The impact of the parameters of the constitutive model on the distribution of strain in the femoral head.

The rapid spread of the finite element method has caused that it has become, among other methods, the standard tool for pre-clinical estimates of bone properties. This paper presents an application of this method for the calculation and prediction of strain and stress fields in the femoral head. The aim of the work is to study the influence of the considered anisotropy and heterogeneity of the modeled bone on the mechanical fields during a typical gait cycle. Three material models were tested with different properties of porous bone carried out in literature: a homogeneous isotropic model, a heterogeneous isotropic model, and a heterogeneous anisotropic model. In three cases studied, the elastic properties of the bone were determined basing on the Zysset-Curnier approach. The tensor of elastic constants defining the local properties of porous bone is correlated with a local porosity and a second order fabric tensor describing the bone microstructure. In the calculations, a model of the femoral head generated from high-resolution tomographic scans was used. Experimental data were drawn from publicly available database "Osteoporotic Virtual Physiological Human Project." To realistically reflect the load on the femoral head, main muscles were considered, and their contraction forces were determined based on inverse kinematics. For this purpose, the results from OpenSim packet were used. The simulations demonstrated that differences between the results predicted by these material models are significant. Only the anisotropic model allowed for the plausible distribution of stresses along the main trabecular groups. The outcomes also showed that the precise evaluation of the mechanical fields is critical in the context of bone tissue remodeling under mechanical stimulations.

Paravalvular leak prediction after transcatheter aortic valve replacement with self-expandable prosthesis based on quantitative aortic calcification analysis.

BACKGROUND: Paravalvular leak (PVL) is one of the most common complications of transcatheter aortic valve replacement (TAVR) and affects short- and long-term outcomes. The aim of this study was to identify the computed tomography (CT) imaging biomarkers that allow PVL after TAVR to be predicted. METHODS: Patients were included who had severe aortic valve stenosis, had undergone TAVR with a self-expanding valve, and had undergone a pre-procedural CT scan. Data on baseline characteristics, procedural and long-term outcomes were collected retrospectively. We used MATLAB software with a self-developed algorithm for CT scan analysis and found parameters that quantified aortic valve calcifications (AVC) in detail. RESULTS: Fifty patients were included. The identified CT-derived parameters included AVC size, volume, thickness and density, as well as calcium radial distribution. The volume of the largest calcium block, calcium perimeter and calcium size (assessed by Feret's diameter) showed a strong association with PVL occurrence after TAVR (P=0.012, P=0.001 and P=0.045, respectively). The prognostic model showed that a 10 mm2 increase in the local AVC amount in each valve section was associated with a 9.8% (95% CI: 2-18%; P=0.019) increase in the risk of PVL occurrence in the corresponding area after TAVR. ROC analysis revealed that the cut-off point of the AVC area was 96.5 mm2 in the polar coordinate system presentation. Kaplan-Meier curves showed worse PVL-free survival in patients with more than 96.5 mm2 of calcium area (P=0.013; log-rank). CONCLUSIONS: Quantitative AVC assessment for PVL prediction may play an important role in screening before TAVR. In future, the use of quantitative AVC assessment as an imaging biomarker in TAVR candidates and the creation and extension of an online database containing quantitative AVC parameters may help to identify high PVL risk patients.

Calcium Pattern Assessment in Patients with Severe Aortic Stenosis Via the Chou's 5-Steps Rule.

BACKGROUND: Progression of aortic valve calcifications (AVC) leads to aortic valve stenosis (AS). Importantly, the AVC degree has a great impact on AS progression, treatment selection and outcomes. Methods of AVC assessment do not provide accurate quantitative evaluation and analysis of calcium distribution and deposition in a repetitive manner. OBJECTIVE: We aim to prepare a reliable tool for detailed AVC pattern analysis with quantitative parameters. METHODS: We analyzed computed tomography (CT) scans of fifty patients with severe AS using a dedicated software based on MATLAB version R2017a (MathWorks, Natick, MA, USA) and ImageJ version 1.51 (NIH, USA) with the BoneJ plugin version 1.4.2 with a self-developed algorithm. RESULTS: We listed unique parameters describing AVC and prepared 3D AVC models with color pointed calcium layer thickness in the stenotic aortic valve. These parameters were derived from CT-images in a semi-automated and repeatable manner. They were divided into morphometric, topological and textural parameters and may yield crucial information about the anatomy of the stenotic aortic valve. CONCLUSION: In our study, we were able to obtain and define quantitative parameters for calcium assessment of the degenerated aortic valves. Whether the defined parameters are able to predict potential long-term outcomes after treatment, requires further investigation.

A novel approach to quantification of aortic valve calcifications in patients undergoing transcatheter aortic valve implantation.

BACKGROUND: Precise calcium evaluation in the aortic complex may be complicated. We aimed to assess the usefulness of a novel semi-automatic algorithm for multi slice computed tomography-derived (MSCT) quantitative estimation of aortic valve calcifications (AVC) in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve implantation (TAVI). METHODS: Ten patients with severe AS who underwent TAVI with Edwards Sapien S3 26 mm prosthesis and had a pre-procedural MSCT scan were included. Data on baseline characteristics, procedural and long-term outcomes were collected prospectively. Pre-procedural MSCT data were used for AVC evaluation with 3D modeling (calcium volume, thickness, area, density, and distribution) in a dedicated program. RESULTS: Mean calcium thickness was 4.6 (3.6-5.8) mm. Median calcium are 333.6 (274.7-386.7) mm2. We found a significant correlation between larger maximal calcium layer thickness and PVL occurrence after TAVI (P=0.039). The radial representation of the calcium distribution allowed to divide aortic valve into 3 zones and to compare each zone to parallel zone on TTE images. In zones with PVL ≥2 mean AVC was higher than in zones with PVL <2 (7354.6±4020.4 pixels vs. 4325.1±1790.6 pixels; P=0.018). Based on ROC analysis, the optimal cut-off value of AVC to predict PVL ≥2 was >6506 pixels with 57.1% sensitivity and 90.5% specificity (AUC 0.762 [95% CI: 0.564 to 0.901], P=0.029). CONCLUSIONS: Multiplane AVC quantitative evaluation provided details on total calcium amount, pattern and distribution in aortic valve. Established AVC parameters allowed better visualization of an operating area and prediction of PVL after TAVI.