AIMC Topic: Aorta

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An Anatomy- and Topology-Preserving Framework for Coronary Artery Segmentation.

IEEE transactions on medical imaging
Coronary artery segmentation is critical for coronary artery disease diagnosis but challenging due to its tortuous course with numerous small branches and inter-subject variations. Most existing studies ignore important anatomical information and vas...

CT angiography prior to endovascular procedures: can artificial intelligence improve reporting?

Physical and engineering sciences in medicine
CT angiography prior to endovascular aortic surgery is the standard non-invasive imaging method for evaluation of aortic dimensions and access sites. A detailed report is crucial to a proper planning. We assessed Artificial Intelligence (AI)-algorith...

Deep Learning-Based Analysis of Aortic Morphology From Three-Dimensional MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Quantification of aortic morphology plays an important role in the evaluation and follow-up assessment of patients with aortic diseases, but often requires labor-intensive and operator-dependent measurements. Automatic solutions would hel...

A precise blood transfusion evaluation model for aortic surgery: a single-center retrospective study.

Journal of clinical monitoring and computing
Cardiac aortic surgery is an extremely complicated procedure that often requires large volume blood transfusions during the operation. Currently, it is not possible to accurately estimate the intraoperative blood transfusion volume before surgery. Th...

Impact of retraining a deep learning algorithm for improving guideline-compliant aortic diameter measurements on non-gated chest CT.

European journal of radiology
PURPOSE/OBJECTIVE: Reliable detection of thoracic aortic dilatation (TAD) is mandatory in clinical routine. For ECG-gated CT angiography, automated deep learning (DL) algorithms are established for diameter measurements according to current guideline...

Physics-informed neural networks (PINNs) for 4D hemodynamics prediction: An investigation of optimal framework based on vascular morphology.

Computers in biology and medicine
Hemodynamic parameters are of great significance in the clinical diagnosis and treatment of cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of hemodynamics remains a challenge for current invasive detection and simul...

Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection.

European radiology experimental
BACKGROUND: Guidelines recommend that aortic dimension measurements in aortic dissection should include the aortic wall. This study aimed to evaluate two-dimensional (2D)- and three-dimensional (3D)-based deep learning approaches for extraction of ou...

Hemodynamic study of blood flow in the aorta during the interventional robot treatment using fluid-structure interaction.

Biomechanics and modeling in mechanobiology
An interventional robot is a means for vascular diagnosis and treatment, and it can perform dredging, releasing drug and operating. Normal hemodynamic indicators are a prerequisite for the application of interventional robots. The current hemodynamic...

Transthoracic Aortic Clamp Technique for Port-Only Endoscopic Robotic Mitral Surgery.

Innovations (Philadelphia, Pa.)
OBJECTIVE: Many robotic mitral surgeons utilize right thoracotomy with transthoracic clamping of the aorta, while a smaller number employ a port-only endoscopic approach with endoaortic balloon occlusion of the aorta. We present our technique for a p...

4D segmentation of the thoracic aorta from 4D flow MRI using deep learning.

Magnetic resonance imaging
BACKGROUND: 4D flow MRI allows the analysis of hemodynamic changes in the aorta caused by pathologies such as thoracic aortic aneurysms (TAA). For personalized management of TAA, new biomarkers are required to analyze the effect of fluid structure it...