Synthetic Elastography using B-mode Ultrasound through a Deep Fully-Convolutional Neural Network

Shear-wave elastography (SWE) permits local estimation of tissue elasticity, an important imaging marker in biomedicine. This recently-developed, advanced technique assesses the speed of a laterally-travelling shear wave after an acoustic radiation force "push" to estimate local Young's moduli in an operator-independent fashion. In this work, we show how synthetic SWE (sSWE) images can be generated based on conventional B-mode imaging through deep learning. Using side-by-side-view B-mode/SWE images collected in 50 patients with prostate cancer, we show that sSWE images with a pixel-wise mean absolute error of 4.5+/-0.96 kPa with regard to the original SWE can be generated. Visualization of high-level feature levels through t-Distributed Stochastic Neighbor Embedding reveals substantial overlap between data from two different scanners. Qualitatively, we examined the use of the sSWE methodology for B-mode images obtained with a scanner without SWE functionality. We also examined the use of this type of network in elasticity imaging in the thyroid. Limitations of the technique reside in the fact that networks have to be retrained for different organs, and that the method requires standardization of the imaging settings and procedure. Future research will be aimed at development of sSWE as an elasticity-related tissue typing strategy that is solely based on B-mode ultrasound acquisition, and the examination of its clinical utility.Comment: (c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Donald J. Jamison Sr. v. Utah Home Fire Insurance Company : Brief of Respondent

Appeal from the Judgment of the Third Judicial District Court, Salt Lake County- Honorable Stewart M. Hanson, Jr

Deficiency of the Metalloproteinase-Disintegrin ADAM8 Is Associated with Thymic Hyper-Cellularity

Thymopoiesis requires thymocyte-stroma interactions and proteases that promote cell migration by degrading extracellular matrix and releasing essential cytokines and chemokines. A role for several members of the A Disintegrin and Metalloprotease (ADAM) family in T cell development has been reported in the past

Oral Biofilm Architecture on Natural Teeth

Periodontitis and caries are infectious diseases of the oral cavity in which oral biofilms play a causative role. Moreover, oral biofilms are widely studied as model systems for bacterial adhesion, biofilm development, and biofilm resistance to antibiotics, due to their widespread presence and accessibility. Despite descriptions of initial plaque formation on the tooth surface, studies on mature plaque and plaque structure below the gum are limited to landmark studies from the 1970s, without appreciating the breadth of microbial diversity in the plaque. We used fluorescent in situ hybridization to localize in vivo the most abundant species from different phyla and species associated with periodontitis on seven embedded teeth obtained from four different subjects. The data showed convincingly the dominance of Actinomyces sp., Tannerella forsythia, Fusobacterium nucleatum, Spirochaetes, and Synergistetes in subgingival plaque. The latter proved to be new with a possibly important role in host-pathogen interaction due to its localization in close proximity to immune cells. The present study identified for the first time in vivo that Lactobacillus sp. are the central cells of bacterial aggregates in subgingival plaque, and that Streptococcus sp. and the yeast Candida albicans form corncob structures in supragingival plaque. Finally, periodontal pathogens colonize already formed biofilms and form microcolonies therein. These in vivo observations on oral biofilms provide a clear vision on biofilm architecture and the spatial distribution of predominant species

Standardisation of magnetic nanoparticles in liquid suspension

Suspensions of magnetic nanoparticles offer diverse opportunities for technology innovation, spanning a large number of industry sectors from imaging and actuation based applications in biomedicine and biotechnology, through large-scale environmental remediation uses such as water purification, to engineering-based applications such as position-controlled lubricants and soaps. Continuous advances in their manufacture have produced an ever-growing range of products, each with their own unique properties. At the same time, the characterisation of magnetic nanoparticles is often complex, and expert knowledge is needed to correctly interpret the measurement data. In many cases, the stringent requirements of the end-user technologies dictate that magnetic nanoparticle products should be clearly defined, well characterised, consistent and safe; or to put it another way—standardised. The aims of this document are to outline the concepts and terminology necessary for discussion of magnetic nanoparticles, to examine the current state-of-the-art in characterisation methods necessary for the most prominent applications of magnetic nanoparticle suspensions, to suggest a possible structure for the future development of standardisation within the field, and to identify areas and topics which deserve to be the focus of future work items. We discuss potential roadmaps for the future standardisation of this developing industry, and the likely challenges to be encountered along the way

Machine learning for multiparametric ultrasound classification of prostate cancer using B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

The diagnosis of prostate cancer (PCa) is still based on systematic biopsy, but is increasingly developing towards an imaging-driven approach. In particular, multiparametric magnetic resonance imaging (MRI) is receiving increasing attention over the last few years. In light of MRI-related issues concerning costs, practicality, and availability, we investigate a multiparametric ultrasound (mpUS) approach. We propose and test a machine-learning-based strategy that automatically combines B-mode ultrasound, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) features. To this end, automatic zonal segmentation by deep learning, model-based feature estimation (related to contrast-agent perfusion and dispersion), radiomic feature extraction, and a random-forest-based pixel-wise classification were combined. The method was trained and validated against histopathologically-confirmed benign and malignant regions of interest in 48 PCa patients, in a leave-one-patient-out cross-correlation fashion. The mpUS classification algorithm yielded a region-wise area under the Receiver Operating Characteristics (ROC) curve of 0.75 and 0.90 for PCa and significant (i.e., Gleason ≥4+3) PCa, respectively. In comparison, the best-performing single parameter (i.e., DCE-US-based contrast velocity) reached a performance of 0.69 and 0.76 in terms of the ROC curve area. In particular the combination of perfusion-, dispersion-, and elasticity-related features were favored in the classification. Even though validation on a larger patient group is required, we have demonstrated the technical feasibility of automatic mpUS for PCa localization. Further development of mpUS might lead to a valuable clinical option for robust, ultrasound-driven PCa diagnosis

Synthetic elastography from B-mode ultrasound through deep learning

Tissue elasticity can be locally estimated using shear-wave elastography (SWE), an advanced technique that measures the speed of laterally-traveling shear waves induced by a sequence of acoustic radiation force "push" pulses. However, SWE is not available on all ultrasound machines due to e.g. power, equipment, and procedural requirements; in particular, wireless devices would face challenges delivering the required power. Here, we propose a fully-convolutional deep neural network for the synthesis of an SWE image given the corresponding B-mode (side-by-side-view) image. Fifty patients diagnosed with prostate cancer underwent a transrectal SWE examination with SWE imaging regions chosen such that they covered the entire or parts of the prostate. The network was trained with the images of 40 patients and subsequently tested using 30 image planes from the remaining 10 patients. The neural network was able to accurately map the B-mode images to sSWE images with a pixel-wise mean absolute error of 4.8 kPa in terms of Young's modulus. Qualitatively, tumour sites characterized by high stiffness were mostly preserved (as validated by histopathology). Despite the need for further validation, our results already suggest that deep learning is a viable way to retrieve elasticity values from conventional B-mode images and can potentially provide valuable information for cancer diagnosis using devices on which no SWE imaging is available

Machine Learning for Multiparametric Ultrasound Classification of Prostate Cancer using B-mode, Shear-Wave Elastography, and Contrast-Enhanced Ultrasound Radiomics

The diagnosis of prostate cancer (PCa) is still based on systematic biopsy, but is increasingly developing towards an imaging-driven approach. In particular, multiparametric magnetic resonance imaging (MRI) is receiving increasing attention over the last few years. In light of MRI-related issues concerning costs, practicality, and availability, we investigate a multiparametric ultrasound (mpUS) approach. We propose and test a machine-learning-based strategy that automatically combines B-mode ultrasound, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) features. To this end, automatic zonal segmentation by deep learning, model-based feature estimation (related to contrast-agent perfusion and dispersion), radiomic feature extraction, and a random-forest-based pixel-wise classification were combined. The method was trained and validated against histopathologically-confirmed benign and malignant regions of interest in 48 PCa patients, in a leave-one-patient-out cross-correlation fashion. The mpUS classification algorithm yielded a region-wise area under the Receiver Operating Characteristics (ROC) curve of 0.75 and 0.90 for PCa and significant (i.e., Gleason ≥4+3) PCa, respectively. In comparison, the best-performing single parameter (i.e., DCE-US-based contrast velocity) reached a performance of 0.69 and 0.76 in terms of the ROC curve area. In particular the combination of perfusion-, dispersion-, and elasticity-related features were favored in the classification. Even though validation on a larger patient group is required, we have demonstrated the technical feasibility of automatic mpUS for PCa localization. Further development of mpUS might lead to a valuable clinical option for robust, ultrasound-driven PCa diagnosis