CT Angiography

Pulmonary hypertension (PH) is a hemodynamic and pathophysiological condition defined by a mean pulmonary artery pressure (mPAP) greater than 20 mmHg at rest, as confirmed by right heart catheterization (RHC) (1). The global prevalence of PH is estimated at approximately 1% of the adult population, rising to 10% or more in individuals over 65 years of age.Pre-capillary pulmonary hypertension, which includes pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH), is characterized by a pulmonary arterial wedge pressure (PAWP) ≤15 mmHg and an elevated pulmonary vascular resistance (PVR) ≥2 Wood units (2) Computed tomography pulmonary angiography (CTPA) is routinely performed in the diagnostic workup of PH and offers a non invasive alternative that can generate multiple quantitative metrics from a single scan (3). Traditional CTPA metrics, including main pulmonary artery diameter (MPAd), the ratio of MPAd to ascending aortic diameter, central pulmonary arteries, and right ventricular to left ventricular diameter (RV/LV ratio) were previously studied in PH patients (4,5,6) Most existing studies have evaluated CTPA metrics in isolation or within single PH subgroups, limiting generalizability across the broader pre-capillary PH population (7). Therefore, this study aims to systematically investigate the correlation between multiple quantitative CTPA metrics and key invasive hemodynamic parameters derived from RHC in patients with confirmed pre-capillary pulmonary hypertension

Coronary artery disease (CAD) is the leading cause of mortality and morbidity worldwide. Coronary Computed Tomography angiography (CCTA) gained a pivotal clinical role for excellent sensitivity in rule-out CAD, but has limited specificity for a tendency to overestimate stenoses and for the lack of information about their hemodynamic impact. Fractional Flow Reserve derived from CT (FFR-CT) and stress CT perfusion (CTP) have been recently proposed to complement CCTA in the non-invasive assessment of myocardial ischemia, increasing the specificity and avoiding unnecessary catheterization. However, on energy-integrating (EID)-CT, FFR-CT has suboptimal performance, while CTP is affected by high radiation exposure. Both these approaches may benefit by the introduction of the new Photon Counting Detector (PCD)-CT technology, but data completely lacks. Aim of the study is to assess the performance of PCD-CT in the identification of significant CAD combining CCTA with FFR-CT and spectral CTP.

The primary objective of this study is to evaluate whether a management strategy based on coronary computed tomography angiography (CCTA) for patients with non-obstructive coronary artery disease can improve the LDL-C target achievement rate compared to a traditional management strategy without follow-up CCTA, thereby reducing the incidence of major adverse cardiovascular and cerebrovascular events over a 3 years period, including all-cause mortality, myocardial infarction, ischemia driven revascularation and stroke.

The primary objective was to evaluate the effect of PCSK9 inhibitors in addition to the background lipid-modifying therapy (LMT), compared with standard LMT in terms of clinical outcomes in patients with coronary CT angiography (CCTA)-detected high-risk plaques.

This study (IEDAL study) intends to prospectively enroll more than 6450 patients who will undergo head CT angiography (CTA) scanning in the outpatient clinic. It will be carried out in 21 hospitals in more than 10 provinces in China. The patient's head CTA images will be randomly assigned to the True-AI and Sham-AI group with a ratio of 1:1, and the patients and radiologists are unaware of the allocation. The primary outcomes are sensitivity and specificity of detecting intracranial aneurysms. The secondary outcomes focus on the prognosis and outcomes of the patients.

This study (IDEAL 2) is a nationwide stepped-wedge cluster-randomized trial designed to prospectively enroll over 14,400 patients undergoing outpatient head CT angiography (CTA). The trial will be conducted across more than 72 regional hospitals in China. Clusters were randomly assigned to nine randomization groups. In accordance with the stepped-wedge design, clusters will sequentially transition from the control condition (standard human diagnosis) to the intervention condition (AI-assisted diagnosis) at regular intervals over a 10-month period, until all clusters receive the intervention. The primary outcome is the detection rate of intracranial aneurysms. Secondary outcomes include patient prognosis and clinical outcomes.

Acute ischemic stroke (AIS) caused by intracranial large vessel occlusion (LVO) in the anterior circulation significantly contributes to stroke-related disability and mortality. Recent randomized controlled trials have demonstrated substantial benefits of endovascular thrombectomy (EVT) when patients are appropriately triaged beforehand. However, accurately orienting the 'missed segment' during EVT remains challenging. Guide-wires often fail to navigate through the occlusion or are mistakenly directed into the small tranches or even cause vessel rupture. To address this clinical need, the investigators developed an artificial intelligence (AI) algorithm to automate the reconstruction of CT angiography (CTA), focusing on the occluded LVO segment. To evaluate the clinical utility of this AI algorithm, the investigators propose a prospective, stepped-wedge cluster-randomized study to determine whether integrating our AI algorithm into AIS care flow can reduce the time for first pass of the thrombus by improving the visualization of the occluded segment on CTA. Physicians will assess patient eligibility for thrombectomy, and all selected patients will receive standard care according to current guidelines. This approach is expected to enhance patient treatment outcomes for endovascular thrombectomy by leveraging readily available data.