CT support of cardiac structural interventions

The role of CT in arrhythmia management-treatment planning and post-procedural imaging surveillance

Several interventional treatment options exist in patients with atrial and ventricular arrhythmia. Cardiac CT is routinely performed prior to occlusion of the left atrial appendage, pulmonary vein isolation, and cardiac device implantation. Besides the evaluation of coronary artery disease, cardiac CT provides isotropic, high-resolution CT images of the cardiac anatomy with the possibility of multiplanar reformations and three-dimensional reconstructions which are helpful to guide interventional treatment. In addition, cardiac CT is increasingly used to rapidly evaluate periprocedural complications and for the routine post-procedural imaging surveillance in patients after interventions. This review article will discuss current applications of pre- and post-interventional CT imaging in patients with arrhythmia.

Cardiac magnetic resonance imaging for preprocedural planning of percutaneous left atrial appendage closure

Introduction

Percutaneous closure of the left atrial appendage (LAA) facilitates stroke prevention in patients with atrial fibrillation. Optimal device selection and positioning are often challenging due to highly variable LAA shape and dimension and thus require accurate assessment of the respective anatomy. Transesophageal echocardiography (TEE) and x-ray fluoroscopy (XR) represent the gold standard imaging techniques. However, device underestimation has frequently been observed. Assessment based on 3-dimensional computer tomography (CTA) has been reported as more accurate but increases radiation and contrast agent burden. In this study, the use of non-contrast-enhanced cardiac magnetic resonance imaging (CMR) to support preprocedural planning for LAA closure (LAAc) was investigated.

Methods

CMR was performed in thirteen patients prior to LAAc. Based on the 3-dimensional CMR image data, the dimensions of the LAA were quantified and optimal C-arm angulations were determined and compared to periprocedural data. Quantitative figures used for evaluation of the technique comprised the maximum diameter, the diameter derived from perimeter and the area of the landing zone of the LAA.

Results

Perimeter- and area-based diameters derived from preprocedural CMR showed excellent congruency compared to those measured periprocedurally by XR, whereas the respective maximum diameter resulted in significant overestimation (p < 0.05). Compared to TEE assessment, CMR-derived diameters resulted in significantly larger dimensions (p < 0.05). The deviation of the maximum diameter to the diameters measured by XR and TEE correlated well with the ovality of the LAA. C-arm angulations used during the procedures were in agreement with those determined by CMR in case of circular LAA.

Discussion

This small pilot study demonstrates the potential of non-contrast-enhanced CMR to support preprocedural planning of LAAc. Diameter measurements based on LAA area and perimeter correlated well with the actual device selection parameters. CMR-derived determination of landing zones facilitated accurate C-arm angulation for optimal device positioning.

Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease

ABSTRACT

Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of modern cardiovascular diagnostics, with modalities such as cardiac computed tomography (CT) now recognized as first-line options for cardiovascular risk stratification and the assessment of stable or even unstable patients. To date, cardiovascular imaging has lagged behind other fields, such as oncology, in the clinical translational of artificial intelligence (AI)-based approaches. We hereby review the current status of AI in non-invasive cardiovascular imaging, using cardiac CT as a running example of how novel machine learning (ML)-based radiomic approaches can improve clinical care. The integration of ML, deep learning, and radiomic methods has revealed direct links between tissue imaging phenotyping and tissue biology, with important clinical implications. More specifically, we discuss the current evidence, strengths, limitations, and future directions for AI in cardiac imaging and CT, as well as lessons that can be learned from other areas. Finally, we propose a scientific framework in order to ensure the clinical and scientific validity of future studies in this novel, yet highly promising field. Still in its infancy, AI-based cardiovascular imaging has a lot to offer to both the patients and their doctors as it catalyzes the transition towards a more precise phenotyping of cardiovascular disease.

Determination of optimal fluoroscopic angulations for aorto-coronary ostial interventions from coronary computed tomography angiography

BACKGROUND

An optimal aorto-coronary angiographic projection, characterized by an orthogonal visualization of the proximal coronary artery, is crucial for interventional success. We determined the distribution of optimal C-arm positions and assessed their feasibility by invasive coronary angiography.

METHODS

Orthogonal aorto-coronary ostial angulations were determined in 310 CT data sets. In 100 patients undergoing subsequent invasive angiography, we assessed if the CT-predicted angulations were achievable by the C-arm system. If the predicted projection was not achievable due to mechanical constraints of the C-arm system, the most close, achievable angulation was determined. Patient characteristics were analyzed regarding the distribution of optimal angulations and its feasibility by the C-arm system.

RESULTS

For the left ostium, CT revealed a mean angulation of LAO 23 ​± ​21°/cranial 25 ​± ​23° (90% of patients with a LAO/cranial angulation, 3% LAO/caudal, 4% RAO/cranial, 3% RAO/caudal) and were achievable by the C-arm system in 87% of patients. For the right ostium, the mean CT-predicted orthogonal angulation was LAO 36 ​± ​37°/cranial 36 ​± ​51° (84% LAO/cranial, 2% LAO/caudal, 14% RAO/caudal) and achievable by the C-arm system in 45% of patients. For the left ostium, a higher body weight was associated with a steeper LAO/cranial angulation being less feasible by the C-arm system due to mechanical constraints.

CONCLUSIONS

Orthogonal aorto-left coronary angulations show a relative narrow distribution predominately in LAO/cranial position whereas a wider range of angulations was found for the right coronary ostium. The feasibility of CT-predicted angulations by the C-arm system is more restricted for the right than the left coronary ostium.