Improving the Quality of Monostatic Synthetic-Aperture Ultrasound Imaging Through Deep-Learning-Based Beamforming

In synthetic aperture (SA) ultrasound imaging, either monostatic or multistatic approaches can be employed. In both cases, in transmission, a single element of the transducer array is used at each time. In reception, the same element is used for the monostatic approach, while the whole array is used for the multistatic one. Thus, the monostatic approach could be implemented using a simpler single-channel architecture, however at the expense of image quality, while the multistatic one provides a high quality image but requires a more complex N-channel system. In this work, we show that a deep neural network can be trained to reconstruct images with a high contrast, as in the multistatic SA case (considering a 128-element array), but starting from the pre-beamforming signals acquired through the monostatic SA approach. We implemented a U-net and trained it using 27200 simulated signal-sets and the corresponding target images generated with Field II, considering numerical phantoms with random elliptical targets. The deep neural network (DNN) output image quality was evaluated in terms of contrast on a test set made of 500 simulated images, and on experimental scans of a commercial phantom and of the carotid artery. The results show that, after training over 39 epochs, the DNN is able to provide images with a good quality starting from the radiofrequency signals obtained with a simple monostatic SA approach, potentially requiring a single-channel only

High-frame-rate coherence imaging of the heart with ultrasound diverging waves

Several techniques have been proposed up to now to achieve higher temporal resolution in echocardiography. Among these, the use of diverging beams, which insonify a large region of interest, allows to significantly increase the frame-rate, but at the cost of a reduced signal-to-noise ratio. For this reason, in this paper we propose to combine high-frame-rate imaging, by transmitting diverging waves (DWs), to the Short-Lag Spatial Coherence (SLSC) technique in reception, which provides images of the coherence of backscattered echoes and is known to yield improved contrast in scenarios with high-clutter. We test this combined method first on phantom acquisitions and then on in vivo cardiac scans, i.e. on apical views of the heart. Results show that SLSC can provide improved contrast ratio (CR) and generalized contrast-to-noise ratio (GCNR) with respect to the classic Delay and Sum (DAS) as the number of transmitted DWs increases, particularly when clutter is present. Indeed, cardiac images show improved apex visibility and artifact suppression in the heart chambers with SLSC, achieving high contrast and high frame-rate at the same time

A Novel Large Structured Cardiotocographic Database

In this work we present the creation of a large, structured database of CardioTocoGraphic (CTG) recordings, starting from a raw dataset containing tracings collected between 2013 and 2021 by the medical team of the University Hospital Federico II of Naples. The aim of the work is to provide a big, structured database of real clinical cardiotocographic data, useful for subsequent processing and analysis through state-of-the-art methods, in particular Deep Learning Methods. This organized dataset could lead to an increase of the diagnostic accuracy of CTG analysis in the discrimination of healthy and unhealthy fetuses

A deep learning mixed-data type approach for the classification of FHR signals

The Cardiotocography (CTG) is a widely diffused monitoring practice, used in Ob-Gyn Clinic to assess the fetal well-being through the analysis of the Fetal Heart Rate (FHR) and the Uterine contraction signals. Due to the complex dynamics regulating the Fetal Heart Rate, a reliable visual interpretation of the signal is almost impossible and results in significant subjective inter and intra-observer variability. Also, the introduction of few parameters obtained from computer analysis did not solve the problem of a robust antenatal diagnosis. Hence, during the last decade, computer aided diagnosis systems, based on artificial intelligence (AI) machine learning techniques have been developed to assist medical decisions. The present work proposes a hybrid approach based on a neural architecture that receives heterogeneous data in input (a set of quantitative parameters and images) for classifying healthy and pathological fetuses. The quantitative regressors, which are known to represent different aspects of the correct development of the fetus, and thus are related to the fetal healthy status, are combined with features implicitly extracted from various representations of the FHR signal (images), in order to improve the classification performance. This is achieved by setting a neural model with two connected branches, consisting respectively of a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN). The neural architecture was trained on a huge and balanced set of clinical data (14.000 CTG tracings, 7000 healthy and 7000 pathological) recorded during ambulatory non stress tests at the University Hospital Federico II, Napoli, Italy. After hyperparameters tuning and training, the neural network proposed has reached an overall accuracy of 80.1%, which is a promising result, as it has been obtained on a huge dataset

Discriminating Healthy and IUGR fetuses through Machine Learning models

The purpose of this study is to develop and understand whether Machine Learning models can classify Cardiotocographic (CTG) recordings of healthy fetuses or Intra Uterine Growth Restricted (IUGR) fetuses, highlighting how a large amount of data can have unexpected effects. We started from other findings in the literature to see what Machine Learning model remained consistent even with a large amount of data. The CTG records used in this study were collected at the Department of Obstetrics of the Federico II University Hospital in Naples, Italy, from 2013 to 2021. From this dataset, we chose 1548 IUGR fetuses and 1548 healthy fetuses to train our models. Each recording contained several parameters, ranging from features calculated on the entire CTG tracing, features calculated every 3 and 1 minute of recording and features related to the pregnant woman, such as age and week of gestation. We trained our machine-learning models on this dataset, checking the results obtained before and after adjusting the hyperparameters, noting that among the best models was Random Forest, which has already been present in other studies, and that the Multilayer Perceptron and the AdaBoost classifier were overall the best performing. This work can surely form a basis for future works in the fetal heart rate classification thus leading to real clinical applications

Generalization of a deep learning network for beamforming and segmentation of ultrasound images

Recently, deep neural networks (DNNs) for beamforming and segmenting plane-wave ultrasound images have been proposed. The promising results obtained so far focus on segmenting anechoic, almost circular structures using one architecture trained on a large dataset. We present a study of DNNs generalizability for beamforming and segmenting structures of various shapes and echogenicity. Three different encoder architectures (i.e. VGG13/16/19) and target images with standard dynamic range (dR = 60 dB, E60) or an automatically determined dR (Eauto) were compared. Field II was used to simulate 6560 images (with hyperechoic, hypoechoic, anechoic and mixed targets) using random bunches of ellipses to generate different shapes for DNN training. The test set included 816 simulated images, 21 images of a phantom (CIRS040GSE) and 24 images of the carotid artery. The DNN architecture has 1 encoder and 2 decoders, for segmentation and beamforming, based on the UNet. Using the VGG19 trained with Eauto images, a considerable improvement was achieved when compared to other architectures, especially when performing tests on experimental data. Overall, the promising results obtained encourage us to further investigate the use of DNNs for beamforming and segmentation, with the aim to improve the performance and generalize their use for specific ultrasound imaging applications

Discriminating Healthy and IUGR fetuses through Machine Learning models

The purpose of this study is to develop and understand whether Machine Learning models can classify Cardiotocographic (CTG) recordings of healthy fetuses or Intra Uterine Growth Restricted (IUGR) fetuses, highlighting how a large amount of data can have unexpected effects. We started from other findings in the literature to see what Machine Learning model remained consistent even with a large amount of data. The CTG records used in this study were collected at the Department of Obstetrics of the Federico II University Hospital in Naples, Italy, from 2013 to 2021. From this dataset, we chose 1548 IUGR fetuses and 1548 healthy fetuses to train our models. Each recording contained several parameters, ranging from features calculated on the entire CTG tracing, features calculated every 3 and 1 minute of recording and features related to the pregnant woman, such as age and week of gestation. We trained our machine-learning models on this dataset, checking the results obtained before and after adjusting the hyperparameters, noting that among the best models was Random Forest, which has already been present in other studies, and that the Multilayer Perceptron and the AdaBoost classifier were overall the best performing. This work can surely form a basis for future works in the fetal heart rate classification thus leading to real clinical applications

Mountain Rescuers through the computation of Sample Entropy

In the present study we propose a novel method to automatically assess the quality of ECG signals collected through a wearable device in typical mountain rescuers activities. ECGs signals have been obtained during sessions of programmed field tests at the Bormio Ski Resort (Valtellina, Lombardy, Italy) in the month of March. Here, following the defined protocol, a group of 15 mountain rescuers has carried out daily rescuers' activities, while wearing wearable textile system by Smartex Srl. The test protocol was designed to simulate the real physiological demands of mountain rescuers during their emergency deployments. Among the activities performed rescuers had to walk up and down hill in snow-covered trails, carrying stretchers onto which simulated victims were located etc... To infer the quality of ECG signals recorded we developed an algorithm for the automatic evaluation of collected signal deterioration. This method is based on the analysis of regularity of ECGs' P-QRS-T complexes pattern. To estimate the maintenance of typical ECGs pattern shape, Sample Entropy (SampEn) was computed in moving fixed-length windows sliding along the signal, obtained after applying wavelet transform of the row ECG. The SampEn indices series was then thresholded to spot ECG points where P-QRS-T complexes were more or less easy to identify, respect to points where signal quality was completely deteriorated. Moreover, we evaluated signal quality maintenance while performing low and high intensity activities

Miglioramento della funzionalit\ue0 fluviale: risultati del Progetto Life+ P.A.R.C

La realizzazione di passaggi per pesci lungo il fiume Vara-Magra nell'ambito del progetto Life P.A.R.C. ha favorito il successo riproduttivo della Lampreda di mare Petromyzon marinus e la risalita della Cheppia Alosa fallax verso siti riproduttivi non pi\uf9 raggiunti9 da quando la continuit\ue0 longitudinale del fiume era stata interrotta dalla costruzione di sbarramenti trasversali

STATUS OF AUSTROPOTAMOBIUS PALLIPES COMPLEX IN THE WATERCOURSES OF THE ALESSANDRIA PROVINCE (N-W ITALY)

Information was gathered on the distribution of white-clawed crayfish Austropotamobius pallipes complex in the watercourses of the Alessandria province (NW Italy), on the biological and ecological preferences of the species, and on the features of the biotopes in which it is found. A total of 409 sites on 361 watercourses connected to the main sub-basins of the Po River were analysed, with data gathered during the field research phase, conducted for three consecutive summers from 2002 through 2004. Thirteen percent of the sites investigated were found to currently house crayfish populations. The persistence in time of superficial water and natural morphology of the watercourse were found to be the chief requirements for the presence of crayfish. Data on land use, human density and other factors which potentially limit the survival of crayfish populations were also analysed