Real-time 3T MRI-guided cardiovascular catheterization in a porcine model using a glass-fiber epoxy-based guidewire

MRI-guided endovascular intervention: current methods and future potential

INTRODUCTION

Image-guided endovascular interventions, performed using the insertion and navigation of catheters through the vasculature, have been increasing in number over the years, as minimally invasive procedures continue to replace invasive surgical procedures. Such endovascular interventions are almost exclusively performed under x-ray fluoroscopy, which has the best spatial and temporal resolution of all clinical imaging modalities. Magnetic resonance imaging (MRI) offers unique advantages and could be an attractive alternative to conventional x-ray guidance, but also brings with it distinctive challenges.

AREAS COVERED

In this review, the benefits and limitations of MRI-guided endovascular interventions are addressed, systems and devices for guiding such interventions are summarized, and clinical applications are discussed.

EXPERT OPINION

MRI-guided endovascular interventions are still relatively new to the interventional radiology field, since significant technical hurdles remain to justify significant costs and demonstrate safety, design, and robustness. Clinical applications of MRI-guided interventions are promising but their full potential may not be realized until proper tools designed to function in the MRI environment are available. Translational research and further preclinical studies are needed before MRI-guided interventions will be practical in a clinical interventional setting.

Cardiac Diffusion Tensor Biomarkers of Chronic Infarction Based on In Vivo Data

In vivo cardiac diffusion tensor imaging (cDTI) data were acquired in swine subjects six to ten weeks post-myocardial infarction (MI) to identify microstructural-based biomarkers of MI. Diffusion tensor invariants, diffusion tensor eigenvalues, and radial diffusivity (RD) are evaluated in the infarct, border, and remote myocardium, and compared with extracellular volume fraction (ECV) and native T1 values. Additionally, to aid the interpretation of the experimental results, the diffusion of water molecules was numerically simulated as a function of ECV. Finally, findings based on in vivo measures were confirmed using higher-resolution and higher signal-to-noise data acquired ex vivo in the same subjects. Mean diffusivity, diffusion tensor eigenvalues, and RD increased in the infarct and border regions compared to remote myocardium, while fractional anisotropy decreased. Secondary (e₂) and tertiary (e₃) eigenvalues increased more significantly than the primary eigenvalue in the infarct and border regions. These findings were confirmed by the diffusion simulations. Although ECV presented the largest increase in infarct and border regions, e₂, e₃, and RD increased the most among non-contrast-based biomarkers. RD is of special interest as it summarizes the changes occurring in the radial direction and may be more robust than e₂ or e₃ alone.

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

BACKGROUND

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.

METHODS

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).

RESULTS

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.

CONCLUSION

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.

Diffusion biomarkers in chronic myocardial infarction

Cardiac diffusion tensor magnetic resonance imaging (cDTI) allows estimating the aggregate cardiomyocyte architecture in healthy subjects and its remodeling as a result of cardiac disease. In this study, cDTI was used to quantify microstructural changes occurring in swine (N=7) six to ten weeks after myocardial infarction. Each heart was extracted and imaged ex vivo with 1mm isotropic spatial resolution. Microstructural changes were quantified in the border zone and infarct region by comparing diffusion tensor invariants - fractional anisotropy (FA), mode, and mean diffusivity (MD) - radial diffusivity, and diffusion tensor eigenvalues with the corresponding values in the remote myocardium. MD and radial diffusivity increased in the infarct and border regions with respect to the remote myocardium (p<0.01). In contrast, FA and mode decreased in the infarct and border regions (p<0.01). Diffusion tensor eigenvalues also increased in the infarct and border regions, with a larger increase in the secondary and tertiary eigenvalues.