Validation of a novel stand-alone software tool for image guided cardiac catheter therapy

Interventional cardiac magnetic resonance imaging: current applications, technology readiness level, and future perspectives

BACKGROUND

Cardiac magnetic resonance (CMR) provides excellent temporal and spatial resolution, tissue characterization, and flow measurements. This enables major advantages when guiding cardiac invasive procedures compared with X-ray fluoroscopy or ultrasound guidance. However, clinical implementation is limited due to limited availability of technological advancements in magnetic resonance imaging (MRI) compatible equipment. A systematic review of the available literature on past and present applications of interventional MR and its technology readiness level (TRL) was performed, also suggesting future applications.

METHODS

A structured literature search was performed using PubMed. Search terms were focused on interventional CMR, cardiac catheterization, and other cardiac invasive procedures. All search results were screened for relevance by language, title, and abstract. TRL was adjusted for use in this article, level 1 being in a hypothetical stage and level 9 being widespread clinical translation. The papers were categorized by the type of procedure and the TRL was estimated.

RESULTS

Of 466 papers, 117 papers met the inclusion criteria. TRL was most frequently estimated at level 5 meaning only applicable to in vivo animal studies. Diagnostic right heart catheterization and cavotricuspid isthmus ablation had the highest TRL of 8, meaning proven feasibility and efficacy in a series of humans.

CONCLUSION

This article shows that interventional CMR has a potential widespread application although clinical translation is at a modest level with TRL usually at 5. Future development should be directed toward availability of MR-compatible equipment and further improvement of the CMR techniques. This could lead to increased TRL of interventional CMR providing better treatment.

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.

3D Myocardial Scar Prediction Model Derived from Multimodality Analysis of Electromechanical Mapping and Magnetic Resonance Imaging

Many cardiac catheter interventions require accurate discrimination between healthy and infarcted myocardia. The gold standard for infarct imaging is late gadolinium-enhanced MRI (LGE-MRI), but during cardiac procedures electroanatomical or electromechanical mapping (EAM or EMM, respectively) is usually employed. We aimed to improve the ability of EMM to identify myocardial infarction by combining multiple EMM parameters in a statistical model. From a porcine infarction model, 3D electromechanical maps were 3D registered to LGE-MRI. A multivariable mixed-effects logistic regression model was fitted to predict the presence of infarct based on EMM parameters. Furthermore, we correlated feature-tracking strain parameters to EMM measures of local mechanical deformation. We registered 787 EMM points from 13 animals to the corresponding MRI locations. The mean registration error was 2.5 ± 1.16 mm. Our model showed a strong ability to predict the presence of infarction (C-statistic = 0.85). Strain parameters were only weakly correlated to EMM measures. The model is accurate in discriminating infarcted from healthy myocardium. Unipolar and bipolar voltages were the strongest predictors.

Multimodality imaging for real-time image-guided left ventricular lead placement during cardiac resynchronization therapy implantations

This study was performed to evaluate the feasibility of intra-procedural visualization of optimal pacing sites and image-guided left ventricular (LV) lead placement in cardiac resynchronization therapy (CRT). In fifteen patients (10 males, 68 ± 11 years, 7 with ischemic cardiomyopathy and ejection fraction of 26 ± 5%), optimal pacing sites were identified pre-procedurally using cardiac imaging. Cardiac magnetic resonance (CMR) derived scar and dyssynchrony maps were created for all patients. In six patients the anatomy of the left phrenic nerve (LPN) and coronary sinus ostium was assessed via a computed tomography (CT) scan. By overlaying the CMR and CT dataset onto live fluoroscopy, aforementioned structures were visualized during LV lead implantation. In the first nine patients, the platform was tested, yet, no real-time image-guidance was implemented. In the last six patients real-time image-guided LV lead placement was successfully executed. CRT implant and fluoroscopy times were similar to previous procedures and all leads were placed close to the target area but away from scarred myocardium and the LPN. Patients that received real-time image-guided LV lead implantation were paced closer to the target area compared to patients that did not receive real-time image-guidance (8 mm [IQR 0–22] vs 26 mm [IQR 17–46], p = 0.04), and displayed marked LV reverse remodeling at 6 months follow up with a mean LVESV change of −30 ± 10% and a mean LVEF improvement of 15 ± 5%. Real-time image-guided LV lead implantation is feasible and may prove useful for achieving the optimal LV lead position.