背景:血管内动脉瘤修复术(EVAR)后的计算机断层扫描血管造影(CTA)图像的图像质量不令人满意,由于金属植入物造成的伪影阻碍了支架和隔离腔的清晰描绘,以及邻近的软组织。然而,由于更高的辐射剂量,目前减少这些伪影的技术仍需要进一步的进步,更长的处理时间等等。因此,这项研究的目的是评估利用单能量金属工件减少(SEMAR)以及一种新颖的深度学习图像重建技术的影响,被称为高级智能Clear-IQ引擎(AiCE),EVAR后CTA随访的图像质量。
方法:这项回顾性研究包括47例患者(平均年龄±标准差:68.6±7.8岁;37例男性),他们在EVAR后接受了CTA检查。使用四种不同的方法重建图像:混合迭代重建(HIR),AICE,HIR和SEMAR的组合(HIR+SEMAR),以及AiCE和SEMAR的组合(AiCE+SEMAR)。两个放射科医生,对重建技术视而不见,独立评估图像。定量评估包括图像噪声的测量,信噪比(SNR),对比噪声比(CNR),工件的最长长度(AL),和工件索引(AI)。随后在不同的重建方法中比较这些参数。
结果:主观结果表明,AiCE+SEMAR在图像质量方面表现最好。AiCE+SEMAR组的平均图像噪声强度(25.35±6.51HU)明显低于HIR组(47.77±8.76HU),AiCE(42.93±10.61HU),和HIR+SEMAR(30.34±4.87HU)组(p<0.001)。此外,AiCE+SEMAR展示了最高的SNR和CNR,以及最低的AIs和AL。重要的是,使用AiCE+SEMAR最清楚地观察到内漏和血栓。
结论:与其他重建方法相比,AiCE+SEMAR的组合展示了卓越的图像质量,从而提高了潜在并发症的检测能力和诊断信心,例如EVAR后的早期小端漏和血栓。图像质量的这种改善可以导致更准确的诊断和更好的患者结果。
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.
METHODS: 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.