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Aorta

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G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning.

PLoS computational biology
Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances in genetics and histomechanical characterization provide significant ...

Segmentation of human aorta using 3D nnU-net-oriented deep learning.

The Review of scientific instruments
Computed tomography angiography (CTA) has become the main imaging technique for cardiovascular diseases. Before performing the transcatheter aortic valve intervention operation, segmenting images of the aortic sinus and nearby cardiovascular tissue f...

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...

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...

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...

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...

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...

Framework Development for Patient-Specific Compliant Aortic Dissection Phantom Model Fabrication: Magnetic Resonance Imaging Validation and Deep-Learning Segmentation.

Journal of biomechanical engineering
Type B aortic dissection is a life-threatening medical emergency that can result in rupture of the aorta. Due to the complexity of patient-specific characteristics, only limited information on flow patterns in dissected aortas has been reported in th...

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...