Optimised passive marker device visibility and automatic marker detection for 3-T MRI-guided endovascular interventions: a pulsatile flow phantom study

Han Nijsink,Paul J. A. Borm,Sytse F. De Jong,Patrick Brand,Jurgen J. Fütterer,Michiel C. Warlé,Christiaan G. Overduin

doi:10.1186/s41747-022-00262-4

Abstract

BackgroundPassive 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.MethodsGuidewires 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).ResultsMedan 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.ConclusionMarker 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.

Highlights

Passive paramagnetic markers on magnetic resonance imaging (MRI)-compatible endovascular devices induce susceptibility artifacts, enabling MRI-visibility and real-time MRI-guidance
Quantitative image analysis Mean artifact width was positively correlated with paramagnetic material concentration and TE for both sequences
Both TE and the IONP concentration contributed to the artifact size in a cumulative manner (Figs. 4 and 5)

Summary

Introduction

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. Endovascular interventions are mainly guided by fluoroscopy, but this technique is hampered by limited two-dimensional visualisation, use of contrast agent and radiation burden to patient and physician [7, 8]. MRIguidance offers some potential advantages over fluoroscopy-guidance, relating to its intrinsic soft tissue contrast, and the possibility of functional imaging, without the need for iodinated contrast agents or ionising radiation [9]. Clinical adoption of MRI-guided vascular interventions is likely hampered by main challenges such as optimising between decreased image contrast and in plane resolution and higher frame rates (> 1 frames/s), MRI safety, costs and limited patient access. There is still a limited availability of MRI conditional and CE marked endovascular devices [7,8,9]

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