Department of Medical Imaging, Radboudumc, Geert Grooteplein Zuid 10, 6525, Nijmegen, GA, The Netherlands.
Department of Cardiothoracic Surgery, Radboudumc, Nijmegen, The Netherlands.
Eur Radiol Exp. 2022 Feb 24;6(1):11. doi: 10.1186/s41747-022-00262-4.
Passive paramagnetic markers on magnetic resonance imaging (MRI)-compatible endovascular devices induce susceptibility artifacts, enabling MRI-visibility and real-time MRI-guidance. Optimised visibility is crucial for automatic detection and device tracking but depends on MRI technical parameters and marker characteristics. We assessed marker visibility and automatic detection robustness for varying MRI parameters and marker characteristics in a pulsatile flow phantom.
Guidewires with varying iron(II,III) oxide nanoparticle (IONP) concentration markers were imaged using gradient-echo (GRE) and balanced steady-state free precession (bSSFP) sequences at 3 T. Furthermore, echo time (TE), slice thickness (ST) and phase encoding direction (PED) were varied. Artifact width was measured and contrast-to-noise ratios were calculated. Marker visibility and image quality were scored by two MRI interventional radiologists. Additionally, a deep learning model for automatic marker detection was trained and the effects of the parameters on detection performance were evaluated. Two-tailed Wilcoxon signed-rank tests were used (significance level, p < 0.05).
Medan artifact width (IQR) was larger in bSSFP compared to GRE images (12.7 mm (11.0-15.2) versus 8.4 mm (6.5-11.0)) (p < 0.001) and showed a positive relation with TE and IONP concentration. Switching PED and doubling ST had limited effect on artifact width. Image quality assessment scores were higher for GRE compared to bSSFP images. The deep learning model automatically detected the markers. However, the model performance was reduced after adjusting PED, TE, and IONP concentration.
Marker visibility was sufficient and a large range of artifact sizes was generated by adjusting TE and IONP concentration. Deep learning-based marker detection was feasible but performance decreased for altered MR parameters. These factors should be considered to optimise device visibility and ensure reliable automatic marker detectability in MRI-guided endovascular interventions.
磁共振成像（MRI）兼容的血管内装置上的被动顺磁标记物会引起磁化率伪影，从而实现 MRI 可视性和实时 MRI 引导。优化可视性对于自动检测和设备跟踪至关重要，但取决于 MRI 技术参数和标记物特性。我们在脉动流体模中评估了不同 MRI 参数和标记物特性下的标记可视性和自动检测鲁棒性。
使用梯度回波（GRE）和平衡稳态自由进动（bSSFP）序列在 3 T 下对具有不同铁（II，III）氧化物纳米颗粒（IONP）浓度标记物的导丝进行成像。此外，还改变了回波时间（TE）、切片厚度（ST）和相位编码方向（PED）。测量了伪影宽度并计算了对比噪声比。两名 MRI 介入放射科医师对标记可视性和图像质量进行了评分。此外，还训练了用于自动标记检测的深度学习模型，并评估了参数对检测性能的影响。使用双侧 Wilcoxon 符号秩检验（显著性水平，p < 0.05）。
bSSFP 图像中的 Medan 伪影宽度（IQR）大于 GRE 图像（12.7 毫米（11.0-15.2）与 8.4 毫米（6.5-11.0））（p < 0.001），并且与 TE 和 IONP 浓度呈正相关。切换 PED 和将 ST 加倍对伪影宽度的影响有限。与 bSSFP 图像相比，GRE 图像的图像质量评估评分更高。深度学习模型自动检测到标记物。然而，调整 PED、TE 和 IONP 浓度后，模型性能会降低。
通过调整 TE 和 IONP 浓度，可以产生足够的标记可视性和较大范围的伪影大小。基于深度学习的标记检测是可行的，但改变 MRI 参数会降低性能。在 MRI 引导的血管内介入中，应考虑这些因素以优化设备可视性并确保可靠的自动标记检测。
