From 4D Medical Images (CT, MRI, and Ultrasound) to 4D Structured Mesh Models of the Left Ventricular Endocardium for Patient-Specific Simulations.
Federico CanèBenedict VerheggheMatthieu De BeulePhilippe B BertrandRob J Van der GeestPatrick SegersGianluca De SantisPublished in: BioMed research international (2018)
With cardiovascular disease (CVD) remaining the primary cause of death worldwide, early detection of CVDs becomes essential. The intracardiac flow is an important component of ventricular function, motion kinetics, wash-out of ventricular chambers, and ventricular energetics. Coupling between Computational Fluid Dynamics (CFD) simulations and medical images can play a fundamental role in terms of patient-specific diagnostic tools. From a technical perspective, CFD simulations with moving boundaries could easily lead to negative volumes errors and the sudden failure of the simulation. The generation of high-quality 4D meshes (3D in space + time) with 1-to-1 vertex becomes essential to perform a CFD simulation with moving boundaries. In this context, we developed a semiautomatic morphing tool able to create 4D high-quality structured meshes starting from a segmented 4D dataset. To prove the versatility and efficiency, the method was tested on three different 4D datasets (Ultrasound, MRI, and CT) by evaluating the quality and accuracy of the resulting 4D meshes. Furthermore, an estimation of some physiological quantities is accomplished for the 4D CT reconstruction. Future research will aim at extending the region of interest, further automation of the meshing algorithm, and generating structured hexahedral mesh models both for the blood and myocardial volume.
Keyphrases
- left ventricular
- contrast enhanced
- magnetic resonance imaging
- computed tomography
- heart failure
- deep learning
- image quality
- dual energy
- cardiovascular disease
- molecular dynamics
- magnetic resonance
- diffusion weighted imaging
- hypertrophic cardiomyopathy
- healthcare
- acute myocardial infarction
- monte carlo
- cardiac resynchronization therapy
- mitral valve
- left atrial
- convolutional neural network
- catheter ablation
- aortic stenosis
- optical coherence tomography
- machine learning
- positron emission tomography
- patient safety
- ultrasound guided
- coronary artery disease
- acute coronary syndrome
- rna seq
- metabolic syndrome
- mass spectrometry
- atrial fibrillation
- high resolution
- pet ct
- neural network