Fractal Dimension Analysis for Robust Ultrasonic Non-Destructive Evaluation (NDE) of Coarse Grained Materials

Over the recent decades, there has been a growing demand on reliable and robust non-destructive evaluation (NDE) of structures and components made from coarse grained materials such as alloys, stainless steels, carbon-reinforced composites and concrete; however, when inspected using ultrasound, the flaw echoes are usually contaminated by high-level, time-invariant, and correlated grain noise originating from the microstructure and grain boundaries, leading to pretty low signal-to-noise ratio (SNR) and the flaw information being obscured or completely hidden by the grain noise. In this paper, the fractal dimension analysis of the A-scan echoes is investigated as a measure of complexity of the time series to distinguish the echoes originating from the real defects and the grain noise, and then the normalized fractal dimension coefficients are applied to the amplitudes as the weighting factor to enhance the SNR and defect detection. Experiments on industrial samples of the mild steel and the stainless steel are conducted and the results confirm the great benefits of the method

First-order statistical speckle models improve robustness and reproducibility of contrast-enhanced ultrasound perfusion estimates

Contrast-enhanced ultrasound (CEUS) permits the quantification and monitoring of adaptive tumor responses in the face of anti-angiogenic treatment, with the goal of informing targeted therapy. However, conventional CEUS image analysis relies on mean signal intensity as an estimate of tracer concentration in indicator-dilution modeling. This discounts additional information that may be available from the first-order speckle statistics in a CEUS image. Heterogeneous vascular networks, typical of tumor-induced angiogenesis, lead to heterogeneous contrast enhancement of the imaged tumor cross-section. To address this, a linear (B-mode) processing approach was developed to quantify the change in the first-order speckle statistics of B-mode cine loops due to the incursion of microbubbles. The technique, named the EDoF (effective degrees of freedom) method, was developed on tumor bearing mice (MDA-MB-231LN mammary fat pad inoculation) and evaluated using nonlinear (two-pulse amplitude modulated) contrast microbubble-specific images. To improve the potential clinical applicability of the technique, a second-generation compound probability density function for the statistics of two-pulse amplitude modulated contrast-enhanced ultrasound images was developed. The compound technique was tested in an antiangiogenic drug trial (bevacizumab) on tumor bearing mice (MDA-MB-231LN), and evaluated with gold-standard histology and contrast-enhanced X-ray computed tomography. The compound statistical model could more accurately discriminate anti-VEGF treated tumors from untreated tumors than conventional CEUS image. The technique was then applied to a rapid patient-derived xenograft (PDX) model of renal cell carcinoma (RCC) in the chorioallantoic membrane (CAM) of chicken embryos. The ultimate goal of the PDX model is to screen RCC patients for de novo sunitinib resistance. The analysis of the first-order speckle statistics of contrast-enhanced ultrasound cine loops provides more robust and reproducible estimates of tumor blood perfusion than conventional image analysis. Theoretically this form of analysis could quantify perfusion heterogeneity and provide estimates of vascular fractal dimension, but further work is required to determine what physiological features influence these measures. Treatment sensitivity matrices, which combine vascular measures from CEUS and power Doppler, may be suitable for screening of de novo sunitinib resistance in patients diagnosed with renal cell carcinoma. Further studies are required to assess whether this protocol can be predictive of patient outcome

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup

Nanoroughness, Surface Chemistry and Drug Delivery Control by Atmospheric Plasma Jet on Implantable Devices

Implantable devices need specific tailored surface morphologies and chemistries to interact with the living systems or to actively induce a biological response also by the release of drugs or proteins. These customised requirements foster technologies that can be implemented in additive manufacturing systems. Here we present a novel approach based on spraying processes that allows to control separately topographic features in the submicron range ( 3d 60 nm - 2 \ub5m), ammine or carboxylic chemistry and fluorophore release even on temperature sensitive biodegradable polymers such as polycaprolactone (PCL). We developed a two-steps process with a first deposition of 220 nm silica and poly(lactic-co-glycolide) (PLGA) fluorescent nanoparticles by aerosol followed by the deposition of a fixing layer by atmospheric pressure plasma jet (APPJ). The nanoparticles can be used to create the nano-roughness and to include active molecule release, while the capping layer ensures stability and the chemical functionalities. The process is enabled by a novel APPJ which allows deposition rates of 10 - 20 nm\ub7s-1 at temperatures lower than 50 \ub0C using argon as process gas. This approach was assessed on titanium alloys for dental implants and on PCL films. The surfaces were characterized by FT-IR, AFM and SEM. Titanium alloys were tested with pre-osteoblasts murine cells line, while PCL film with fibroblasts. Cell behaviour was evaluated by viability and adhesion assays, protein adsorption, cell proliferation, focal adhesion formation and SEM. The release of a fluorophore molecule was assessed in the cell growing media, simulating a drug release. Osteoblast adhesion on the plasma treated materials increased by 20% with respect to commercial titanium alloys implants. Fibroblast adhesion increased by a 100% compared to smooth PCL substrate. The release of the fluorophore by the dissolution of the PLGA nanoparticles was verified and the integrity of the encapsulated drug model confirmed