Model-Optimizing Radiofrequency Parameters of 3D Finite Element Analysis for Ablation of Benign Thyroid Nodules.

Radiofrequency (RF) ablation represents an efficient strategy to reduce the volume of thyroid nodules. In this study, a finite element model was developed with the aim of optimizing RF parameters, e.g., input power and treatment duration, in order to achieve the target volume reduction rate (VRR) for a thyroid nodule. RF ablation is modelled as a coupled electro-thermal problem wherein the electric field is applied to induce tissue heating. The electric problem is solved with the Laplace equation, the temperature distribution is estimated with the Pennes bioheat equation, and the thermal damage is evaluated using the Arrhenius equation. The optimization model is applied to RF electrode with different active tip lengths in the interval from 5 mm to 40 mm at the 5 mm step. For each case, we also explored the influence of tumour blood perfusion rate on RF ablation outcomes. The model highlights that longer active tips are more efficient as they require lesser power and shorter treatment time to reach the target VRR. Moreover, this condition is characterized by a reduced transversal ablation zone. In addition, a higher blood perfusion increases the heat dispersion, requiring a different combination of RF power and time treatment to achieve the target VRR. The model may contribute to an improvement in patient-specific RF ablation treatment.

Harnessing Biofabrication Strategies to Re-Surface Osteochondral Defects: Repair, Enhance, and Regenerate.

Osteochondral tissue (OC) is a complex and multiphasic system comprising cartilage and subchondral bone. The discrete OC architecture is layered with specific zones characterized by different compositions, morphology, collagen orientation, and chondrocyte phenotypes. To date, the treatment of osteochondral defects (OCD) remains a major clinical challenge due to the low self-regenerative capacity of damaged skeletal tissue, as well as the critical lack of functional tissue substitutes. Current clinical approaches fail to fully regenerate damaged OC recapitulating the zonal structure while granting long-term stability. Thus, the development of new biomimetic treatment strategies for the functional repair of OCDs is urgently needed. Here, we review recent developments in the preclinical investigation of novel functional approaches for the resurfacing of skeletal defects. The most recent studies on preclinical augmentation of OCDs and highlights on novel studies for the in vivo replacement of diseased cartilage are presented.

3D Tortuosity and Diffusion Characterization in the Human Mineralized Collagen Fibril Using a Random Walk Model.

Bone tissue is mainly composed at the nanoscale of apatite minerals, collagen molecules and water that form the mineralized collagen fibril (MCF). In this work, we developed a 3D random walk model to investigate the influence of bone nanostructure on water diffusion. We computed 1000 random walk trajectories of water molecules within the MCF geometric model. An important parameter to analyse transport behaviour in porous media is tortuosity, computed as the ratio between the effective path length and the straight-line distance between initial and final points. The diffusion coefficient is determined from the linear fit of the mean squared displacement of water molecules as a function of time. To achieve more insight into the diffusion phenomenon within MCF, we estimated the tortuosity and diffusivity at different quotes in the longitudinal direction of the model. Tortuosity is characterized by increasing values in the longitudinal direction. As expected, the diffusion coefficient decreases as tortuosity increases. Diffusivity outcomes confirm the findings achieved by experimental investigations. The computational model provides insights into the relation between the MCF structure and mass transport behaviour that may contribute to the improvement of bone-mimicking scaffolds.

A Proof of Concept Combined Using Mixed Reality for Personalized Neurorehabilitation of Cerebellar Ataxic Patients.

BACKGROUND: Guidelines for degenerative cerebellar ataxia neurorehabilitation suggest intensive coordinative training based on physiotherapeutic exercises. Scientific studies demonstrate virtual exergaming therapeutic value. However, patient-based personalization, post processing analyses and specific audio-visual feedbacks are not provided. This paper presents a wearable motion tracking system with recording and playback features. This system has been specifically designed for ataxic patients, for upper limbs coordination studies with the aim to retrain movement in a neurorehabilitation setting. Suggestions from neurologists and ataxia patients were considered to overcome the shortcomings of virtual systems and implement exergaming. METHODS: The system consists of the mixed-reality headset Hololens2 and a proprietary exergaming implemented in Unity. Hololens2 can track and save upper limb parameters, head position and gaze direction in runtime. RESULTS: Data collected from a healthy subject are reported to demonstrate features and outputs of the system. CONCLUSIONS: Although further improvements and validations are needed, the system meets the needs of a dynamic patient-based exergaming for patients with cerebellar ataxia. Compared with existing solutions, the mixed-reality system is designed to provide an effective and safe therapeutic exergaming that supports both primary and secondary goals of an exergaming: what a patient should do and how patient actions should be performed.

A Comparative Study on a Novel Quality Assessment Protocol Based on Image Analysis Methods for Color Doppler Ultrasound Diagnostic Systems.

Color Doppler (CD) imaging is widely used in diagnostics since it allows real-time detection and display of blood flow superimposed on the B-mode image. Nevertheless, to date, a shared worldwide standard on Doppler equipment testing is still lacking. In this context, the study herein proposed would give a contribution focusing on the combination of five test parameters to be included in a novel Quality Assessment (QA) protocol for CD systems testing. A first approach involving the use of the Kiviat diagram was investigated, assuming the diagram area, normalized with respect to one of the gold standards, as an index of the overall Doppler system performance. The QA parameters were obtained from the post-processing of CD data through the implementation of custom-written image analysis methods and procedures, here applied to three brand-new high-technology-level ultrasound systems. Experimental data were collected through phased and convex array probes, in two configuration settings, by means of a Doppler flow phantom set at different flow rate regimes. The outcomes confirmed that the Kiviat diagram might be a promising tool applied to quality controls of Doppler equipment, although further investigations should be performed to assess the sensitivity and specificity of the proposed approach.

Artificial Intelligence in Thyroid Field-A Comprehensive Review.

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

3D random walk model of diffusion in human Hypo- and Hyper- mineralized collagen fibrils.

Bone tissue is composed at the nanoscale of apatite minerals, collagen molecules and water that form the mineralized collagen fibril (MCF). Water has a crucial role in bone biomineralization. We developed a 3D random walk model to investigate the water diffusion process within the MCF for three different scenarios, namely low, intermediate and high mineral volume fraction. The MCF geometric model is obtained after applying 6·106 translational and rotational perturbations to an ordered arrangement of mineral. Subsequently, we compute 300 random trajectories of water molecules within the MCF for each mineral volume fraction. Every trajectory is constituted of up to 500 k positions of the water particle. We determined the diffusion coefficient from the linear fit of the mean squared displacement of water molecules as a function of time. We investigate changes in the diffusivity values in relation to variation of bone mineral content. The analysis performed on the random walk data, for all mineralization conditions, leads to diffusion coefficients in good agreement with the diffusivity outcomes achieved from previous experimental studies. Thus, the 3D geometrical configuration adopted in this numerical study appears suitable for modelling the MCF with different volume fractions, from hypo- to hyper-mineralized conditions. We observed that low mineral content is associated with an increase of the water diffusion, while lower values of diffusivity are determined in hypermineralized conditions. In agreement with experimental data, our results highlight the influence of the structural alterations on the mass transport properties.

Percolation networks inside 3D model of the mineralized collagen fibril.

Bone is a hierarchical biological material, characterized at the nanoscale by a recurring structure mainly composed of apatite mineral and collagen, i.e. the mineralized collagen fibril (MCF). Although the architecture of the MCF was extensively investigated by experimental and computational studies, it still represents a topic of debate. In this work, we developed a 3D continuum model of the mineral phase in the framework of percolation theory, that describes the transition from isolated to spanning cluster of connected platelets. Using Monte Carlo technique, we computed overall 120 × 106 iterations and investigated the formation of spanning networks of apatite minerals. We computed the percolation probability for different mineral volume fractions characteristic of human bone tissue. The findings highlight that the percolation threshold occurs at lower volume fractions for spanning clusters in the width direction with respect to the critical mineral volume fractions that characterize the percolation transition in the thickness and length directions. The formation of spanning clusters of minerals represents a condition of instability for the MCF, as it could be the onset of a high susceptibility to fracture. The 3D computational model developed in this study provides new, complementary insights to the experimental investigations concerning human MCF.

A 3D Model of the Effect of Tortuosity and Constrictivity on the Diffusion in Mineralized Collagen Fibril.

Bone tissue is a hierarchically structured material composed at the nanoscale by an organic matrix of collagen type I, apatite mineral and water. We considered an idealized 3D geometrical model of the mineralized collagen fibril in order to analyze the influence of structural factors, i.e. tortuosity, constrictivity, on the water effective diffusivity. The average values of the factors investigated in the diffusivity are computed on 5000 iterations by means of the Montecarlo method. The input parameters of the numerical model are the geometrical dimensions of the apatite mineral, collagen fibrils and their spatial orientation obtained with random extractions from Gaussian probability distribution functions. We analyzed the diffusion phenomenon for concentration gradients parallel to three orthogonal directions (Length, Width and Thickness) and for different scenarios, namely low, intermediate and high apatite volume fraction. For each degree of volume fraction, in the thickness direction, the tortuosity assumes greater values, up to two orders of magnitude, in comparison with the tortuous factors computed in the other directions, highlighting the anisotropy of the nanostructure. Furthermore, it was found that the tortuosity is the dominant parameter which control the effective transport properties within the mineralized collagen fibrils.

3D diffusion model within the collagen apatite porosity: An insight to the nanostructure of human trabecular bone.

Bone tissue at nanoscale is a composite mainly made of apatite crystals, collagen molecules and water. This work is aimed to study the diffusion within bone nanostructure through Monte-Carlo simulations. To this purpose, an idealized geometric model of the apatite-collagen structure was developed. Gaussian probability distribution functions were employed to design the orientation of the apatite crystals with respect to the axes (length L, width W and thickness T) of a plate-like trabecula. We performed numerical simulations considering the influence of the mineral arrangement on the effective diffusion coefficient of water. To represent the hindrance of the impermeable apatite crystals on the water diffusion process, the effective diffusion coefficient was scaled with the tortuosity, the constrictivity and the porosity factors of the structure. The diffusion phenomenon was investigated in the three main directions of the single trabecula and the introduction of apatite preferential orientation allowed the creation of an anisotropic medium. Thus, different diffusivities values were observed along the axes of the single trabecula. We found good agreement with previous experimental results computed by means of a genetic algorithm.