Ozpeynirci Yigit, Gorodezky Margarita, Sanches Augusto Fava, Mandava Sagar, Solana Ana Beatriz, Liebig Thomas
Institute for Diagnostic and Interventional Neuroradiology, University Hospital, Ludwig-Maximilians-University (LMU), Marchioninistr. 15, 81377 Munich, Germany.
GE HealthCare, 80807 Munich, Germany.
Diagnostics (Basel). 2024 May 30;14(11):1146. doi: 10.3390/diagnostics14111146.
Silent MRA has shown promising results in evaluating the stents used for intracranial aneurysm treatment. A deep learning-based denoising and deranging algorithm was recently introduced by GE HealthCare. The purpose of this study was to compare the performance of several MRA techniques regarding lumen visibility in silicone models with flow diverter stents.
Two Surpass Evolve stents of different sizes were implanted in two silicone tubes. The tubes were placed in separate boxes in the straight position and in two different curve configurations and connected to a pulsatile pump to construct a flow loop. Using a 3.0T MRI scanner, TOF and silent MRA images were acquired, and deep learning reconstruction was applied to the silent MRA dataset. The intraluminal signal intensity in the stent (SI), in the tube outside the stent (SI), and of the background (SI) were measured for each scan.
The SI/SI and SI/SI ratios were higher in the silent scans and DL-based reconstructions than in the TOF images. The stent tips created severe artefacts in the TOF images, which could not be observed in the silent scans.
Our study demonstrates that the DL reconstruction algorithm improves the quality of the silent MRA technique in evaluating the flow diverter stent patency.
静态磁共振血管造影（MRA）在评估用于颅内动脉瘤治疗的支架方面已显示出有前景的结果。通用电气医疗集团最近推出了一种基于深度学习的去噪和去伪影算法。本研究的目的是比较几种MRA技术在带有血流导向支架的硅胶模型中关于管腔可视性的性能。
将两个不同尺寸的Surpass Evolve支架植入两根硅胶管中。将管子笔直放置以及以两种不同的弯曲构型分别置于单独的盒子中，并连接到脉动泵以构建血流环路。使用3.0T磁共振成像（MRI）扫描仪采集时间飞跃法（TOF）和静态MRA图像，并将深度学习重建应用于静态MRA数据集。对每次扫描测量支架内的腔内信号强度（SI）、支架外管内的信号强度（SI）以及背景的信号强度（SI）。
与TOF图像相比，静态扫描和基于深度学习的重建中的SI/SI和SI/SI比值更高。支架末端在TOF图像中产生严重伪影，而在静态扫描中未观察到。
我们的研究表明，深度学习重建算法在评估血流导向支架通畅性方面提高了静态MRA技术的质量。
