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Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide. Computational medicine and digital twins hold promise in mitigating the impact and prevalence of CVD. Recent advances in image-based computational methods have enabled the quantification of functional and biologically important metrics that would otherwise be difficult to obtain from the standard of care. However, significant challenges remain due to the manual/semi-automated nature of the processes and the domain expertise required to perform them. This paper addresses these challenges by proposing a novel framework that builds on our recently developed direct point cloud-to-CFD approach using immersogeometric analysis. The proposed method leverages advanced auto-segmentation techniques to extract medically relevant geometries as point clouds, which are then directly used for CFD simulations. The framework is validated using benchmark flow problems with analytical and computational solutions and is subsequently applied to patient-specific images to demonstrate its capabilities. The results highlight the method's ability to facilitate rapid CFD simulations directly on point clouds derived from patient scans, underscoring its potential to accelerate the image-to-simulation pipeline and enable the tractability of cardiovascular digital twins. 

Magnetic particle tracking (MPT) is a recently developed non-invasive measurement technique that has gained popularity for studying dense particulate or granular flows. This method involves tracking the trajectory of a magnetically labeled particle, the field of which is modeled as a dipole. The nature of this method allows it to be used in opaque environments, which can be highly beneficial for the measurement of dense particle dynamics. However, since the magnetic field of the particle used is weak, the signal-to-noise ratio is usually low. The noise from the measuring devices contaminates the reconstruction of the magnetic tracer’s trajectory. A filter is then needed to reduce the noise in the final trajectory results. In this work, we present a neural network-based framework for MPT trajectory reconstruction and filtering, which yields accurate results and operates at very high speed. The reconstruction derived from this framework is compared to the state-of-the-art extended Kalman filter-based reconstruction. 

Abstract Computational hemodynamic modeling has been widely used in cardiovascular research and healthcare. However, the reliability of model predictions is largely dependent on the uncertainties of modeling parameters and boundary conditions, which should be carefully quantified and further reduced with available measurements. In this work, we focus on propagating and reducing the uncertainty of vascular geometries within a Bayesian framework. A novel deep learning (DL)-assisted parallel Markov chain Monte Carlo (MCMC) method is presented to enable efficient Bayesian posterior sampling and geometric uncertainty reduction. A DL model is built to approximate the geometry-to-hemodynamic map, which is trained actively using online data collected from parallel MCMC chains and utilized for early rejection of unlikely proposals to facilitate convergence with less expensive full-order model evaluations. Numerical studies on two-dimensional aortic flows are conducted to demonstrate the effectiveness and merit of the proposed method. 

Optimization and uncertainty quantification have been playing an increasingly important role in computational hemodynamics. However, existing methods based on principled modeling and classic numerical techniques have faced significant challenges, particularly when it comes to complex three-dimensional (3D) patient-specific shapes in the real world. First, it is notoriously challenging to parameterize the input space of arbitrary complex 3D geometries. Second, the process often involves massive forward simulations, which are extremely computationally demanding or even infeasible. We propose a novel deep learning surrogate modeling solution to address these challenges and enable rapid hemodynamic predictions. Specifically, a statistical generative model for 3D patient-specific shapes is developed based on a small set of baseline patient-specific geometries. An unsupervised shape correspondence solution is used to enable geometric morphing and scalable shape synthesis statistically. Moreover, a simulation routine is developed for automatic data generation by automatic meshing, boundary setting, simulation, and post-processing. An efficient supervised learning solution is proposed to map the geometric inputs to the hemodynamics predictions in latent spaces. Numerical studies on aortic flows are conducted to demonstrate the effectiveness and merit of the proposed techniques. 

Magnetic particle tracking is a recently developed technology that can measure the translation and rotation of a particle in an opaque environment like a turbidity flow and fluidized-bed flow. The trajectory reconstruction usually relies on numerical optimization or filtering, which involve artificial parameters or thresholds. Existing analytical reconstruction algorithms have certain limitations and usually depend on the gradient of the magnetic field, which is not easy to measure accurately in many applications. This paper discusses a new semi-analytical solution and the related reconstruction algorithm. The new method can be used for an arbitrary sensor arrangement. To reduce the measurement uncertainty in practical applications, deep neural network (DNN)-based models are developed to denoise the reconstructed trajectory. Compared to traditional approaches such as wavelet-based filtering, the DNN-based denoisers are more accurate in the position reconstruction. However, they often over-smooth the velocity signal, and a hybrid method that combines the wavelet and DNN model provides a more accurate velocity reconstruction. All the DNN-based and wavelet methods perform well in the orientation reconstruction.