Repository of MRI-derived models of the breast with single and multiple benign and malignant tumors for microwave imaging research.

The diagnosis of breast cancer through MicroWave Imaging (MWI) technology has been extensively researched over the past few decades. However, continuous improvements to systems are needed to achieve clinical viability. To this end, the numerical models employed in simulation studies need to be diversified, anatomically accurate, and also representative of the cases in clinical settings. Hence, we have created the first open-access repository of 3D anatomically accurate numerical models of the breast, derived from 3.0T Magnetic Resonance Images (MRI) of benign breast disease and breast cancer patients. The models include normal breast tissues (fat, fibroglandular, skin, and muscle tissues), and benign and cancerous breast tumors. The repository contains easily reconfigurable models which can be tumor-free or contain single or multiple tumors, allowing complex and realistic test scenarios needed for feasibility and performance assessment of MWI devices prior to experimental and clinical testing. It also includes an executable file which enables researchers to generate models incorporating the dielectric properties of breast tissues at a chosen frequency ranging from 3 to 10 GHz, thereby ensuring compatibility with a wide spectrum of research requirements and stages of development for any breast MWI prototype system. Currently, our dataset comprises MRI scans of 55 patients, but new exams will be continuously added.

A non-invasive approach to skin cancer diagnosis via graphene electrical tattoos and electrical impedance tomography.

Objective.Making up one of the largest shares of diagnosed cancers worldwide, skin cancer is also one of the most treatable. However, this is contingent upon early diagnosis and correct skin cancer-type differentiation. Currently, methods for early detection that are accurate, rapid, and non-invasive are limited. However, literature demonstrating the impedance differences between benign and malignant skin cancers, as well as between different types of skin cancer, show that methods based on impedance differentiation may be promising.Approach.In this work, we propose a novel approach to rapid and non-invasive skin cancer diagnosis that leverages the technologies of difference-based electrical impedance tomography (EIT) and graphene electronic tattoos (GETs).Main results.We demonstrate the feasibility of this first-of-its-kind system using both computational numerical and experimental skin phantom models. We considered variations in skin cancer lesion impedance, size, shape, and position relative to the electrodes and evaluated the impact of using individual and multi-electrode GET (mGET) arrays. The results demonstrate that this approach has the potential to differentiate based on lesion impedance, size, and position, but additional techniques are needed to determine shape.Significance.In this way, the system proposed in this work, which combines both EIT and GET technology, exhibits potential as an entirely non-invasive and rapid approach to skin cancer diagnosis.

Proof-of-Concept of Microwave-Based Bladder State Detection Using Realistic Pelvic Models.

Goal: Urinary incontinence (UI) affects a significant proportion of the population and is associated with negative physical and psychological side-effects. Microwave-based technologies may have the potential to monitor bladder volume, providing a proactive, low-cost and non-invasive tool to support individuals with UI. Methods: Studies to date on microwave bladder monitoring have been limited to highly simplified computational and experimental scenarios. In this work, we study the most realistic models to date (both male and female), which incorporate dielectrically and anatomically representative tissues of the pelvic region. Results: We examine the ability of detecting bladder fullness through both reflection and transmission-based parameters and, for the first time, study the effect of urine permittivity. As a proof-of-concept of bladder state detection, we further investigate reconstructing differential radar images of the bladder with two different volumes of urine. Conclusions: The results indicate that there is strong potential for monitoring and detecting the bladder state using microwave measurements.

Analysis of electrode arrangements for brain stroke diagnosis via electrical impedance tomography through numerical computational models.

Objective.Rapid stroke-type classification is crucial for improved prognosis. However, current methods for classification are time-consuming, require expensive equipment, and can only be used in the hospital. One method that has demonstrated promise in a rapid, low-cost, non-invasive approach to stroke diagnosis is electrical impedance tomography (EIT). While EIT for stroke diagnosis has been the topic of several studies in recent years, to date, the impact of electrode placements and arrangements has rarely been analyzed or tested and only in limited scenarios. Optimizing the location and choice of electrodes can have the potential to improve performance and reduce hardware cost and complexity and, most importantly, diagnosis time.Approach.In this study, we analyzed the impact of electrodes in realistic numerical models by (1) investigating the effect of individual electrodes on the resulting simulated EIT boundary measurements and (2) testing the performance of different electrode arrangements using a machine learning classification model.Main results.We found that, as expected, the electrodes deemed most significant in detecting stroke depend on the location of the electrode relative to the stroke lesion, as well as the role of the electrode. Despite this dependence, there are notable electrodes used in the models that are consistently considered to be the most significant across the various stroke lesion locations and various head models. Moreover, we demonstrate that a reduction in the number of electrodes used for the EIT measurements is possible, given that the electrodes are approximately evenly distributed.Significance.In this way, electrode arrangement and location are important variables to consider when improving stroke diagnosis methods using EIT.

Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models.

Electrical impedance tomography (EIT) may have potential to overcome existing limitations in stroke differentiation, enabling low-cost, rapid, and mobile data collection. Combining bioimpedance measurement technologies such as EIT with machine learning classifiers to support decision-making can avoid commonly faced reconstruction challenges due to the nonlinear and ill-posed nature of EIT imaging. Therefore, in this work, we advance this field through a study integrating realistic head models with clinically relevant test scenarios, and a robust architecture consisting of nested cross-validation and principal component analysis. Specifically, realistic head models are designed which incorporate the highly conductive layers of cerebrospinal fluid in the subarachnoid space and ventricles. In total, 135 unique models are created to represent a large patient population, with normal, haemorrhagic, and ischemic brains. Simulated EIT voltage data generated from these models are used to assess the classification performance of support vector machines. Parameters explored include driving frequency, signal-to-noise ratio, kernel function, and composition of binary classes. Classifier accuracy at 60 dB signal-to-noise ratio, reported as mean and standard deviation, are (79.92% ± 10.82%) for lesion differentiation, (74.78% ± 3.79%) for lesion detection, (77.49% ± 15.90%) for bleed detection, and (60.31% ± 3.98%) for ischemia detection (after ruling out bleed). The results for each method were obtained with statistics from 3 independent runs with 17,280 observations, polynomial kernel functions, and feature reduction of 76% by PCA (from 208 to 50 features). While results of this study show promise for stroke differentiation using EIT data, our findings indicate that the achievable accuracy is highly dependent on the classification scenario and application-specific classifiers may be necessary to achieve acceptable accuracy.

3D printable phantom for mimicking electrical properties of dermal tissue.

Skin cancer is one of the most ubiquitous forms of cancer that is often overdiagnosed or missed by traditional diagnostic techniques. Bioimpedance spectroscopy (BIS) is a technology that aims to take advantage of the variations in electrical properties of tissue to identify ectopic formations. It is difficult to develop BIS technologies without obtaining tumor tissue samples. One solution is to use a "tissue phantom," a synthetic structure that mimics the properties of tissue. Current solutions using natural biomaterials, such as gelatin, have not been able to create complex tissue geometries that are vital to honing BIS diagnostics. However, semi-synthetic polymers, such has gelatin methacrylate (GelMA), offer the benefits of possessing similar electrical properties to their respective source biomaterial while being 3D printable. In this work, we first measured the impedance of porcine dermal tissue. We then applied these impedance measurements to create an electrically accurate tissue phantom using a photocurable hydrogel, GelMA, and varying concentrations of NaCl, aluminum powder, and titanium dioxide powder.

Comparative analysis of fish environmental DNA reveals higher sensitivity achieved through targeted sequence-based metabarcoding.

Environmental DNA (eDNA)-based methods of species detection are enabling various applications in ecology and conservation including large-scale biomonitoring efforts. qPCR is widely used as the standard approach for species-specific detection, often targeting a fish species of interest from aquatic eDNA. However, DNA metabarcoding has the potential to displace qPCR in certain eDNA applications. In this study, we compare the sensitivity of the latest Illumina NovaSeq 6000 NGS platform to qPCR TaqMan assays by measuring limits of detection and by analysing eDNA from water samples collected from Churchill River and Lake Melville, NL, Canada. Species-specific, targeted next generation sequencing (NGS) assays had significantly higher sensitivity than qPCR, with limits of detection 14- to 29-fold lower. For example, when analysing eDNA, qPCR detected Gadus ogac (Greenland cod) in 21% of samples, but targeted NGS detected this species in 29% of samples. General NGS assays were as sensitive as qPCR, while simultaneously detecting 15 fish species from eDNA samples. With over 34,000 fish species on the planet, parallel and sensitive methods such as NGS will be required to support effective biomonitoring at both regional and global scales.

Examination of Information and Misinformation about Urinary Tract Infections on TikTok and YouTube.

OBJECTIVE: To describe and assess the quality and accuracy of the most highly viewed YouTube and TikTok posts related to urinary tract infections (UTIs). Social media is increasingly a resource for health information. YouTube and TikTok videos are highly utilized and are potentially a source of helpful information or misinformation. METHODS: During January 2021, "UTI" was searched within YouTube and TikTok and the most relevant videos were identified and analyzed for their content. Accuracy of scientific information, possible misinformation, and credibility of the videos was rated independently by three reviewers. Posts were categorized as educational/informational, shared experience, humor/entertainment, and home remedies/alternative therapies. RESULTS AND CONCLUSIONS: In 50 YouTube and 50 TikTok videos respectively, the median number of views was 49K and 1.4M, the median number of likes was 296 and 58K, and the median number of comments was 50 and 616. The proportion of female to male presenters was equal for YouTube videos while 94% of those from TikTok were female. Overall, YouTube videos had higher median scores for scientific information, credibility, and less misinformation compared to TikTok. YouTube videos with more views, likes, and comments tended to have lower scores in all categories and more misinformation. More presenters were medical professionals on YouTube videos than those on TikTok. While videos from both platforms contained misinformation, none promoted misinformation that would cause harm to health. Healthcare providers should be aware of the potential influence of social media as patients are getting health information from many sources.

Measurement and image-based estimation of dielectric properties of biological tissues -past, present, and future.

The dielectric properties of biological tissues are fundamental pararmeters that are essential for electromagnetic modeling of the human body. The primary database of dielectric properties compiled in 1996 on the basis of dielectric measurements at frequencies from 10 Hz to 20 GHz has attracted considerable attention in the research field of human protection from non-ionizing radiation. This review summarizes findings on the dielectric properties of biological tissues at frequencies up to 1 THz since the database was developed. Although the 1996 database covered general (normal) tissues, this review also covers malignant tissues that are of interest in the research field of medical applications. An intercomparison of dielectric properties based on reported data is presented for several tissue types. Dielectric properties derived from image-based estimation techniques developed as a result of recent advances in dielectric measurement are also included. Finally, research essential for future advances in human body modeling is discussed.

Bioaccumulation and effects of selenium from surface coal mining in an aquatic songbird.

Open-pit mining operations are hailed for safe working conditions for miners as well as economically and logistically favourable outcomes for mining companies. However, ecological impacts of these operations may persist for decades. Expansions of open-pit coal mining in British Columbia of Western Canada are planned. Governmental regulation of background contaminants leached from these mines into nearby water systems were established to mitigate environmental impacts associated with these operations. We analyzed water, periphyton, invertebrate, and American dipper (Cinclus mexicanus) blood, egg, and feather samples for selenium and mercury exposure related to mining operations in the Elk Valley. We also quantified effects associated with exposure on clutch size and hatching success. Selenium concentrations in water, periphyton, and invertebrates were significantly higher downstream of mines compared to reference sites within and outside of the Elk River watershed. Selenium concentrations in water from exposed sites exceeded current regulatory levels established to protect wildlife by up to 20 times. Mercury concentrations were below toxic levels for birds in all media and did not factor into determination of selenium exposure or effects. Egg selenium concentrations were on average 0.9 times the regulatory threshold. Our stable isotope analyses showed that diets of nesting females included a negligible proportion of higher order consumers. We did not detect a significant effect of selenium on the rate of hatching success or clutch size of dippers nesting in exposed sites. We conclude that at the time of the investigation, selenium exposure was not impairing hatching of American dippers at the sampled locations.

Impact of Local Electrodes on Brain Stroke Type Differentiation using Electrical Impedance Tomography.

Electrical impedance tomography (EIT) of the head has the potential to provide rapid characterization of brain stroke. This study builds on previous work by implementing a more anatomically complex head model, contrasting results of bleed and clot simulations, and by establishing the electrodes which dominate in voltage difference measurements. This work provides the basis for machine learning with clusters of small numbers of electrodes as unique features for stroke-type detection and differentiation.Clinical Relevance- This application of EIT can aid in early detection, classification, and localization of brain stroke, allowing for faster treatment.

Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis.

Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing. We propose a pre-processing pipeline, which includes image registration, bias field correction, data normalisation, background subtraction, and median filtering. We segmented the fat tissue with the region growing algorithm in fat-weighted Dixon images. Skin, fibroglandular tissue, and the chest wall boundary were segmented from water-weighted Dixon images. Then, we applied a 3D region growing and Hoshen-Kopelman algorithms for tumour segmentation. The developed semi-automatic segmentation procedure is suitable to segment tissues with a varying level of heterogeneity regarding voxel intensity. Two accurate breast models with benign and malignant tumours, with dielectric properties at 3, 6, and 9 GHz frequencies have been made available to the research community. These are suitable for microwave diagnosis, i.e., imaging and classification, and can be easily adapted to other imaging modalities.

Dielectric Properties of Ovine Heart at Microwave Frequencies.

Accurate knowledge of the dielectric properties of biological tissues is important in dosimetry studies and for medical diagnostic, monitoring and therapeutic technologies. In particular, the dielectric properties of the heart are used in numerical simulations of radiofrequency and microwave heart ablation. In one recent study, it was demonstrated that the dielectric properties of different components of the heart can vary considerably, contrary to previous literature that treated the heart as a homogeneous organ with measurements that ignored the anatomical location. Therefore, in this study, we record and report the dielectric properties of the heart as a heterogeneous organ. We measured the dielectric properties at different locations inside and outside of the heart over the 500 MHz to 20 GHz frequency range. Different parts of the heart were identified based on the anatomy of the heart and their function; they include the epicardium, endocardium, myocardium, exterior and interior surfaces of atrial appendage, and the luminal surface of the great vessels. The measured dielectric properties for each part of the heart are reported at both a single frequency (2.4 GHz), which is of interest in microwave medical applications, and as parameters of a broadband Debye model. The results show that in terms of dielectric properties, different parts of the heart should not be considered the same, with more than 25% difference in dielectric properties between some parts. The specific Debye models and single frequency dielectric properties from this study can be used to develop more detailed models of the heart to be used in electromagnetic modeling.

Exposure to persistent organic pollutants is linked to over-wintering latitude in a Pacific seabird, the rhinoceros auklet, Cerorhinca monocerata.

Seabirds are wide-ranging organisms often used to track marine pollution, yet the effect of migration on exposure over the annual cycle is often unclear. We used solar geolocation loggers and stable isotope analysis to study the effects of post breeding dispersal and diet on persistent organic pollutant (POP) and mercury (Hg) burdens in rhinoceros auklets, Cerorhinca monocerata, breeding on islands along the Pacific Coast of Canada. Hg and four classes of POPs were measured in auklet eggs: organochlorine insecticides (OCs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and perfluoralkyl substances (PFASs). Stable isotope values of adult breast feathers grown during winter were used in conjunction with geolocation to elucidate adult wintering latitude. Wintering latitude was the most consistent and significant predictor of some POP and of Hg concentrations in eggs. The magnitude and pattern of exposure varied by contaminant, with ∑PCBs, ∑PBDEs and DDE decreasing with wintering latitude, and mirex, perfluoro-n-tridecanoic acid, and Hg increasing with latitude. We suggest that concentrations of these contaminants in rhinoceros auklet eggs are influenced by variation in uptake at adult wintering locations related to anthropogenic inputs and oceanic and atmospheric transport.

Development of an Anthropomorphic Phantom of the Axillary Region for Microwave Imaging Assessment.

We produced an anatomically and dielectrically realistic phantom of the axillary region to enable the experimental assessment of Axillary Lymph Node (ALN) imaging using microwave imaging technology. We segmented a thoracic Computed Tomography (CT) scan and created a computer-aided designed file containing the anatomical configuration of the axillary region. The phantom comprises five 3D-printed parts representing the main tissues of interest of the axillary region for the purpose of microwave imaging: fat, muscle, bone, ALNs, and lung. The phantom allows the experimental assessment of multiple anatomical configurations, by including ALNs of different size, shape, and number in several locations. Except for the bone mimicking organ, which is made of solid conductive polymer, we 3D-printed cavities to represent the fat, muscle, ALN, and lung and filled them with appropriate tissue-mimicking liquids. Existing studies about complex permittivity of ALNs have reported limitations. To address these, we measured the complex permittivity of both human and animal lymph nodes using the standard open-ended coaxial-probe technique, over the 0.5 GHz-8.5 GHz frequency band, thus extending current knowledge on dielectric properties of ALNs. Lastly, we numerically evaluated the effect of the polymer which constitutes the cavities of the phantom and compared it to the realistic axillary region. The results showed a maximum difference of 7 dB at 4 GHz in the electric field magnitude coupled to the tissues and a maximum of 10 dB difference in the ALN response. Our results showed that the phantom is a good representation of the axillary region and a viable tool for pre-clinical assessment of microwave imaging technology.

Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis.

OBJECTIVE: Multi-frequency symmetry difference electrical impedance tomography (MFSD-EIT) can robustly detect and identify unilateral perturbations in symmetric scenes. Here, an investigation is performed to assess if the algorithm can be successfully applied to identify the aetiology of stroke with the aid of machine learning. METHODS: Anatomically realistic four-layer finite element method models of the head based on stroke patient images are developed and used to generate EIT data over a 5 Hz-100 Hz frequency range with and without bleed and clot lesions present. Reconstruction generates conductivity maps of each head at each frequency. Application of a quantitative metric assessing changes in symmetry across the sagittal plane of the reconstructed image and over the frequency range allows lesion detection and identification. The algorithm is applied to both simulated and human (n = 34 subjects) data. A classification algorithm is applied to the metric value in order to differentiate between normal, haemorrhage and clot values. MAIN RESULTS: An average accuracy of 85% is achieved when MFSD-EIT with support vector machines (SVM) classification is used to identify and differentiate bleed from clot in human data, with 77% accuracy when differentiating normal from stroke in human data. CONCLUSION: Applying a classification algorithm to metrics derived from MFSD-EIT images is a novel and promising technique for detection and identification of perturbations in static scenes. SIGNIFICANCE: The MFSD-EIT algorithm used with machine learning gives promising results of lesion detection and identification in challenging conditions like stroke. The results imply feasible translation to human patients.

Challenges of Post-measurement Histology for the Dielectric Characterisation of Heterogeneous Biological Tissues.

The dielectric properties of biological tissues are typically measured using the open-ended coaxial probe technique, which is based on the assumption that the tissue sample is homogeneous. Therefore, for heterogeneous tissue samples, additional post-measurement sample processing is conducted. Specifically, post-measurement histological analysis may be performed in order to associate the measured dielectric properties with the tissue types present in a heterogeneous sample. Accurate post-measurement histological analysis enables identification of the constituent tissue types that contributed to the measured dielectric properties, and their relative distributions. There is no standard protocol for conducting post-measurement histological analysis, which leads to high numbers of excluded tissue samples and inconsistencies in the resulting reported data for heterogeneous tissues. To this extent, this study examines the post-measurement histological process and the challenges in associating the acquired dielectric properties with the different tissue types present in heterogeneous samples. The results demonstrate that the histological process inevitably alters the morphology of samples, thus introducing errors in the interpretation of the dielectric properties acquired from heterogeneous biological samples. Notably, sample size was seen to shrink by up to 90% through the histological process, meaning that sensing volume determined from fresh tissues is not directly applicable to histology images.

Magnitude and Kinetics of T Cell and Antibody Responses During H1N1pdm09 Infection in Inbred Babraham Pigs and Outbred Pigs.

We have used the pig, a large natural host animal for influenza with many physiological similarities to humans, to characterize αß, γδ T cell and antibody (Ab) immune responses to the 2009 pandemic H1N1 virus infection. We evaluated the kinetic of virus infection and associated response in inbred Babraham pigs with identical MHC (Swine Leucocyte Antigen) and compared them to commercial outbred animals. High level of nasal virus shedding continued up to days 4 to 5 post infection followed by a steep decline and clearance of virus by day 9. Adaptive T cell and Ab responses were detectable from days 5 to 6 post infection reaching a peak at 9 to 14 days. γδ T cells produced cytokines ex vivo at day 2 post infection, while virus reactive IFNγ producing γδ T cells were detected from day 7 post infection. Analysis of NP tetramer specific and virus specific CD8 and CD4 T cells in blood, lung, lung draining lymph nodes, and broncho-alveolar lavage (BAL) showed clear differences in cytokine production between these tissues. BAL contained the most highly activated CD8, CD4, and γδ T cells producing large amounts of cytokines, which likely contribute to elimination of virus. The weak response in blood did not reflect the powerful local lung immune responses. The immune response in the Babraham pig following H1N1pdm09 influenza infection was comparable to that of outbred animals. The ability to utilize these two swine models together will provide unparalleled power to analyze immune responses to influenza.

Bi-Frequency Symmetry Difference EIT-Feasibility and Limitations of Application to Stroke Diagnosis.

OBJECTIVE: Bi-Frequency Symmetry Difference (BFSD)-EIT can detect, localize and identify unilateral perturbations in symmetric scenes. Here, we test the viability and robustness of BFSD-EIT in stroke diagnosis. METHODS: A realistic 4-layer Finite Element Method (FEM) head model with and without bleed and clot lesions is developed. Performance is assessed with test parameters including: measurement noise, electrode placement errors, contact impedance errors, deviations in assumed tissue conductivity, deviations in assumed anatomy, and a frequency-dependent background. A final test is performed using ischemic patient data. Results are assessed using images and quantitative metrics. RESULTS: BFSD-EIT may be feasible for stroke diagnosis if a signal-to-noise ratio (SNR) of ≥60 dB is achievable. Sensitivity to errors in electrode positioning is seen with a tolerance of only ±5 mm, but a tolerance of up to ±30 mm is possible if symmetry is maintained between symmetrically opposite partner electrodes. The technique is robust to errors in contact impedance and assumed tissue conductivity up to at least ±50%. Asymmetric internal anatomy affects performance but may be tolerable for tissues with frequency-dependent conductivity. Errors in assumed external geometry marginally affect performance. A frequency-dependent background does not affect performance with carefully chosen frequency points or use of multiple frequency points across a band. The Global Left-Hand Side (LHS) & Right-Hand Side (RHS) Mean Intensity metric is particularly robust to errors. CONCLUSION: BFSD-EIT is a promising technique for stroke diagnosis, provided parameters are within the tolerated ranges. SIGNIFICANCE: BFSD-EIT may prove an important step forward in imaging of static scenes such as stroke.