Showing papers by "Francesco Amato published in 2019"

Deep Convolution Neural Network (DCNN) Multiplane Approach to Synthetic CT Generation From MR images-Application in Brain Proton Therapy.

Abstract: Purpose The first aim of this work is to present a novel deep convolution neural network (DCNN) multiplane approach and compare it to single-plane prediction of synthetic computed tomography (sCT) by using the real computed tomography (CT) as ground truth. The second aim is to demonstrate the feasibility of magnetic resonance imaging (MRI)-based proton therapy planning for the brain by assessing the range shift error within the clinical acceptance threshold. Methods and Materials The image database included 15 pairs of MRI/CT scans of the head. Three DCNNs were trained to estimate, for each voxel, the Hounsfield unit (HU) value from MRI intensities. Each DCNN gave an estimation in the axial, sagittal, and coronal plane, respectively. The median HU among the 3 values was selected to build the sCT. The sCT/CT agreement was evaluated by a mean absolute error (MAE) and mean error, computed within the head contour and on 6 different tissues. Dice similarity coefficients were calculated to assess the geometric overlap of bone and air cavities segmentations. A 3-beam proton therapy plan was simulated for each patient. Beam-by-beam range shift (RS) analysis was conducted to assess the proton-stopping power estimation. RS analysis was performed using clinically accepted thresholds of (1) 3.5% + 1 mm and (2) 2.5% + 1.5 mm of the total range. Results DCNN multiplane statistically outperformed single-plane prediction of sCT (P Conclusions The multiplane DCNN approach significantly improved the sCT prediction compared with other DCNN methods presented in the literature. The method was demonstrated to be highly accurate for MRI-only proton planning purposes.

Cortical-like mini-columns of neuronal cells on zinc oxide nanowire surfaces.

Abstract: A long-standing goal of neuroscience is a theory that explains the formation of the minicolumns in the cerebral cortex. Minicolumns are the elementary computational units of the mature neocortex. Here, we use zinc oxide nanowires with controlled topography as substrates for neural-cell growth. We observe that neuronal cells form networks where the networks characteristics exhibit a high sensitivity to the topography of the nanowires. For certain values of nanowires density and fractal dimension, neuronal networks express small world attributes, with enhanced information flows. We observe that neurons in these networks congregate in superclusters of approximately 200 neurons. We demonstrate that this number is not coincidental: the maximum number of cells in a supercluster is limited by the competition between the binding energy between cells, adhesion to the substrate, and the kinetic energy of the system. Since cortical minicolumns have similar size, similar anatomical and topological characteristics of neuronal superclusters on nanowires surfaces, we conjecture that the formation of cortical minicolumns is likewise guided by the interplay between energy minimization, information optimization and topology. For the first time, we provide a clear account of the mechanisms of formation of the minicolumns in the brain.

Efficacy of Machine Learning in Predicting the Kind of Delivery by Cardiotocography

Abstract: It is well known that the interpretation of cardiotocographic (CTG) signals is still subjective and prone to misinterpretations; as a consequence, there has been an increase of cesarean sections, often not necessary, and initial expectations of significantly reducing perinatal morbidity and mortality have been unattended. Nevertheless, in developed countries, CTG is still the most widely employed prenatal technique for monitoring fetal health; and in many countries it represents a medical report with legal value. To overcome the drawbacks of the visual interpretation, many computerized systems for automatic or semi-automatic analysis of CTG have been developed in the last years. Recently, in order to support the diagnosis process, and to increase the predictive capability of these systems, also other techniques such as artificial neural network, deep learning and machine learning have been introduced. In previous works of the authors, software for automatic CTG analysis has been developed and described in detail. Now, by employing a dataset of features extracted from CTG signals with that software, to enhance its performances, different algorithms, such as J48, Adaboosting, Random Forests and Gradient Boosted Tree, have been tested to predict whether a birth would be a caesarean section or a vaginal delivery. The RF algorithm showed the best performance, since it reached the highest accuracy (87.6%), precision (87.9%) and AUCROC (93.0%). These preliminary results are very satisfying and encouraging; they confirm that to enrich the CTG analysis software with this methodology can help to significantly improve CTG classification.

An Observer-Based Output Feedback Controller for the Finite-Time Stabilization of Markov Jump Linear Systems

Abstract: In this letter, we investigate the finite-time output feedback control problem for continuous-time Markov jump linear systems. In this context, the first result is a sufficient condition for stochastic finite-time stability, requiring the solution of a feasibility problem constrained by differential linear matrix inequalities. Afterward, we consider the stabilization problem via output feedback dynamical controllers. The usual machinery pursued in the deterministic case would lead to stabilization conditions depending on differential bilinear matrix inequalities, that cannot be solved in practice. Therefore, a different methodology, based on the separation approach provided by Amato et al. , is exploited to design an observer-based output feedback controller, which can be computed by solving an optimization problem depending on linear constraints. A non-trivial application example, involving the finite-time stabilization of the longitudinal dynamics of a helicopter, is presented in order to illustrate the effectiveness of the proposed technique.

A Multistate Friction Model for the Compensation of the Asymmetric Hysteresis in the Mechanical Response of Pneumatic Artificial Muscles

Abstract: These days, biomimetic and compliant actuators have been made available to the main applications of rehabilitation and assistive robotics. In this context, the interaction control of soft robots, mechatronic surgical instruments and robotic prostheses can be improved through the adoption of pneumatic artificial muscles (PAMs), a class of compliant actuators that exhibit some similarities with the structure and function of biological muscles. Together with the advantage of implementing adaptive compliance control laws, the nonlinear and hysteretic force/length characteristics of PAMs pose some challenges in the design and implementation of tracking control strategies. This paper presents a parsimonious and accurate model of the asymmetric hysteresis observed in the force response of PAMs. The model has been validated through the experimental identification of the mechanical response of a small-sized PAM where the asymmetric effects of hysteresis are more evident. Both the experimental results and a comparison with other dynamic friction models show that the proposed model could be useful to implement efficient compensation strategies for the tracking control of soft robots.

Relating the rate of growth of metal nanoparticles to cluster size distribution in electroless deposition

Abstract: Electroless deposition on patterned silicon substrates enables the formation of metal nanomaterials with tight control over their size and shape. In the technique, metal ions are transported by diffusion from a solution to the active sites of an autocatalytic substrate where they are reduced as metals upon contact. Here, using diffusion limited aggregation models and numerical simulations, we derived relationships that correlate the cluster size distribution to the total mass of deposited particles. We found that the ratio ξ between the rates of growth of two different metals depends on the ratio γ between the rates of growth of clusters formed by those metals through the linearity law ξ = 14(γ − 1). We then validated the model using experiments. Different from other methods, the model derives k using as input the geometry of metal nanoparticle clusters, decoded by SEM or AFM images of samples, and a known reference.

Mathematical Model of the Release of the cTnT and CK-MB cardiac biomarkers in patients with acute myocardial infarction

Abstract: The diagnosis of an acute myocardial infarction (AMI) is based, among other things, on the measurement of the level of cardiac biomarkers in the plasma. In order to optimize and speed up the analysis of cardiac Troponin T (cTnT), we have previously developed a mathematical model, which can be used to estimate the patient-specific cTnT release curve, based on a discrete number of acquisitions. Here, the effectiveness of our approach is improved by the extending the model to take into account also the release of another biomarker of cardiac damage, namely the creatine kinase MB (CK-MB). Moreover, the description of cTnT release dynamics has also been improved, taking into account a delay mechanism in the release from cytoplasm to plasma. Structural identifiability of the devised model is studied and a procedure for the identification of the parameters is set up and tested on a clinical dataset to assess the effectiveness of the proposed approach.

Transforming diatomaceous earth into sensing devices by surface modification with gold nanoparticles

Abstract: Diatomaceous earth, or diatomite, is produced through the accumulation of diatom (Bacillariophyceae) skeletons (i.e. cell walls called frustules) made of amorphous silica. The porous, highly symmetrical structure and microscopic size of diatom cell walls make them ideal constituents of sensing devices and analytical chips. Here, we propose chemical methods to purify diatom frustules extracted from diatomaceous earth. Using photo deposition techniques, we grow gold nanoparticles on the surface of diatom skeletons and within the pores of the skeletons, where the size and density of nanoparticles can be controlled by changing the parameters of the synthesis. Resulting devices have an internal porous structure that can harvest molecules from a solution, and an external shell of gold nanoparticles that amplifies the electromagnetic field generated by the measurement laser in Raman or other spectroscopies. The combination of these effects enables the analysis of biological specimens, chemical analytes and pollutants in extremely low abundance ranges. The devices were demonstrated in the analysis of Bovine Serum Albumin in water with a concentration of 100 aM, and mineral oil with a concentration of 50 ppm.

Annular Finite-Time Stability and Stabilization of Continuous-Time Markov Jump Linear Systems

Abstract: In this paper we tackle some control problems related to the class of continuous-time, Markov jump linear systems. First of all, the annular stochastic finite-time stability problem is considered, and two different sufficient conditions are derived. Both conditions require the solution of a feasibility problem based on differential linear matrix inequalities. The analysis conditions are the starting point to solve the state-feedback design problem. Some numerical examples, considering also an electrical circuit, show the effectiveness of the proposed approach.

A quantitative approach for determining the role of geometrical constraints when shaping mesenchymal condensations

Abstract: In embryogenesis, mesenchymal condensation is a critical event during the formation of many organ systems, including cartilage and bone. During organ formation, mesenchymal cells aggregate and undergo compaction while activating developmental programmes. The final three-dimensional form of the organ, as well as cell fates, can be influenced by the size and shape of the forming condensation. This process is hypothesized to result from multiscale cell interactions within mesenchymal microenvironments; however, these are complex to investigate in vivo. Three-dimensional in vitro models that recapitulate key phenotypes can contribute to our understanding of the microenvironment interactions regulating this fundamental developmental process. Here we devise such models by using image analysis to guide the design of polydimethylsiloxane 3D microstructures as cell culture substrates. These microstructures establish geometrically constrained micromass cultures of mouse embryonic skeletal progenitor cells which influence the development of condensations. We first identify key phenotypes differentiating face and limb bud micromass cultures by linear discriminant analysis of the shape descriptors for condensation morphology, which are used to guide the rational design of a micropatterned polydimethylsiloxane substrate. High-content imaging analysis highlights that the geometry of the microenvironment affects the establishment and growth of condensations. Further, cells commit to establish condensations within the first 5 h; condensations reach their full size within 17 h; following which they increase cell density while maintaining size for at least 7 days. These findings elucidate the value of our model in dissecting key aspects of mesenchymal condensation development.

Identification of the infarct time in patients with acute myocardial infarction

Abstract: In the present work, we introduce a novel technique to identify the infarct time from time-series meaurements of the cardiac troponin T (cTnT) into plasma. Although this information is extremely valuable from a clinical standpoint, it is not always possible to establish with certainty the exact infarct time. Here, we show how the infarct time can be reliably estimated from the cTnT release data in the first few hours after AMI, by using an optimization-based procedure and a model-based approach. To validate the present approach, we have used a clinical dataset of patients in whom the infarct has been induced and, therefore, the infarct time is certain.