An updated Lagrangian constrained mixture model of pathological cardiac growth and remodelling

Biomechanics of soft biological tissues and organs, mechanobiology, homeostasis and modelling

The human body consists of many different soft biological tissues that exhibit diverse microstructures and functions and experience diverse loading conditions. Yet, under many conditions, the mechanical behaviour of these tissues can be described well with similar nonlinearly elastic or inelastic constitutive relations, both in health and some diseases. Such constitutive relations are essential for performing nonlinear stress analyses, which in turn are critical for understanding physiology, pathophysiology and even clinical interventions, including surgery. Indeed, most cells within load-bearing soft tissues are highly sensitive to their local mechanical environment, which can typically be quantified using methods of continuum mechanics only after the constitutive relations are determined from appropriate data, often multi-axial. In this review, we discuss some of the many experimental findings of the structure and the mechanical response, as well as constitutive formulations for 10 representative soft tissues or organs, and present basic concepts of mechanobiology to support continuum biomechanical studies. We conclude by encouraging similar research along these lines, but also the need for models that can describe and predict evolving tissue properties under many conditions, ranging from normal development to disease progression and wound healing. An important foundation for biomechanics and mechanobiology now exists and methods for collecting detailed multi-scale data continue to progress. There is, thus, considerable opportunity for continued advancement of mechanobiology and biomechanics.

Advanced statistical inference of myocardial stiffness: A time series Gaussian process approach of emulating cardiac mechanics for real-time clinical decision support

Cardiac mechanics modelling promises to revolutionize personalized health care; however, inferring patient-specific biophysical parameters, which are critical for understanding myocardial functions and performance, poses substantial methodological challenges. Our work is primarily motivated to determine the passive stiffness of the myocardium from the measurement of the left ventricle (LV) volume at various time points, which is crucial for diagnosing cardiac physiological conditions. Although there have been significant advancements in cardiac mechanics modelling, the tasks of inference and uncertainty quantification of myocardial stiffness remain challenging, with high computational costs preventing real-time decision support. We adapt Gaussian processes to construct a statistical surrogate model for emulating LV cavity volume during diastolic filling to overcome this challenge. As the LV volumes, obtained at different time points in diastole, constitute a time series, we apply the Kronecker product trick to decompose the complex covariance matrix of the whole system into two separate covariance matrices, one for time and the other for biophysical parameters. To proceed towards personalized health care, we further integrate patient-specific LV geometries into the Gaussian process emulator using principal component analysis (PCA). Utilizing a deep learning neural network for extracting time-series left ventricle volumes from magnetic resonance images, Bayesian inference is applied to determine the posterior probability distribution of critical cardiac mechanics parameters. Tests on real-patient data illustrate the potential for real-time estimation of myocardial properties for clinical decision-making. These advancements constitute a crucial step towards clinical impact, offering valuable insights into posterior uncertainty quantification for complex cardiac mechanics models.

Modeling biological growth of human keratoconus: On the effect of tissue degradation, location and size

Keratoconus is a non-inflammatory bilateral disease, that usually occurs in the inferior-temporal region, where the cornea bulges out and becomes thinner, due to the gradual loss of structural organization in corneal tissue. Degenerated extracellular matrix and fibers breakage have been observed in keratoconic corneas, that may promote the progression of the pathology. While keratoconus histopathology has been widely described in literature, its etiology is still not clear. Being able to fully understand keratoconus growing process could be crucial to detect its development and improve prevention strategies. This work proposes a novel continuum-based keratoconus growth model. The proposed framework accounts for the structural changes occurring in the underlying tissue during the progression of the disease, as indicated in experiments. The developed formulation is able to replicate the typical bulging and thinning of keratoconic corneas, as well as different forms in terms of shape, as they are commonly classified in clinics (nipple, oval and globus cones). The cone that is obtained constitutes a permanent deformed state, not pressure dependent. The resulting model may help to better understand the etiology of the behavior of this disease with the aim of improving the diagnosis and the treatment of the pathology.

Using LDDMM and a kinematic cardiac growth model to quantify growth and remodelling in rat hearts under PAH

Pulmonary arterial hypertension (PAH) is a rapidly progressive and fatal disease, with right ventricular failure being the primary cause of death in patients with PAH. This study aims to determine the mechanical stimuli that may initiate heart growth and remodelling (G&R). To achieve this, two bi-ventricular models were constructed: one for a control rat heart and another for a rat heart with PAH. The growth of the diseased heart was estimated by warping it to the control heart using an improved large deformation diffeomorphic metric mapping (LDDMM) framework. Correlation analysis was then performed between mechanical cues (stress and strain) and growth tensors, which revealed that principal strains may serve as a triggering stimulus for myocardial growth and remodelling under PAH. The growth tensors, estimated from in vivo images, could explain 84.3% of the observed geometrical changes in the diseased heart with PAH by using a kinematic cardiac growth model. Our approach has the potential to quantify G&R using sparse in vivo images and to provide insights into the underlying mechanism of triggering right heart failure from a biomechanical perspective.

The role of aorta distal to stent in the occurrence of distal stent graft-induced new entry tear: A computational fluid dynamics and morphological study

Distal stent graft-induced new entry tear (dSINE) is an important complication of thoracic endovascular aortic repair (TEVAR) for the treatment of type B aortic dissection (TBAD). This study aims to explore whether the aorta distal to the stent plays an important role in the occurrence of dSINE. Sixty-nine patient-specific geometrical models of twenty-three enrolled patients were reconstructed from preoperative, postoperative, and predSINE computed tomography scans. Computational fluid dynamics (CFD) simulations were performed to calculate the von Mises stress in the CFD group. Meanwhile, morphological measurements were performed in all patients, including measurements of the inverted pyramid index at different follow-up time points and the postoperative true lumen volume change rate. In the CFD study, the time-averaged von Mises stress of the true lumen distal to the stent in dSINE patients was significantly higher than that in the CFD controls (20.42 kPa vs. 15.47 kPa). In the morphological study, a special aortic plane (plane A) with an extremely small area distal to the stent was observed in dSINE patients, which resulted in an inverted pyramid structure in the true lumen distal to the stent. This structure in dSINE patients became increasingly obvious during the follow-up period and finally reached the maximum value before dSINE occurred (mean, 3.91 vs. 1.23). At the same time, enlargement of the true lumen distal to the stent occurs before dSINE, manifesting as a continuous increase in the true lumen volume (mean, 0.70 vs. 013). A new theory of what causes dSINE to occur has been proposed: the inverted pyramid structure of the true lumen distal to the stent caused an increase in the von Mises stress in this region and aortic enlargement, which ultimately led to the occurrence of dSINE.