Echocardiography-A Journal of Cardiovascular Ultrasound and Allied Techniques最新文献

Pulmonary hypertension (PH) is a disease characterized by pathologically increased pressure in the pulmonary arteries, defined by a mean pulmonary arterial pressure (mPAP) >20 mmHg at rest measured with right heart catheterization (RHC). This definition encompasses pathologies with very different pathological backgrounds, ultimately resulting in PH. For this reason, the latter can be possibly (though seldom) accompanied by cardiomyopathies, pathologies characterized by a structural and functionally abnormal myocardium not secondary to coronary disease, hypertension, valvular disease, or congenital heart disease. Notable examples of these diseases are sarcoidosis (a multi-systemic inflammatory granulomatous disease, possibly involving the lung and the heart), systemic sclerosis (SSc) (a connective tissue disease [CTD], possibly causing interstitial lung disease [ILD], direct as well indirect involvement of the cardiovascular system), and chronic kidney disease (CKD) (a progressive pathological process involving the kidneys, with multi-systemic involvement and possible development of a peculiar form of cardiomyopathy, i.e., uremic cardiomyopathy [UC]). The diagnostic work-up of patients with coexistent PH and cardiomyopathies implies the use of multiple imaging techniques, with computed tomography (CT) and cardiovascular magnetic resonance (CMR) being among the most important. The knowledge of CT and MRI findings, together with a suggestive clinical picture, forms the basis for a correct diagnosis, therefore it is important for the radiologist to recognize them in complex clinical scenarios. The advent of new technologies (e.g., photon counting detectors) and the development of new artificial intelligence (AI) algorithms will further pave the way for improved diagnostic processes (also regarding this kind of pathologies) as well as allowing to perform a better prognostic evaluation.

Cardiomyopathies represent a diverse group of heart diseases that can be broadly classified into ischemic and nonischemic etiologies, each requiring distinct diagnostic approaches. Noninvasive imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), play a pivotal role in the diagnosis, risk stratification, and prognosis of these conditions. This paper reviews the characteristic CT and MRI findings associated with ischemic cardiomyopathy (ICM) and nonischemic cardiomyopathy (NICM), focusing on their ability to provide detailed anatomical, functional, and tissue characterization. In ICM, CT and MRI reveal myocardial scarring, infarct size, and coronary artery disease, while MRI further distinguishes tissue viability through late gadolinium enhancement (LGE). Conversely, nonischemic cardiomyopathies demonstrate a wide array of findings, with MRI's LGE pattern analysis being particularly critical for identifying specific subtypes, such as restrictive, hypertrophic, or dilated cardiomyopathies. By comparing the strengths and limitations of these modalities, this paper highlights their complementary roles in improving diagnostic accuracy, risk stratification, prognosis, and therapeutic decision making in both ischemic and nonischemic cardiomyopathies.

Artificial intelligence (AI) has transformed medical imaging by detecting insights and patterns often imperceptible to the human eye, enhancing diagnostic accuracy and efficiency. In cardiovascular imaging, numerous AI models have been developed for cardiac computed tomography (CCT), a primary tool for assessing coronary artery disease (CAD). CCT provides comprehensive, non-invasive assessment, including plaque burden, stenosis severity, and functional assessments such as CT-derived fractional flow reserve (FFRct). Its prognostic value in predicting major adverse cardiovascular events (MACE) has increased the demand for CCT, consequently adding to radiologists’ workloads. This review aims to examine AI's role in CCT for ischemic heart disease, highlighting its potential to streamline workflows and improve the efficiency of cardiac care through machine learning and deep learning applications.

The subcostal en face view of the tricuspid valve has emerged as a promising echocardiographic technique for visualizing right ventricular (RV) lead positions in patients with cardiac implantable electronic devices (CIEDs). This approach offers the potential to prevent tricuspid regurgitation (TR) by allowing rhythmologists to adjust lead positions in real time, ensuring optimal device placement and minimizing valve interference. A study by Zach et al. (2024) demonstrated the feasibility of this technique in 64% of patients, offering a viable alternative when 3D echocardiography is unavailable or non-diagnostic. The study found no significant correlation between lead position and TR severity, suggesting that other factors may contribute to TR development. While the subcostal en face view holds significant promise, limitations such as poor image quality in patients with obesity, abdominal pathologies, or multiple leads, as well as the need for experience to maximize success rates, must be addressed. Future prospective studies are needed to validate the clinical benefits of the subcostal en face view during CIED implantation, including its impact on procedural duration and TR prevention. This technique represents an important step toward enhancing patient safety and improving CIED implantation protocols.