Stefano Pagani

We aim to develop a patient-specific computational model to predict the risk of Ventricular Tachycardia (VT) in patients with a Biventricular Cardiac Resynchronization Therapy (BiV-CRT) device. Patients are indeed at risk of developing arrhythmias due to BiV-CRT pacing, a known potential complication that requires preventive evaluation. We consider three non-ischemic fibrotic patients. Patient-specific left ventricle geometries and fibrosis are extracted from Cine-MRI and LGE-MRI. The electrophysiology model, based on the monodomain equation and the Ten Tusscher–Panfilov (TTP06) ionic model, is personalized using pre-operative Electro-Anatomical Mapping System data. The TTP06 parameters are adapted to reflect the altered electrical properties of the fibrotic tissue. To test inducibility, we use an S1–S2 stimulation protocol: S1 simulates the clinical BiV-CRT pacing with a patient-specific VV-delay, followed by an S2 ectopic impulse. This procedure is repeated for 10 ectopic sites. The arrhythmogenic risk is quantified by the number of ectopic sites that generate a reentry loop. The model's prediction of VT risk in our simulations matches the clinical outcome for all three patients, recognizing the two with spontaneous VT episodes and the one non-arrhythmic patient. Arrhythmic patients show a higher number of ectopic sites from which a reentry loop is generated compared to the non-arrhythmic patient. This study provides a preliminary attempt toward the use of computational tools to assess the vulnerability of the arrhythmic substrate during BiV-CRT pacing in non-ischemic patients. In the future, such tools could serve as a noninvasive diagnostic metric to inform clinicians about possible therapies to be associated with BiV-CRT.

In the current clinical practice, the diagnosis of spinal disorders and their surgical planning are critically based on imaging data. To complement this data, patient-specific finite element models have been developed and showed to be powerful tools for evaluating spine mechanics. Most of them rely on Computational Tomography (CT) scans - which have a high resolution but are seldom available in routine clinical practice - while only a recent few models are on less invasive Magnetic Resonance Imaging (MRI). Yet, despite the proliferation of these computational models, encompassing detailed anatomical and functional information, the rheological assumptions they are built upon are based on tissue-sample mechanical response data, which leaves a gap in the quantitative analysis on how such assumptions influence the macroscopic response of a functional spinal unit. Aiming at addressing these shortcomings, the main purpose of this work is to introduce a quantitative computational assessment of the macroscopic impact of commonly adopted rheological models - from linear elasticity to fiber-reinforced nonlinear hyperelasticity - in several loading conditions, focusing on a lumbar unit which is considered as a typical benchmark system. We also propose a reconstruction procedure to accurately describe subject-specific anatomy from MRI data, including the intervertebral disc and its nucleus pulposus. Bones are modeled as linear elastic media, whereas for the AF, we consider three different mechanical models - namely, isotropic linear elasticity and the Holzapfel-Gasser-Ogden model with and without fiber reinforcement. Model verification on an idealized geometry demonstrates numerical consistency, while parametric orthostatic simulations highlight the need for nonlinear formulations to capture anisotropy and strain-stiffening behavior of the intervertebral disc. Then, we carry out flexion, lateral bending, and torsion tests on a subject-specific reconstructed functional unit, for which we provide parametric analysis in terms of momentum magnitude and resulting range of motion. These tests further confirm the need for a nonlinear rheology of the annulus fibrosus and provide a quantitative assessment of the differences between the constitutive laws considered. Moreover, successful comparisons with the literature, in terms of macroscopic deformation under several loading conditions, serve as partial validation for our computational model.

We investigate non-overlapping Schwarz preconditioners for the algebraic systems stemming from high-order discretizations of the coupled monodomain and Barreto-Cressman models, with applications to brain electrophysiology. The spatial discretization is based on a high-order Polytopal Discontinuous Galerkin (PolyDG) method, coupled with the Crank-Nicolson time discretization scheme with explicit extrapolation of the ion term. To improve solver efficiency, we consider additive Schwarz preconditioners within the PolyDG framework, which combines (massively parallel) local subdomain solvers with a coarse-grid correction. Numerical experiments demonstrate robustness with respect to the discretization parameters, as well as a significant reduction in iteration counts compared to the unpreconditioned solver. These features make the proposed approach well-suited for parallel large-scale simulations in brain electrophysiology.

A bstract: Aims: We aim to develop a patient-specific computational model to predict the risk of Ventricular Tachycardia (VT) in patients with Biventricular Cardiac Resynchronization Therapy (BiV-CRT) device. Patients are indeed at risk of developing arrhythmias due to BiV-CRT pacing, a known potential complication that puts the cardiologist on guard against its prevention. Materials and Methods: We consider three non-ischemic fibrotic patients. Patient-specific left ventricle geometries and fibrosis regions are extracted from Cine-MRI and LGE-MRI. The electrophysiology model, based on the monodomain equation and on the Ten Tusscher-Panfilov (TTP06) ionic current model, is personalized using pre-operative Electro-Anatomical Mappings System data. The TTP06 parameters are adapted to reflect the altered electrical properties of the fibrotic tissue. To test inducibility, we use an 𝕊1−𝕊2 stimulation protocol: 𝕊1 simulates the clinical BiV-CRT pacing with patient-specific VV-delay, followed by a 𝕊2 ectopic impulse. This procedure is repeated for ten ectopic sites. The arrhytmogenic risk is quantified by the number of ectopic sites that successfully generates a reentry loop. Conclusion: The model’s prediction of VT risk is consistent with the long-term clinical follow-up for all the patients. Arrhythmic patients show a higher number of ectopic sites from which a reentry loop is generated compared to the non-arrhythmic patient. This study provides a first, preliminary attempt towards the use of computational tools in assessing the vulnerability of the arrhythmic substrate during BiV-CRT pacing in non-ischemic patients. In future, such tools could serve as a powerful non-invasive diagnostic metric to inform clinicians about possible therapies to associate to BiV-CRT.

Alzheimer’s disease shows significantly variable progressions between patients, making early diagnosis, disease monitoring, and care planning difficult. Existing data-driven Disease Progression Models try to tackle this issue, but they usually require sufficiently large datasets of specific diagnostic modalities, which are rarely available in clinical practice. Here, we introduce a new modeling framework capable of predicting individual disease trajectories from sparse, irregularly sampled, multi-modal clinical data. Our method uses (recurrent) Neural Ordinary Differential Equations to determine the current hidden state of a patient from sparse past exams and to forecast future disease progression, illustrating how biomarkers evolve over time. When applied to the ADNI clinical cohort, the model detected early signs of disease more accurately than common data-driven alternatives and effectively tracked changes in biomarker trajectories that align with established clinical knowledge. This provides a versatile tool for accurate diagnosis and monitoring of neurodegenerative diseases.

Active stress models in cardiac biomechanics account for the mechanical deformation caused by muscle activity, thus providing a link between the electrophysiological and mechanical properties of the tissue. The accurate assessment of active stress parameters is fundamental for a precise understanding of myocardial function but remains difficult to achieve in a clinical setting, especially when only displacement and strain data from medical imaging modalities are available. This work investigates, through an in-silico study, the application of physics-informed neural networks (PINNs) for inferring active contractility parameters in time-dependent cardiac biomechanical models from these types of imaging data. In particular, by parametrising the sought state and parameter field with two neural networks, respectively, and formulating an energy minimisation problem to search for the optimal network parameters, we are able to reconstruct in various settings active stress fields in the presence of noise and with a high spatial resolution. To this end, we also advance the vanilla PINN learning algorithm with the use of adaptive weighting schemes, ad-hoc regularisation strategies, Fourier features, and suitable network architectures. In addition, we thoroughly analyse the influence of the loss weights in the reconstruction of active stress parameters. Finally, we apply the method to the characterisation of tissue inhomogeneities and detection of fibrotic scars in myocardial tissue. This approach opens a new pathway to significantly improve the diagnosis, treatment planning, and management of heart conditions associated with cardiac fibrosis.