value of the cerebrospinal fluid of the lateral ventricle which was not statistically significant ( P>0.05), the remaining pairwise comparisons between the groups for each site measurements were statistically significant ( P<0.001). The difference in the overall comparison of the subjective scores of the overall vessels and LSAs in the images of the DLR-AiCE group, the AIDR group, and the FBP group was statistically significant ( P<0.001), and the two-by-two comparisons showed a statistically significant difference ( P<0.001) except for the difference in the subjective scores of LSAs between the FBP group and the AIDR group. Conclusion:The DLR-AiCE algorithm significantly reduces image noise and improves image quality, enabling superior visualization of LSAs, thereby enhancing diagnostic confidence.

Objective:To assess the value of cerebral small vessel disease (CSVD) burden in predicting prognosis in acute ischemic stroke (AIS) patients with anterior circulation large vessel occlusion (LVO) after endovascular therapy (EVT).Methods:The study was a cross-sectional study. A total of 242 patients with AIS due to anterior circulation LVO received EVT in the First Affiliated Hospital of Nanjing Medical University from February 2018 to September 2022. The clinical and imaging data of all patients were analyzed retrospectively. On follow-up MRI within 7 days after EVT, CSVD features [white matter hyperintensity (WMH), lacune, perivascular space, cerebral microbleed, cerebral atrophy] and CSVD burden score (0-5) was evaluated. Modified Rankin scale (mRS) score at 90 days after EVT was assessed. Patients were categorized into a mild burden group (0-1 points) and a moderate-severe burden group (2-5 points) based on CSVD burden score. Meanwhile, patients were categorized into a good prognosis group (0-2 points) and a bad prognosis group (3-6 points) based on mRS score at 90 days after EVT. Mann-Whitney U test and χ2 test were used to compare the difference of clinical and imaging indexes between the 2 groups, and variables with P<0.1 in the univariate analysis were included in the multifactorial logistic regression to screen for independent factors to predict the prognosis. Results:There were 169 patients in the good prognosis group and 73 patients in the bad prognosis group out of 242 patients. Compared with the good prognosis group, age, incidence of hyperlipidemia, baseline National Institutes of Health Stroke Scale (NIHSS) scores, incidence of hemorrhagic conversion, CSVD burden scores, incidence of periventricular WMH scores of 3 and/or deep WMH scores≥2, and incidence of moderate-severe cerebral atrophy of patients in the bad prognosis group were higher, and the incidence of complete recanalization was lower (all P<0.05). Multivariate analysis showed hyperlipemia ( OR=8.438, 95% CI 1.691-42.119, P=0.009), baseline NIHSS score ( OR=1.103, 95% CI 1.047-1.162, P<0.001), complete recanalization ( OR=0.131, 95% CI 0.038-0.454, P=0.001) and hemorrhage transformation ( OR=1.952, 95% CI 1.031-3.697, P=0.040) were independent factors for the prognosis of EVT in patients with LVO AIS. There were 157 cases in the mild burden group and 85 cases in the moderate-severe burden group. The 90-day mRS score was higher in the moderate-severe burden group compared with the mild burden group ( Z=-2.24, P=0.025). Conclusion:CSVD burden has some clinical implications in predicting the prognosis of EVT in patients with anterior circulation LVO AIS.