How to improve navigation during cardioband transcatheter tricuspid annuloplasty

Advances in Imaging for Tricuspid Transcatheter Edge-to-Edge Repair: Lessons Learned and Future Perspectives

Severe tricuspid valve (TV) regurgitation (TR) has been associated with adverse long-term outcomes in several natural history studies, but isolated TV surgery presents high mortality and morbidity rates. Transcatheter tricuspid valve interventions (TTVI) therefore represent a promising field and may currently be considered in patients with severe secondary TR that have a prohibitive surgical risk. Tricuspid transcatheter edge-to-edge repair (T-TEER) represents one of the most frequently used TTVI options. Accurate imaging of the tricuspid valve (TV) apparatus is crucial for T-TEER preprocedural planning, in order to select the right candidates, and is also fundamental for intraprocedural guidance and post-procedural follow-up. Although transesophageal echocardiography represents the main imaging modality, we describe the utility and additional value of other imaging modalities such as cardiac CT and MRI, intracardiac echocardiography, fluoroscopy, and fusion imaging to assist T-TEER. Developments in the field of 3D printing, computational models, and artificial intelligence hold great promise in improving the assessment and management of patients with valvular heart disease.

The year 2021 in the European Heart Journal: Cardiovascular Imaging Part II

The European Heart Journal-Cardiovascular Imaging was launched in 2012 and has during these years become one of the leading multimodality cardiovascular imaging journals. The journal is currently ranked as Number 19 among all cardiovascular journals. It has an impressive impact factor of 9.130. The most important studies published in our Journal from 2021 will be highlighted in two reports. Part II will focus on valvular heart disease, heart failure, cardiomyopathies, and congenital heart disease, while Part I of the review has focused on studies about myocardial function and risk prediction, myocardial ischaemia, and emerging techniques in cardiovascular imaging.

Computed tomography angiography/magnetic resonance imaging-based preprocedural planning and guidance in the interventional treatment of structural heart disease

Preprocedural planning and periprocedural guidance based on image fusion are widely established techniques supporting the interventional treatment of structural heart disease. However, these two techniques are typically used independently. Previous works have already demonstrated the benefits of integrating planning details into image fusion but are limited to a few applications and the availability of the proprietary tools used. We propose a vendor-independent approach to integrate planning details into periprocedural image fusion facilitating guidance during interventional treatment. In this work, we demonstrate the feasibility of integrating planning details derived from computer tomography and magnetic resonance imaging into periprocedural image fusion with open-source and commercially established tools. The integration of preprocedural planning details into periprocedural image fusion has the potential to support safe and efficient interventional treatment of structural heart disease.

Deep learning-based framework for motion-compensated image fusion in catheterization procedures

OBJECTIVE

Augmenting X-ray (XR) fluoroscopy with 3D anatomic overlays is an essential technique to improve the guidance of the catheterization procedures. Unfortunately, cardiac and respiratory motion compromises the augmented fluoroscopy. Motion compensation methods can be applied to update the overlay of a static model with regard to respiratory and cardiac motion. We investigate the feasibility of motion detection between two fluoroscopic frames by applying a convolutional neural network (CNN). Its integration in the existing open-source software framework 3D-XGuide is demonstrated, such extending its functionality to automatic motion detection and compensation.

METHODS

The CNN is trained on reference data generated from tracking of the rapid pacing catheter tip by applying template matching with normalized cross-correlation (CC). The developed CNN motion compensation model is packaged in a standalone web service, allowing for independent use via a REST API. For testing and demonstration purposes, we have extended the functionality of 3D-XGuide navigation framework by an additional motion compensation module, which uses the displacement predictions of the standalone CNN model service for motion compensation of the static 3D model overlay. We provide the source code on GitHub under BSD license.

RESULTS

The performance of the CNN motion compensation model was evaluated on a total of 1690 fluoroscopic image pairs from ten clinical datasets. The CNN model-based motion compensation method clearly overperformed the tracking of the rapid pacing catheter tip with CC with prediction frame rates suitable for live application in the clinical setting.

CONCLUSION

A novel CNN model-based method for automatic motion compensation during fusion of 3D anatomic models with XR fluoroscopy is introduced and its integration with a real software application demonstrated. Automatic motion extraction from 2D XR images using a CNN model appears as a substantial improvement for reliable augmentation during catheter interventions.