David Nordsletten

Use of universal ventricular coordinates to integrate information from cine and late gadolinium enhancement cardiac MRI enabled generation of high-resolution fibrosis maps and revealed distinct spatial patterns across disease severity in desmoplakin cardiomyopathy.

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.

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.

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.

Determining the unloaded, or reference, configuration of the heart is essential for developing patient-specific models that can accurately estimate in-vivo strains and stresses. Various strategies have been proposed to obtain this configuration, with inverse mechanics being a practical approach. The inverse mechanics method estimates the unloaded geometry from a deformed state and its known loads without the need for optimization procedures. However, for the resulting unloaded geometry to be physiologically meaningful, accurate boundary conditions are crucial. While spring boundary conditions are commonly applied to the epicardium to model interactions with surroundings, they do not account for the localized forces exerted by structures like the ribs and diaphragm. Since these external forces cannot be directly measured from medical images, we propose a novel approach that integrates them into the inverse mechanics formulation by penalizing large deformations. Using a series of test problems, we show this approach produces a reference configuration that closely matches the ground truth and improves circumferential strain estimations by an order of magnitude compared to standard inverse mechanics methods.

This study investigates the passive viscoelastic mechanical properties of cardiac muscle by introducing a theoretical model that explains the power-law kinetics of passive stress decay. The model accounts for two parallel processes contributing to passive mechanics: an elastic component and a viscoelastic component designed to simulate stress/strain-mediated unfolding of serial domains in the titin molecule. Under stress, serial globular domains within the elastic region of the titin molecule reversibly unfold. This unfolding phenomenon contributes to both hysteresis (a lag in stress between loading and unloading) and preconditioning effects in simulated striated muscle mechanics. Moreover, experimental evidence indicates that stress relaxation in cardiac muscle follows a power law, and that the muscle’s nonlinear stress-strain relationship and hysteresis behavior are calcium-dependent. To analyze these mechanical phenomena, we simulate the apparent viscous element as a mesoscopic-scale ensemble of chains, each composed of serial globular domains that unfold in a stress-dependent manner. Although the model was developed to represent the behavior of titin, it equivalently represents any contributing process involving a linked series of domains that undergo stress-mediated unfolding. By providing a unified basis for the observed viscoelastic and preconditioning effects, calcium dependency, and power-law stress relaxation phenomena, this study offers a novel theoretical basis for understanding and simulating the role of titin in striated muscle mechanics. 1 Key points: Passive stress relaxation of cardiac muscle follows a power-law decay, a phenomenon that is explained using a theoretical model of dynamic unfolding of globular domains along polymer chain. The theoretical model simulates the behavior of titin, a giant sarcomere protein linking myosin thick filaments to the Z disk and providing passive restoring force during muscle stretch. The theoretical model is able to account the observed effects of calcium on the effective viscoelastic passive mechanics of cardiac muscle. This model provides a theoretical basis for understanding passive visocelastic properties and titin’s role in striated muscle mechanics.