Linear-Array Photoacoustic Imaging Using Minimum Variance-Based Delay Multiply and Sum Adaptive Beamforming Algorithm

In Photoacoustic imaging (PA), Delay-and-Sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely Delay-Multiply-and-Sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a novel beamformer is introduced using Minimum Variance (MV) adaptive beamforming combined with DMAS, so-called Minimum Variance-Based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45 mm MVB-DMAS results in about 31 dB, 18 dB and 8 dB sidelobes reduction compared to DAS, MV and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96 %, 94 % and 45 % and signal-to-noise ratio about 89 %, 15 % and 35 % compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33 mm of the experimental images, MVB-DMAS results in about 20 dB sidelobes reduction in comparison with other beamformers.Comment: This is the final version of this paper, which is accepted in the "Journal of Biomedical Optics". Compared to previous versions, this version contains more experiments and evaluatio

An effective approach for road asset management through the FDTD simulation of the GPR signal

Ground-penetrating radar is a non-destructive tool widely used in many fields of application including pavement engineering surveys. Over the last decade, the need for further breakthroughs capable to assist end-users and practitioners as decision-support systems in more effective road asset management is increasing. In more details and despite the high potential and the consolidated results obtained over years by this non-destructive tool, pavement distress manuals are still based on visual inspections, so that only the effects and not the causes of faults are generally taken into account. In this framework, the use of simulation can represent an effective solution for supporting engineers and decision-makers in understanding the deep responses of both revealed and unrevealed damages. In this study, the potential of using finite-difference time-domain simulation of the ground-penetrating radar signal is analyzed by simulating several types of flexible pavement at different center frequencies of investigation typically used for road surveys. For these purposes, the numerical simulator GprMax2D, implementing the finite-difference time-domain method, was used, proving to be a highly effective tool for detecting road faults. In more details, comparisons with simplified undisturbed modelled pavement sections were carried out showing promising agreements with theoretical expectations, and good chances for detecting the shape of damages are demonstrated. Therefore, electromagnetic modelling has proved to represent a valuable support system in diagnosing the causes of damages, even for early or unrevealed faults. Further perspectives of this research will be focused on the modelling of more complex scenarios capable to represent more accurately the real boundary conditions of road cross-sections. Acknowledgements - This work has benefited from networking activities carried out within the EU funded COST Action TU1208 “Civil Engineering Applications of Ground Penetrating Radar”

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and in conjunction with decision-theoretic approaches used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue

Inferring strength and deformation properties of hot mix asphalt layers from the GPR signal: recent advances

The great flexibility of ground-penetrating radar has led to consider worldwide this instrument as an effective and efficient geophysical tool in several fields of application. As far as pavement engineering is concerned, ground-penetrating radar is employed in a wide range of applications, including physical and geometrical evaluation of road pavements. Conversely, the mechanical characterization of pavements is generally inferred through traditional (e.g., plate bearing test method) or advanced non-destructive techniques (e.g., falling weight deflectometer). Nevertheless, measurements performed using these methods, inevitably turn out to be both much more time-consuming and low-significant whether compared with ground-penetrating radar’s potentials. In such a framework, a mechanical evaluation directly coming from electromagnetic inspections could represent a real breakthrough in the field of road assets management. With this purpose, a ground-penetrating radar system with 600 MHz and 1600 MHz center frequencies of investigation and ground-coupled antennas was employed to survey a 4m×30m flexible pavement test site. The test area was marked by a regular grid mesh of 836 nodes, respectively spaced by a distance of 0.40 m alongside the horizontal and vertical axes. At each node, the elastic modulus was measured using a light falling weight deflectometer. Data processing has provided to reconstruct a 3-D matrix of amplitudes for the surveyed area, considering a depth of around 300 mm, in accord to the influence domain of the light falling weight deflectometer. On the other hand, deflectometric data were employed for both calibration and validation of a semi-empirical model by relating the amplitude of signal reflections through the media along fixed depths within the depth domain considered, and the Young’s modulus of the pavement at the evaluated point. This statistically-based model is aimed at continuously taking into account alongside the depth of investigation, of both the different strength provision of each layer composing the hot mix asphalt pavement structure, and of the attenuation occurring to electromagnetic waves during their in-depth propagation. Promising results are achieved by matching modelled and measured elastic modulus data. This continuous statistically-based model enables to consider the whole set of information related to each single depth, in order to provide a more comprehensive prediction of the strength and deformation behavior of such a complex multi-layered medium. Amongst some further developments to be tackled in the near future, a model improvement could be reached through laboratory activities under controlled conditions and by adopting several frequency bandwidths suited for purposes. In addition, the perspective to compare electromagnetic data with mechanical measurements retrieved continuously, i.e. by means of specifically equipped lorries, could pave the way to considerable enhancements in this field of research. Acknowledgements - This work has benefited from networking activities carried out within the EU funded COST Action TU1208 “Civil Engineering Applications of Ground Penetrating Radar”

Breast cancer detection using forward scattering radar technique

This paper presents an initial investigation for breast cancer detection using a special mode of bistatic radar system known as forward scattering radar (FSR). The proposed method analyzes the Doppler frequency in the received signal scattered from the tumor for cancer detection and localization. Three systems of architectures were analyzed which determined by the mechanical movement of transmitter or receiver or both. This paper also discusses an initial simulated result by using CST Microwave Studio as a feasibility study of utilizing FSR for breast cancer detection. It is shown that by investigating the unique character of Radar Cross Section (RCS) for breast tissue and tumor of FSR a cancer can be predicted. Electromagnetic model including fatty tissue and a tumor were simulated to obtain RCS parameter and analyzed as well as compared with whose fatty tissue without cancerous lesion to pinpoint the presence of tumor from its FSR signature. The result shows a significant different between these two models in FS RCS

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing