Study of the Influence of Boundary Conditions on Corneal Deformation Based on the Finite Element Method of a Corneal Biomechanics Model

Implementing in silico corneal biomechanical models for surgery applications can be boosted by developing patient-specific finite element models adapted to clinical requirements and optimized to reduce computational times. This research proposes a novel corneal multizone-based finite element model with octants and circumferential zones of clinical interest for material definition. The proposed model was applied to four patient-specific physiological geometries of keratoconus-affected corneas. Free-stress geometries were calculated by two iterative methods, the displacements and prestress methods, and the influence of two boundary conditions: embedded and pivoting. The results showed that the displacements, stress and strain fields differed for the stress-free geometry but were similar and strongly depended on the boundary conditions for the estimated physiological geometry when considering both iterative methods. The comparison between the embedded and pivoting boundary conditions showed bigger differences in the posterior limbus zone, which remained closer in the central zone. The computational calculation times for the stress-free geometries were evaluated. The results revealed that the computational time was prolonged with disease severity, and the displacements method was faster in all the analyzed cases. Computational times can be reduced with multicore parallel calculation, which offers the possibility of applying patient-specific finite element models in clinical applications.

Computational Re-Entry Vulnerability Index Mapping to Guide Ablation in Patients With Post-Myocardial Infarction Ventricular Tachycardia

BACKGROUND

Ventricular tachycardias (VTs) in patients with myocardial infarction (MI) are often treated with catheter ablation. However, the VT induction during this procedure does not always identify all of the relevant activation pathways or may not be possible or tolerated. The re-entry vulnerability index (RVI) quantifies regional activation-repolarization differences and can detect multiple regions susceptible to re-entry without the need to induce the arrhythmia.

OBJECTIVES

This study aimed to further develop and validate the RVI mapping in patient-specific computational models of post-MI VTs.

METHODS

Cardiac magnetic resonance imaging data from 4 patients with post-MI VTs were used to induce VTs in a computational electrophysiological model by pacing. The RVI map of a premature beat in each patient model was used to guide virtual ablations. We compared our results with those of clinical ablation in the same patients.

RESULTS

Single-site virtual RVI-guided ablation prevented VT induction in 3 of 9 cases. Multisite virtual ablations guided by RVI mapping successfully prevented re-entry in all cases (9 of 9). Overall, virtual ablation required 15-fold fewer ablation sites (235.5 ± 97.4 vs 17 ± 6.8) and 2-fold less ablation volume (5.34 ± 1.79 mL vs 2.11 ± 0.65 mL) than the clinical ablation.

CONCLUSIONS

RVI mapping allows localization of multiple regions susceptible to re-entry and may help guide VT ablation. RVI mapping does not require the induction of arrhythmia and may result in less ablated myocardial volumes with fewer ablation sites.

Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications

Inverse modeling approaches in cardiovascular medicine are a collection of methodologies that can provide non-invasive patient-specific estimations of tissue properties, mechanical loads, and other mechanics-based risk factors using medical imaging as inputs. Its incorporation into clinical practice has the potential to improve diagnosis and treatment planning with low associated risks and costs. These methods have become available for medical applications mainly due to the continuing development of image-based kinematic techniques, the maturity of the associated theories describing cardiovascular function, and recent progress in computer science, modeling, and simulation engineering. Inverse method applications are multidisciplinary, requiring tailored solutions to the available clinical data, pathology of interest, and available computational resources. Herein, we review biomechanical modeling and simulation principles, methods of solving inverse problems, and techniques for image-based kinematic analysis. In the final section, the major advances in inverse modeling of human cardiovascular mechanics since its early development in the early 2000s are reviewed with emphasis on method-specific descriptions, results, and conclusions. We draw selected studies on healthy and diseased hearts, aortas, and pulmonary arteries achieved through the incorporation of tissue mechanics, hemodynamics, and fluid-structure interaction methods paired with patient-specific data acquired with medical imaging in inverse modeling approaches.

Patient-Specific iPSCs-Based Models of Neurodegenerative Diseases: Focus on Aberrant Calcium Signaling

The development of cell reprogramming technologies became a breakthrough in the creation of new models of human diseases, including neurodegenerative pathologies. The iPSCs-based models allow for the studying of both hereditary and sporadic cases of pathologies and produce deep insight into the molecular mechanisms underlying neurodegeneration. The use of the cells most vulnerable to a particular pathology makes it possible to identify specific pathological mechanisms and greatly facilitates the task of selecting the most effective drugs. To date, a large number of studies on patient-specific models of neurodegenerative diseases has been accumulated. In this review, we focused on the alterations of such a ubiquitous and important intracellular regulatory pathway as calcium signaling. Here, we reviewed and analyzed the data obtained from iPSCs-based models of different neurodegenerative disorders that demonstrated aberrant calcium signaling.

The Role of Patient-Specific Morphological Features of the Left Atrial Appendage on the Thromboembolic Risk Under Atrial Fibrillation

Background

A large majority of thrombi causing ischemic complications under atrial fibrillation (AF) originate in the left atrial appendage (LAA), an anatomical structure departing from the left atrium, characterized by a large morphological variability between individuals. This work analyses the hemodynamics simulated for different patient-specific models of LAA by means of computational fluid-structure interaction studies, modeling the effect of the changes in contractility and shape resulting from AF.

Methods

Three operating conditions were analyzed: sinus rhythm, acute atrial fibrillation, and chronic atrial fibrillation. These were simulated on four patient-specific LAA morphologies, each associated with one of the main morphological variants identified from the common classification: chicken wing, cactus, windsock, and cauliflower. Active contractility of the wall muscle was calibrated on the basis of clinical evaluations of the filling and emptying volumes, and boundary conditions were imposed on the fluid to replicate physiological and pathological atrial pressures, typical of the various operating conditions.

Results

The LAA volume and shear strain rates were analyzed over time and space for the different models. Globally, under AF conditions, all models were well aligned in terms of shear strain rate values and predicted levels of risk. Regions of low shear rate, typically associated with a higher risk of a clot, appeared to be promoted by sudden bends and focused at the trabecule and the lobes. These become substantially more pronounced and extended with AF, especially under acute conditions.

Conclusion

This work clarifies the role of active and passive contraction on the healthy hemodynamics in the LAA, analyzing the hemodynamic effect of AF that promotes clot formation. The study indicates that local LAA topological features are more directly associated with a thromboembolic risk than the global shape of the appendage, suggesting that more effective classification criteria should be identified.

Patient-Specific Modeling for Structural Heart Intervention: Role of 3D Printing Today and Tomorrow CME

Structural heart interventions (SHIs) are increasingly applicable in a wide range of heart defects, but the intricate and dynamic nature of cardiac structures can make SHIs challenging to perform. Three-dimensional (3D) printed modeling integrates advanced clinical imaging and 3D printing technology to replicate patient-specific anatomy for comprehensive planning and simulation of SHIs. This review discusses the basic principles of patient-specific 3D print model development, print material selection, and model fabrication and highlights how cardiovascular 3D printing can be used in preprocedural planning, device sizing, enhanced communication, and procedure simulation.

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest that our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

Population-specific modelling of between/within-subject flow variability in the carotid arteries of the elderly

Computational fluid dynamics models are increasingly proposed for assisting the diagnosis and management of vascular diseases. Ideally, patient-specific flow measurements are used to impose flow boundary conditions. When patient-specific flow measurements are unavailable, mean values of flow measurements across small cohorts are used as normative values. In reality, both the between-subjects and within-subject flow variabilities are large. Consequently, neither one-shot flow measurements nor mean values across a cohort are truly indicative of the flow regime in a given person. We develop models for both the between-subjects and within-subject variability of internal carotid flow. A log-linear mixed effects model is combined with a Gaussian process to model the between-subjects flow variability, while a lumped parameter model of cerebral autoregulation is used to model the within-subject flow variability in response to heart rate and blood pressure changes. The model parameters are identified from carotid ultrasound measurements in a cohort of 103 elderly volunteers. We use the models to study intracranial aneurysm flow in 54 subjects under rest and exercise and conclude that OSI, a common wall shear-stress derived quantity in vascular CFD studies, may be too sensitive to flow fluctuations to be a reliable biomarker.

Patient-specific parameter estimates of glioblastoma multiforme growth dynamics from a model with explicit birth and death rates

Glioblastoma multiforme (GBM) is an aggressive primary brain cancer with a grim prog-nosis. Its morphology is heterogeneous, but prototypically consists of an inner, largely necrotic core surrounded by an outer, contrast-enhancing rim, and often extensive tumor-associated edema beyond. This structure is usually demonstrated by magnetic resonance imaging (MRI). To help relate the three highly idealized components of GBMs (i.e., necrotic core, enhancing rim, and maximum edema ex-tent) to the underlying growth "laws," a mathematical model of GBM growth with explicit motility, birth, and death processes is proposed. This model generates a traveling-wave solution that mimics tumor progression. We develop several novel methods to approximate key characteristics of the wave profile, which can be compared with MRI data. Several simplified forms of growth and death terms and their parameter identifiability are studied. We use several test cases of MRI data of GBM patients to yield personalized parameterizations of the model, and the biological and clinical implications are discussed.

Three-dimensional printing CT-derived objects with controllable radiopacity

Purpose

The goal of this work was to develop phantoms for the optimization of pre‐operative computed tomography (CT) scans of the prostate artery, which are used for embolization planning.

Methods

Acrylonitrile butadiene styrene (ABS) pellets were doped with barium sulfate and extruded into filaments suitable for 3D printing on a fused deposition modeling (FDM) printer. Cylinder phantoms were created to evaluate radiopacity as a function of doping percentage. Small‐diameter tree phantoms were created to assess their composition and dimensional accuracy. A half‐pelvis phantom was created using clinical CT images, to assess the printer's control over cortical bone thickness and cancellous bone attenuation. CT‐derived prostate artery phantoms were created to simulate complex, contrast‐filled arteries.

Results

A linear relationship (R = 0.998) was observed between barium sulfate added (0%–10% by weight), and radiopacity (−31 to 1454 Hounsfield Units [HU]). Micro‐CT scans showed even distribution of the particles, with air pockets comprising 0.36% by volume. The small vessels were found to be oversized by a consistent amount of 0.08 mm. Micro‐CT scans revealed that the phantoms' interiors were completely filled in. The maximum HU values of cortical bone in the phantom were lower than that of the filament, a result of CT image reconstruction. Creation of cancellous bone regions with lower HU values, using the printer's infill parameter, was successful. Direct volume renderings of the pelvis and prostate artery were similar to the clinical CT, with the exception that the surfaces of the phantom objects were not as smooth.

Conclusions

It is possible to reliably create FDM 3D printer filaments with predictable radiopacity in a wide range of attenuation values, which can be used to print dimensionally accurate radiopaque objects derived from CT data. Phantoms of this type can be quickly and inexpensively developed to assess and optimize CT protocols for specific clinical applications.

Making three-dimensional echocardiography more tangible: a workflow for three-dimensional printing with echocardiographic data

Three-dimensional (3D) printing is a rapidly evolving technology with several potential applications in the diagnosis and management of cardiac disease. Recently, 3D printing (i.e. rapid prototyping) derived from 3D transesophageal echocardiography (TEE) has become possible. Due to the multiple steps involved and the specific equipment required for each step, it might be difficult to start implementing echocardiography-derived 3D printing in a clinical setting. In this review, we provide an overview of this process, including its logistics and organization of tools and materials, 3D TEE image acquisition strategies, data export, format conversion, segmentation, and printing. Generation of patient-specific models of cardiac anatomy from echocardiographic data is a feasible, practical application of 3D printing technology.

Pilot study on the short-term prediction of symptoms in children with hay fever monitored with e-Health technology

Forecasting symptoms of pollen-related allergic rhinoconjunctivitis at the level of individual patients would be useful to improve disease control and plan pharmacological intervention. Information Technology nowadays facilitates a more efficient and easier monitoring of patients with chronic diseases. We aimed this study at testing the efficiency of a model to short-term forecast symptoms of pollen-AR at the "individual" patient level. We analysed the data prospectively acquired from a group of 21 Italian children affected by pollen-related allergic rhinoconjunctivitis and recorded their symptoms and medication "Average Combined Score" (ACS) on a daily basis during April-June 2010-2011 through an informatics platform (Allergymonitor™). The dataset used for prediction included 15 variables in four categories: (A) date, (B) meteo-climatic, (C) atmospheric concentration of 5 pollen taxa, and (D) intensity of the patient's IgE sensitization. A Partial Least Squares Discriminant Analysis approach was used in order to predict ACS values above a fixed threshold value (0.5). The best performing predicting model correctly classified 77.8% ± 10.3% and 75.5% ± 13.2% of the recorded days in the model and test years, respectively. In this model, 9/21 patients showed ≥ 80% correct classification of the recorded days in both years. A better performance was associated with a higher degree of patient's atopic sensitization and a time lag > 1. Symptom forecasts of seasonal allergic rhinitis are possible in highly polysensitised patients in areas with complex pollen exposure. However, only predictive models tailored to the individual patient's allergic susceptibility are accurate enough. Multicenter studies in large population samples adopting the same acquisition data system on smart phones are now needed to confirm this encouraging outcome.