Mia Bonini

We present a fully coupled, patient-specific 3D–0D computational framework for hearts supported with left ventricular assist devices (LVAD) that enables controlled in silico experimentation. The approach monolithically integrates three-dimensional CFD of the left ventricle (LV), left atrium (LA), aortic root, and LVAD cannulae with a closed-loop 0D lumped parameter network of the full circulation. Mitral and aortic valve dynamics are governed by transvalvular pressure and flow with patient-specific regurgitant orifice areas, and the LVAD is represented via a pressure–flow (H–Q) relation. This manuscript provides the complete mathematical formulation, coupling strategy, and parameterization required to build a reproducible pipeline from dynamic CT, 2D transthoracic echocardiography, and right heart catheterization. This methodology is demonstrated in a patient under long-term support of LVAD and concomitant mitral and aortic regurgitation. The personalized, fully coupled 3D–0D models reproduced available clinical targets with a mean error of 8.6%, enabling controlled in silico interrogation of valve repair strategies. In the patient-specific state, simulated mitral and aortic regurgitant volumes were 6.6 and 6.5 mL per cycle, yielding a forward cardiac output of 3.16 L/min despite an LVAD flow of 3.7 L/min. In silico isolated mitral valve (MV) repair, isolated aortic valve (AV) repair, and combined MV+AV repair increased forward output to 3.41, 3.33, and 3.55 L/min, respectively; however, aortic valve opening and increased aortic pressure pulsatility (up to 38.9 vs. 13.5 mmHg) were observed only when MV repair was involved. These left-sided improvements propagated through the cardiopulmonary circulation, reducing pulmonary pressures and right ventricular loading, with the largest benefit observed following combined repair. We show that the modeling platform presented provides a powerful means to study mechanical circulatory support, enabling patient-specific evaluation of surgical interventions in patients with LVAD and delivering quantitative insight into clinically important metrics—such as aortic pulsatility, RV afterload, and chamber-level flow patterns.

4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive technique for volumetric, time-resolved blood flow quantification. However, apparent trade-offs between acquisition time, image noise, and resolution limit clinical applicability. In particular, in regions of highly transient flow, coarse temporal resolution can hinder accurate capture of physiologically relevant flow variations. Deep learning-based post-processing techniques have shown promise in overcoming these issues using so-called super-resolution networks. However, while existing super-resolution research has primarily focused on spatial upsampling, temporal super-resolution remains largely unexplored. The aim of this study was therefore to implement and evaluate a residual data-driven network for temporal super-resolution 4D Flow MRI. To achieve this, an existing spatial network (4DFlowNet) was re-designed for temporal upsampling, adapting input dimensions, and optimizing internal layer structures. The model was trained and tested on synthetic 4D Flow MRI data derived from patient-specific in-silico models, followed by additional evaluation on clinically acquired in-vivo datasets. Overall, excellent performance was achieved with input velocities effectively denoised and temporally upsampled, with a mean absolute error (MAE) of 1.0 cm/s in an unseen in-silico setting, outperforming deterministic alternatives (linear interpolation MAE = 2.3 cm/s, sinc interpolation MAE = 2.6 cm/s). Further, the network synthesized high-resolution temporal information from unseen low-resolution in-vivo data, with strong correlation observed at peak flow frames. As such, our results highlight the potential of utilizing data-driven neural networks for temporal super-resolution 4D Flow MRI, enabling high-frame-rate flow quantification without extending acquisition times beyond clinically acceptable limits.

Introduction: Mitral regurgitation (MR) is a common valvular disease associated with complications such as pulmonary hypertension, atrial fibrillation, and heart failure. However, its full impact on the cardiovascular system, especially on right heart function, is not yet fully understood. Understanding this relationship is important because the right ventricle (RV) is critical for maintaining cardiovascular function. Dysfunction of the RV, which may be contributed by conditions like MR, is strongly associated with poor clinical outcomes. Despite its importance, comprehensively studying MR's effect on the RV has been challenging due to the complex, interdependent nature of cardiovascular dynamics, limited patient data, and the difficulty in synthesizing disparate information to clarify the left heart-right heart connection. Methods: The primary goal of this study is to investigate the effects of MR on cardiovascular hemodynamics and RV function by integrating 3D models of the left heart with a closed-loop 0D models of the entire cardiovascular system. We further conduct detailed analyses using patient-specific models to explore how various system modifications impact the RV, providing insights into the nuanced effects of MR on the right heart. Results and Discussion: This analysis provides several clinically relevant insights. First, progressive MR markedly increases RV afterload and predisposes the RV to dysfunction, even when intrinsic RV contractility is preserved or enhanced. Second, MR-specific severity indices and left-heart metrics alone fail to capture the true burden on the right heart; RV impairment can progress despite stable or only modestly changing MR descriptors. Finally, these findings highlight the need to incorporate direct assessment of RV structure and function into the evaluation of MR, as RV vulnerability plays a critical role in determining patient risk and guiding management decisions.

Magnetic resonance imaging (MRI) can estimate three-dimensional (3D) time-resolved blood velocity fields using 4D-flow MRI, providing rich relative pressure field information. Clinical alternatives-catheterization and Doppler echocardiography-provide one-dimensional pressure drops. While the accuracy of one-dimensional pressure drops from 4D-flow has been explored, the accuracy of 3D relative pressure field estimates requires further evaluation. This work analyzes three state-of-the-art relative pressure estimators: virtualWork-Energy Relative Pressure (vWERP), the Pressure Poisson Estimator (PPE), and the Stokes Estimator (STE). Spatiotemporal characteristics and noise sensitivity were determined in silico via comparison with CFD-derived relative pressure fields. Estimators were then validated using a type B aortic dissection (TBAD) flow phantom with varying tear geometry and twelve catheter pressure measurements. Finally, each estimator was evaluated across eight patient cases in a clinical feasibility analysis. Estimators were cross-compared and evaluated for coherence since no reference catheter pressure measurements were available. PPE outperformed STE in the in silico relative pressure analysis, while both outperformed vWERP in plane-to-plane assessment. High velocity gradients and low spatial resolution contributed most significantly to local variations in 3D relative pressure field errors. Low temporal resolution led to systematic underestimation of highly transient peak pressure events. In the flow phantom analysis, vWERP was the most accurate method, followed by STE and then PPE. Each relative pressure estimator was strongly correlated with ground truth relative pressure values, despite the tendency to underestimate peak relative pressure. Patient case results demonstrated that each relative pressure estimator could be feasibly integrated into a clinical workflow.

We present a high-fidelity, patient-specific computational framework for simulating cardiovascular hemodynamics in left ventricular assist device (LVAD) patients, with the ability to evaluate the impact of mitral and aortic valve repair. The method couples three-dimensional computational fluid dynamics (CFD) with a zero-dimensional lumped-parameter network representing the full cardiovascular system. Valve dynamics are governed by pressure–flow physics with patient-specific regurgitant areas, enabling the investigation of valve insufficiency and surgical repair. The model incorporates dynamic contrast-enhanced CT imaging, echocardiography, and catheter-based measurements. We demonstrate the utility of this framework using a clinical case involving a patient on long-term LVAD support with mitral regurgitation and aortic insufficiency. The coupled model resolves detailed pressure and velocity fields in the left heart, LVAD cannulae, and aortic root, while maintaining system-wide physiological consistency. Importantly, the framework also quantifies right ventricular loading and function, allowing assessment of how left-sided valve interventions propagate through the cardiopulmonary circulation. Using this pipeline, we simulate mitral and aortic valve repair by modulating valve closure mechanics and regurgitant area, revealing changes in both local and global hemodynamics. This modeling platform provides a powerful means to study the interplay between mechanical circulatory support, native valve physiology, and right heart function. It enables rigorous, patient-specific evaluation of surgical interventions in LVAD patients and delivers quantitative insight into clinically important metrics—such as aortic pulsatility, RV afterload, and chamber-level flow patterns—that are challenging to capture with current imaging modalities.

Myocardial infarction significantly degrades heart function, and current treatments can bring forth serious cost and complications including blood clots and infections. To improve the current state of treatment, researchers are developing tissue patches from induced-pluripotent stem cells that can be incorporated into the heart, improving organ function after a myocardial infarction. These tissue patches include surface patches, attached to the epicardium of the heart, and thick transmural patches that replace the infarcted region. However, little is known about the impact of cardiac tissue patches on pump function in a patient's heart. In addition, it is not clear what patch structural properties - such as active stress generation, muscle fiber alignment, or material stiffness - may best augment existing heart tissue. Computational modeling can be used to examine different implementations and patch properties, illuminating the mechanical impact of cardiac tissue patches in the beating heart. In this work, we computationally implement different cardiac tissue patches to understand benefits of particular patch types and properties. We find that in transmural cardiac tissue patches, both activation and fiber alignment improve function. A transmural patch generating 10% of healthy active stress can increase stroke volume by 18%, and higher generated active stress in a circumferential muscle fiber orientation can recover stroke volume by over 50%. Furthermore, we find that surface cardiac tissue patches can enhance heart function slightly despite limiting diastolic filling, especially when fibrotic thinning has occurred. These conclusions identify broad design goals for the engineering of cardiac tissue patches to improve heart function after a myocardial infarction.

Y-incision aortic annular enlargement (Y-AAE) with surgical aortic valve replacement (SAVR) may improve outflow tract hemodynamics and valve-in-valve (ViV) outcomes but could increase thrombosis risk. We used computational fluid dynamics to analyze post-operative hemodynamics in 15 patient-specific SAVR models, comparing cases with and without Y-AAE. ViV scenarios were simulated by virtually deploying transcatheter aortic valves. Transvalvular peak velocities, pressure gradients, and blood residence time (a proxy for hemostatic risk) were analyzed to assess performance across cases. Y-AAE reduced peak velocity by 39.3% (55% in ViV), transvalvular pressure gradient by 87.2% (92% in ViV), and mean blood residence time by 10.3% (14% in ViV), with no consistent difference in maximum residence time. SAVR with Y-AAE demonstrated improved hemodynamics, even with ViV procedures, and no evidence of increased thrombosis risk. Graphical