Related papers: Intra-operative Optimal Flow Diverter Selection fo…
The precise segmentation of intracranial aneurysms and their parent vessels (IA-Vessel) is a critical step for hemodynamic analyses, which mainly depends on computational fluid dynamics (CFD). However, current segmentation methods…
Rapid and accurate simulation of cerebral aneurysm flow modifications by flow diverters (FDs) can help improving patient-specific intervention and predicting treatment outcome. However, with explicit FD devices being placed in…
Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of intracranial aneurysms (IA) from computed tomography…
Intrasaccular flow disruptors treat cerebral aneurysms by diverting the blood flow from the aneurysm sac. Residual flow into the sac after the intervention is a failure that could be due to the use of an undersized device, or to vascular…
Intracranial aneurysms (IAs) are serious cerebrovascular lesions found in approximately 5\% of the general population. Their rupture may lead to high mortality. Current methods for assessing IA risk focus on morphological and…
In intracranial aneurysm (IA) treatment, digital subtraction angiography (DSA) monitors device-induced hemodynamic changes. Quantitative angiography (QA) provides more precise assessments but is limited by hand-injection variability. This…
Bias from contrast injection variability is a significant obstacle to accurate intracranial aneurysm occlusion prediction using quantitative angiography and deep neural networks . This study explores bias removal and explainable AI for…
Flow diversion has become a key treatment modality for selected intracranial aneurysms, relying on the principle that a dense mesh of stent wires disrupts blood flow into the aneurysm sac, promoting thrombosis and vessel reconstruction.…
Introduction: Deep learning (DL) models can help detect intracranial aneurysms on CTA, but high false positive (FP) rates remain a barrier to clinical translation, despite improvement in model architectures and strategies like detection…
This study evaluates a multimodal machine learning framework for predicting treatment outcomes in intracranial aneurysms (IAs). Combining angiographic parametric imaging (API), patient biomarkers, and disease morphology, the framework aims…
In Angiographic Parametric Imaging (API), accurate estimation of parameters from Time Density Curves (TDC) is crucial. However, these estimations are often marred by errors arising from factors such as patient motion, procedural…
The treatment of intracranial aneurysms (IA) relies on angiography guidance using biplane views. However, accurate flow estimation and device sizing for treatment are often compromised by vessel overlap and foreshortening, which can obscure…
Intracranial aneurysms (IAs) are generally asymptomatic and thus often discovered incidentally on angiographic scans like 3D DSA, CTA and MRA. Skilled radiologists achieved a sensitivity of 88% by means of visual detection, which seems…
Coronary angiography is the main tool for assessing coronary artery disease, but visual grading of stenosis is variable and only moderately related to ischaemia. Wire based fractional flow reserve (FFR) improves lesion selection but is not…
Endoluminal reconstruction using flow diverters represents a novel paradigm for the minimally invasive treatment of intracranial aneurysms. The configuration assumed by these very dense braided stents once deployed within the parent vessel…
Intracranial aneurysms (IAs) are abnormal dilations of cerebral blood vessels that, if ruptured, can lead to life-threatening consequences. However, their small size and soft contrast in radiological scans often make it difficult to perform…
Predicting rupture risk and deciding on optimal treatment plan for intracranial aneurysms (IAs) is possible by quantification of their size and shape. For this purpose the IA has to be isolated from 3D angiogram. State-of-the-art methods…
Background: To develop an artificial intelligence system that can accurately identify acute non-traumatic intracranial hemorrhage (ICH) etiology based on non-contrast CT (NCCT) scans and investigate whether clinicians can benefit from it in…
Background: Computational fluid dynamics (CFD) has become an essential design tool for ventricular assist devices (VADs), where the goal of maximizing performance often conflicts with biocompatibility. This tradeoff becomes even more…
An abdominal aortic aneurysm (AAA) is a focal dilation of the aorta that, if not treated, tends to grow and may rupture. A significant unmet need in the assessment of AAA disease, for the diagnosis, prognosis and follow-up, is the…