Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques.

BACKGROUND: The image quality of computed tomography angiography (CTA) images following endovascular aneurysm repair (EVAR) is not satisfactory, since artifacts resulting from metallic implants obstruct the clear depiction of stent and isolation lumens, and also adjacent soft tissues. However, current techniques to reduce these artifacts still need further advancements due to higher radiation doses, longer processing times and so on. Thus, the aim of this study is to assess the impact of utilizing Single-Energy Metal Artifact Reduction (SEMAR) alongside a novel deep learning image reconstruction technique, known as the Advanced Intelligent Clear-IQ Engine (AiCE), on image quality of CTA follow-ups conducted after EVAR. MATERIALS: This retrospective study included 47 patients (mean age ± standard deviation: 68.6 ± 7.8 years; 37 males) who underwent CTA examinations following EVAR. Images were reconstructed using four different methods: hybrid iterative reconstruction (HIR), AiCE, the combination of HIR and SEMAR (HIR + SEMAR), and the combination of AiCE and SEMAR (AiCE + SEMAR). Two radiologists, blinded to the reconstruction techniques, independently evaluated the images. Quantitative assessments included measurements of image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the longest length of artifacts (AL), and artifact index (AI). These parameters were subsequently compared across different reconstruction methods. RESULTS: The subjective results indicated that AiCE + SEMAR performed the best in terms of image quality. The mean image noise intensity was significantly lower in the AiCE + SEMAR group (25.35 ± 6.51 HU) than in the HIR (47.77 ± 8.76 HU), AiCE (42.93 ± 10.61 HU), and HIR + SEMAR (30.34 ± 4.87 HU) groups (p < 0.001). Additionally, AiCE + SEMAR exhibited the highest SNRs and CNRs, as well as the lowest AIs and ALs. Importantly, endoleaks and thrombi were most clearly visualized using AiCE + SEMAR. CONCLUSIONS: In comparison to other reconstruction methods, the combination of AiCE + SEMAR demonstrates superior image quality, thereby enhancing the detection capabilities and diagnostic confidence of potential complications such as early minor endleaks and thrombi following EVAR. This improvement in image quality could lead to more accurate diagnoses and better patient outcomes.

SAD score of intracranial aneurysms for rupture risk assessment based on high-resolution vessel wall imaging.

OBJECTIVE: We aimed to develop a comprehensive model that integrates the radiological, morphological, and clinical factors to assess rupture risk for intracranial aneurysms. METHODS: We prospectively enrolled patients with intracranial saccular aneurysms who underwent high-resolution vessel wall imaging (HR-VWI) preoperatively. Clinical characteristics, aneurysm features and aneurysm wall enhancement scale (AWES) were recorded. AWES was categorized into three grades (no/faint/strong enhancement) by comparing AWE to enhancement of the pituitary infundibulum or choroid plexus on HR-VWI. Univariate and multivariate logistic regression analyses were performed to determine risk factors associated with aneurysmal rupture. RESULTS: A total of 25 ruptured and 116 unruptured aneurysms were included. Multivariate logistic regression analysis revealed that non-ICA site (OR 6.25, 95% CI 1.35-28.30, P = 0.019), AWES (OR 5.99, 95% CI 2.51-14.29, P < 0.001) and daughter sac or lobulated shape (OR 6.22, 95% CI 1.68-23.16, P = 0.006) were independent factors associated with ruptured aneurysms. The "SAD" model was generated and named after the first letters of each of these factors. SAD scores of 0-4 predicted 0, 2%, 12%, 42% and 100% ruptured aneurysms, respectively. The area under the receiver operating characteristic curve for the SAD model was 0.8822. CONCLUSION: The SAD model aids in distinguishing aneurysm rupture status and in managing unruptured aneurysms. Larger cohort studies are needed to confirm its applicability in predicting the rupture risk of unruptured aneurysms.